text:
Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network
Xiaonan Tan1,
Geng Chen ORCID: orcid.org/0000-0001-9432-05631 &
Hongyu Sun1,2
A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.
In this paper, in order to solve the problem of users switching networks under the condition of heterogeneous integrated network, we propose a vertical handover algorithm of heterogeneous integrated network based on neural network framework. We introduce the BP neural network to participate in the construction and execution of this algorithm, and introduce the 5G network in the environment where UMTS, GPRS, WLAN, and 4G networks coexist to improve the scope of this algorithm. The type of three-layer BP neural network uses user moving speed, maximum transmission rate, minimum transmission delay, signal-to-interference plus noise ratio, bit error rate, and packet loss rate as inputs of the neural network in a heterogeneous network environment. The six network input factors are set the middle layer of the neural network according to the relevant empirical formula, we use the network download rate as the key factor to determine the performance of the current wireless network, and then set it as the output of the neural network to participate in the vertical handover decision. Compared with other intelligent algorithms represented by fuzzy logic algorithms, the vertical handover algorithm improves the success rate of network handover and makes the seamless handover between heterogeneous networks more efficient.
In recent years, with the rapid development of wireless network technology and the rapid advancement of network access technology of the heterogeneous convergence architecture, many different types of wireless networks exist simultaneously [1, 2]. Different networks compete and complement with each other, resulting in the coexistence of multiple types of network [3, 4]. In order to adapt to the different requirements of users and the development of various types of business, wireless networks with different architectures form a multiple heterogeneous integrated network [5,6,7], in which the most representative is the integration of two kinds of heterogeneous networks: WLAN and cellular [8,9,10,11]. WLAN networks have relatively wide bandwidth and small coverage, while cellular series networks (such as current 3G/4G wireless networks) have relatively narrow bandwidth and wide coverage [12,13,14], so the integration of cellular series network and WLAN network can complement each other [15, 16], giving full play to their respective strengths and restraining their limitations at the same time [17,18,19], in addition, with the birth of 5G technology, the integration between multiple networks with different architectures will make a revolutionary progress [20,21,22,23,24,25]. Therefore, the vertical handover algorithm should be considered with a variety of disturbing factors and a variety of candidate network types [26], and it is more suitable to be combined with intelligent algorithms [27]. The relevant neural network algorithms in the field of machine learning are now used in the design of this paper's algorithm [28,29,30,31,32].
There are many vertical handover algorithms, and each has its own advantages and disadvantages. Reference [1] proposed a vertical handover algorithm based on visible light communication system for decision selection. By utilizing analytic hierarchy process and a two-person cooperative game model to consider multiple attributes, including dynamic network parameters and actual traffic preferences. Although handover was reduced. Overhead, it considered fewer handover factors. Reference [3] proposed a vertical handover decision algorithm for cooperation of multi-terminal based on fuzzy logic and analytic hierarchy process. Although it could fully reflect the application requirements and user preferences, it lacked consideration of the upcoming 5G network. Similar algorithms were mentioned in the reference [7, 17]. Reference [4] reviewed and classified the most significant multi-criteria decision-making algorithm for network selection in next-generation wireless networks, and illustrated an insight into the importance of network-criteria weighting and the current research trended in the application of multi-criteria decision-making algorithms to network-selection problems in heterogeneous wireless networks. Reference [6] proposed to maximize network utilization as more than one target network existed during vertical handover. Also, they showed how network parameters could be embedded into IEEE 802.21-based signaling procedures to provide seamless connectivity during a handover. However, the algorithm lacked consideration of the important factor of network transmission rate. The vertical handover algorithm proposed in reference [8] mainly used "New Composite Rule Inference based logarithm" to improve the service quality between WLAN_UMTS (Wireless Local Area Network and Universal Mobile Telephone System) networks. Although it had a high handover success rate, it also did not integrate the 4G and 5G network. Reference [9] was similar to reference [3], reference [7], and reference [8]. It employed fuzzy logic theory and multi-attributes decision making in selecting the best network to optimize the vertical handover algorithm, but did not involve the network attribute of SINR. Reference [11] proposed an adaptive vertical handover algorithm based on artificial neural network similar to the algorithm designed in this paper, but it only chose the LTE and WLAN network as candidate network. Reference [12] presented a mixed integer linear programming (MILP) model to balance multi-homing loads in heterogeneous wireless networks based on multi-objective tabu search method. Both the reference [13] and the reference [15] applied the genetic algorithm to the relevant handover decision, which had a high success rate of handover, but also lacked consideration of 5G network factors. Reference [16] proposed a heterogeneous network selection algorithm based on the combination of network attributes and user preferences, similar to the multi-attribute-based vertical handover algorithm. Reference [20] designed an intelligent algorithm for Seamless Vertical Handover in vehicular networks, which were essentially based on a hybrid model merged the biogeography-based optimization or BBO with the Markov chain, and applied to the vertical handover between different access technologies such as WiMAX, WiFi, and UMTS. However, this handover process was more complicated. Reference [18], reference [22], and reference [24] emphasized the application of 5G network technology, but failed to integrate perfectly with vertical handover algorithms in heterogeneous networks. Reference [26] considered factors related to inter-cell interference in communication activities, but the related theory was more complicated. Reference [27] introduced fuzzy logic algorithm into wireless positioning, and failed to combine related network environment factors. The literature [28,29,30,31,32] generally used the relevant neural network algorithms in the field of machine learning, but the theories mentioned had been improved to a certain extent. Although the efficiency was improved, the operation process of the algorithm was also more complicated .
In summary, the environmental factors that can affect the performance of wireless networks considered by most of the current vertical handover algorithms in the engineering field are not comprehensive. With the 5G network about to popularize, most existing vertical handover algorithms affect the performance of 5G networks. But the related factors of 5G networks are not included, which causes most of the existing vertical handover algorithms will not be able to switch to the 5G network when selecting the network in the area covered by the 5G network.
Therefore, to solve the drawback of existing vertical handover algorithm including the high complexity of the running process and exclude 5G network system, a novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR ), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of "BP neural network input layer neurons" and "the training and learning process of subsequent neural network data." Finally, to perform vertical handover decision, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and it will help the vertical handover algorithm designed in this paper select the right wireless network.
This vertical handover algorithm is applied to the network's selection with different architectures. Therefore, when performing relay selection between networks with different architectures, the six network characteristics set at the input of the neural network model corresponding to the above five networks are used to select to implement a decision strategy, that is, to predict the final download rate of the five networks based on the real six network attributes that have been input into the neural network model, so as to choose the existing five networks to be robust and optimal in the current environment. The network with the highest download rate to be selected as the final switch and gives users a good experience.
Compared with most of the existing vertical handover algorithms, the novelty of the vertical handover algorithm designed in this paper is to include the 5G network in the switching range of the heterogeneous integrated networks, the use of the excellent learning ability, and the independently designable framework integrates most of the key factors that affect the performance of the wireless network into this vertical handover algorithm. Therefore, the environmental factors related to the performance of the wireless network considered by the algorithm in this paper are more comprehensive.
This paper is organized as follows. First, the proposed methods are presented in Section 2. Simulation and experimental results are given in Section 3. Finally, the conclusion is presented in Section 4.
Proposed method
Neural network model
The vertical handover algorithm proposed in this paper is designed for UMTS, GPRS, WLAN, 4G and 5G networks. Each network build their own three-layer BP neural network models. The neuron weights of each layer are determined according to the theoretical values of the maximum transmission rate, minimum delay, SINR, bit error rate, user's moving speed, packet loss rate, and network download rate. When the user enters into the network environment where the five heterogeneous networks are coexisted, the terminal collects the actual maximum transmission rate, minimum delay, SINR, bit error rate, user moving speed, and packet loss rate according to the algorithm and inputs the values to the five neural network models that have been trained respectively. The predicted values of the download rates of network corresponding to each of the five types of networks are obtained, and finally the wireless network with highest network download rate prediction value is selected as the pre-switched network.
Since the vertical handover algorithm designed in this paper is based on the BP neural network model, Fig. 1. shows the generalized three-layer BP neural network model.
Framework diagram of three-layer BP neural network model
Where m, k, and n represent the neurons in the input layer, hidden layer, and output layer, respectively. BP neural network model is constructed by using Log-sigmoid transfer function as Eq. (1).
$$ f(x)=\frac{1}{1+{e}^{-x}} $$
At the same time, the error back propagation algorithm and the error function (2) are employed in this work, so that the weights value of the neural network are constantly regulated by the error feedback, and the offset value of the error function is more closer to the expected one.
$$ E=\frac{\sum \limits_i{\left({t}_i+{O}_i\right)}^2}{2} $$
Where ti are the expected values of the network parameter results and oi denote the related output values calculated by the neural network, respectively. And the corresponding neural network model needs to be solved for the operation of the algorithm.
Therefore, the operating principle of the BP neural network model shown in Fig. 1 is given now, the network weights are now set to (Wij,Tij), the neuron threshold is set to (a), Xj is the jth input layer node, Yj is the jth hidden layer node, Oj is the jth output layer node, and E is the error function, then the relevant calculation formulas of the above three-layer BP neural network model are as follows, where the neurons are set the input vector is X = (x1,x2,x3,......,xm), then the weights value corresponding to the input vector in the input neuron is W = (w1,w2,w3,......,wm), the output of the neural network is expressed by Eq. (3):
$$ y=f\left(\sum \limits_{i=1}^{\mathrm{m}}{w}_i{x}_i-a\right) $$
where f is the activation function and its expression is Eq. (4):
$$ f(x)=\left\{\begin{array}{c}1,\\ {}-1,\end{array}\right.{\displaystyle \begin{array}{c}x\ge 0\\ {}x<0\end{array}} $$
The output of the hidden layer node is Eq. (5):
$$ {y}_i=f\left(\sum \limits_j{w}_{ij}{x}_j-{a}_i\right)=f\left({net}_i\right) $$
The output of the output layer node is Eq. (6):
$$ {o}_l=f\left(\sum \limits_j{T}_{ij}{x}_j-{a}_l\right)=f\left({net}_l\right) $$
Then, the error of the output layer node is Eq. (7):
$$ E=\frac{1}{2}\sum \limits_l\left({t}_l-{o}_l\right) $$
Therefore, minimizing the error E is the ultimate goal of the BP neural network for training and learning, so the weight adjustment of the BP neural network and the negative gradient of the error E should be in a proportional relationship. It is expressed as Eq. (8).
$$ \frac{\partial E}{\partial {T}_{li}}=\sum \limits_{k=1}^m\frac{\partial E}{\partial {o}_k}\frac{\partial {o}_k}{\partial {T}_{li}}=\frac{\partial E}{\partial {o}_l}\frac{\partial {o}_l}{\partial {T}_{li}} $$
The gradient of the error function of the hidden layer node is Eq. (9):
$$ \frac{\partial E}{\partial {w}_{li}}=\sum \limits_l\sum \limits_i\frac{\partial E}{\partial {o}_l}\frac{\partial {o}_l}{\partial {y}_i}\frac{\partial {y}_i}{\partial {w}_{ij}} $$
The gradient of the node error function of the output layer is Eq. (10):
$$ \frac{\partial E}{\partial {a}_l}=\sum \limits_{k=1}^m\frac{\partial E}{\partial {o}_k}\frac{\partial {o}_k}{\partial {a}_l}=\frac{\partial E}{\partial {o}_l}\frac{\partial {o}_l}{\partial {a}_l} $$
In summary, the BP neural network is a multi-layer feed-forward network trained according to error back propagation algorithm. In this algorithm, first, a little value is given to the connection value of network, and then, a training sample is selected to calculate gradient of error relative to this sample. By utilizing the methods of mean square error and gradient descent to realize the modification to the connection weights of network. The continuous modification of weight values and offset values are applied to make the real output of network more closer to the expected one.
Figure 2 shows the schematic diagram of the three-layer BP neural network model designed in this paper. (The five types of networks correspond to the five models respectively. Here we take the 5G network model for example).
Wireless network training model diagram
The neural network model construction of this work can be described as follows:
First, the input layer of network and the attribute elements should be required. As presented above, the maximum transmission rate, minimum delay, SINR, bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in setting of BP neural network input layer neurons and the training and learning process parameters of subsequent neural network data. In order to ensure that the designed algorithm utilizes multiple network attributes comprehensively and makes effective handover strategies, the network download rate is adopted as prediction target to evaluate the network environment on the five wireless networks in which the user is located and selects the right wireless network that will help the algorithm to perform the process of vertical handover. Therefore, six neuron nodes are set at the input layer of the respective BP neural network models of the five wireless networks, respectively. The six input layer neuron nodes respectively represent the maximum transmission rate, minimum delay, SINR, bit error rate, user moving speed, and packet loss rate corresponding to each wireless network in the heterogeneous integrated network composed of the five major wireless networks.
To design the hidden layer. According to the empirical formula (11) of the number of hidden layer neurons to determine the number of hidden layer neurons:
$$ l=\sqrt{n+m+a} $$
Where n is the number of neural nodes in the input layer of network, m is the number of neural nodes in the output layer of network, and a denotes one number between 1 and 10 (including 1 and 10). And l is the empirical value of the final number of hidden layer neuron nodes.
As the hidden layer of the BP neural network model, the neuron nodes of the hidden layer play an extremely important role. In theory, a considerable amount of neuron node of hidden layer can perform an infinite numerical approximation on a nonlinear function with arbitrarily small error precision. But the number of hidden layer neuron nodes should not be too large, otherwise it will increase the computational complexity and computational cost of the network. At the same time, complex network computing is very prone to over-fitting. Conversely, if the number of hidden layer neurons is very small, the error will increase, which will seriously affect the performance of the neural network, and thus result in excessively difference from the expected theoretical value of desired decision. Appropriate number of hidden nodes can lower computational complexity and improve the training convergence speed. There are considerable correlation among the number of hidden layer neuron nodes, the actual complexity of the heterogeneous integrated network, the number of neuron nodes in the input layer and the output layer, and the expected error values set by the vertical handover decision. Finally, after comprehensive consideration, during the data training of the neural network, based on the principle of minimizing the final value of the mean square error function of the BP neural network, six neurons are set in the hidden layer according to the formula (11).
The third step is to design the output layer. Those six attribute data of five heterogeneous network collected by the terminal are finally input to their respective models, and then through network training, numerical approximation and prediction, and output their respective predictions of network download rate for the following decision.
Finally, after determining the prediction values of network download rate representing the five types of wireless networks and then comparing the value to select the most suitable wireless network at time. Therefore, for each neural network model corresponding to the wireless network, the number of neurons in output layer is set to one.
In summary, the three-layer BP neural network model corresponding to the five networks is built. Each model adopts the "6, 6, 1" mode, namely input layer have six neurons, hidden layer have six neurons, and output layer has one neuron, respectively. In the process of BP neural network training, for the maximum transmission rate, minimum delay, SINR, bit error rate, user moving speed, and packet loss rate of the five major wireless networks, take appropriate sample data and divide them into five groups as a reference sample of the BP neural network "6,6,1" model corresponding to each of the five wireless networks for instructor learning, then train the BP neural network models corresponding to the five wireless networks and after all of them have reached the corresponding mean square error standard, collect real data about the maximum transmission rate, minimum delay, SINR, bit error rate, user moving speed and packet loss rate in the heterogeneous integrated network environment and then input the real data of these six network attributes in the environment to the BP neural network "6,6,1" model corresponding to the five wireless networks that have been trained, the six BP neural network "6,6,1" models will obtain their respective network download rate prediction values through prediction. Then compare the six network download rate prediction values and finally select the wireless network corresponding to the highest value as the next network switching target, so that the algorithm completes a vertical handover process.
Vertical handover process
Based on the network model designed in Section 2.1, Fig. 3 shows the flow diagram of the implementation of the handover strategy designed in this paper.
Flow diagram of vertical handover in heterogeneous networks
The steps of the algorithm are the following:
The vertical handover algorithm starts to run when the user terminal collects relevant network attribute parameter values in a heterogeneous converged network environment composed of five wireless networks.
Next, the collected six types of network attribute parameters are sent to the input layer neurons of the three-layer BP neural network model represented by the five networks.
For the network attribute values that have been input to the neural network model, the BP neural network performs numerical calculation and approximation according to the previously reference values and expected values, and finally find the internal correlation of the neural network corresponding to each wireless networks' weights.
Then, according to the collected network attribute values, using the trained and learned BP neural network model to predict the network download rate of participating the final decision.
Finally, based on the predicted network download rates of the five wireless networks that have been predicted, numerical comparisons are made, and based on the compared numerical results, the network with the best performance is selected to perform the vertical handover decision.
The above five steps are the required operation to perform a handover decision for the vertical handover algorithm designed in this paper. In the real environment where the five heterogeneous networks are integrated, when the user moves to a different location, the six network attribute parameters set in this paper will change with the network environment around the user. Therefore, the data need to be collected continuously to achieve network handover in a realistic heterogeneous converged network environment. That is, the above five steps need to be continuously cycled to complete multiple decisions to adapt to the dynamic network environment.
Simulation results and analysis
The algorithm uses the MATLAB R2016b software platform for simulation evaluation. Table 1 shows the empirical values of the network attributes of the five networks and uses those values as a benchmark to take a certain range of fluctuations for supervised neural network learning.
Table 1. Experience value of training parameters in heterogeneous integrated network
The following is the simulation steps of the BP neural network model in this paper:
Firstly, the six network attribute reference values are normalized as neural network sample training parameters and input to the three-layer neural network model.
At the same time with step (1), respectively set up excitation functions of the BP network between input layer and the hidden layer neural network structure and the output layer neural network structure as the "logsig" node transfer function and the "purelin" node transfer function for parameter transfer and network training.
Then, set the initialization parameters inside the neural network, that is, a reasonable neural network iteration number(epochs) and predicted values expected error threshold (goal) are set for the algorithm.
Here, the algorithm designed in this paper sets the network iteration training number to 5000 times, sets the target mean square error of the training network expected prediction value to 0.00065, and sets the corresponding learning rate(lr) to 0.05.
After setting all the initial parameters, start training the network learning process and run the designed algorithm.
In summary, the algorithm designed in this paper is to set 20 sets of learning parameters for neural network training for each of the five networks, and then, we input the six network environment attribute values set according to the environment into their respective nerves. The handover process is run in the input layer of the network model, and finally the corresponding simulation results are obtained according to the selection of the performance optimal network by the neural network model.
Figures 4, 5, and 6's curves are BP neural network training error curves, training state curves, and data regression analysis curves, respectively. (Taking the training of related data of BP neural network model corresponding to UMTS network as an example).
BP neural network training error curve
BP neural network training state curve
Regression analysis curve of BP neural network data
The three solid lines shown in Fig. 4 are train, validation, and test, which represent the performance of the mean square error index of the BP training process in iteration, the performance of the mean square error index of the BP cross-validation process in iteration, the mean square error index of the BP testing process is expressed in iteration. The test line represents the BP calculation and the final result of the training. In addition, the best dotted line in the figure shows the algorithm designed in this paper when the neural network is trained to the seventh generation. The BP training result is the best, and the "goal dashed line" indicates the value of the network training termination target set by the algorithm when training the BP model. Overall, Fig. 4 shows that the algorithm designed in this paper passes seven iterations to reduce the mean square error to within the allowable range to achieve the ideal training results required by its BP neural network model.
The three lines shown in Fig. 5 represent the error surface gradient corresponding to the BP neural network model of the proposed algorithm in the data training process, the momentum parameters of the "trainlm" training function, and the sample data verification detection. According to the data shown in the Fig. 5, the BP neural network model corresponding to the algorithm in this paper has the error surface gradient and the momentum parameter of the "trainlm" training function has reached the expected requirements when training its network. The data verification test also stops in seventh steps, indicating that after seven consecutive tests, the error range has stabilized and the system will stop training to prevent over-learning. Therefore, Fig. 5 shows that the training state of the BP neural network is generally in line with expectations.
Figure 6 divides the training data into three parts, namely training, validation, and test. Only training data are used for training, and the other two parts of the data are not used for training. When the training is in progress, since the network is trained based on these data, the error between the target data and the training data will become smaller and smaller, and the error between the validation and validation targets will also become smaller at the beginning, but with as the number of training increases, the error of test continues to decrease, and the error of validation will increase. When the error of validation continues to rise to the number of times the user has set, the training stops (because there is a tendency to overfit). The abscissa of Fig. 6 is the target value set by the user, and the ordinate is the output of the BP neural network. Therefore, in general, the significance of Fig. 6 is to realize the regression analysis of the data. It can be seen from the Fig. 6 that the curves of the four images and the training data are basically on the diagonal, thus achieving a good data fitting, indicating the algorithm of the algorithm. The neural network model achieves an ideal prediction accuracy.
In summary, as can be seen from Figs. 4, 5, and 6, the BP neural network model established by the relevant parameters of the UMTS network achieves the expected error threshold through seven repetitions of learning, thereby completing the data learning, and the same reasoning the corresponding neural network models established by the relevant parameters of the four types of networks, GPRS, WLAN, 4G, and 5G, also reach the expected error standard set before data training after undergoing relevant repeated learning. Therefore, in these five types of networks, the results of the data learning and testing of their respective neural network models are shown below.
From the five simulation comparison diagrams given above, it can be seen that after BP neural network data learning, training, and comparison between errors, the average training error of Fig. 7 is 0.61%, and the average training error of Fig. 8 is 0.25%. The average training error of Fig. 9 is 0.50%, the average training error of Fig. 10 is 0.20%, and the average training error of Fig. 11 is 0.21%, so its corresponding UMTS, GPRS, WLAN, 4G, and 5G network prediction download rate and the actual download rate are basically the same and does not exceed the upper limit of 1.00% of the training error set by the algorithm. Therefore, the prediction data of the BP neural network model composed of these five types of networks can be considered to meet the relevant error requirements. That is, the predicted network download rate obtained under the heterogeneous converged environment composed of these five types of networks is reliable.
Contrast curve of data learning and testing in UMTS network
Contrast curve of data learning and testing in GPRS network
Contrast curve of data learning and testing in WLAN network
Contrast curve of data learning and testing in 4G network
Figure 12 shows the results of running the network selection handover strategy for these five types of networks using the vertical handover algorithm designed in this paper on the MATLAB simulation platform.
Running result of the algorithm
It can be seen from the above simulation results that the vertical handover algorithm designed in this paper uses the network download rate as the final decision factor to judge the advantages and disadvantages of the five types of networks in the heterogeneous integrated environment, so that the terminal can be in the current environment and switch to the network with the best performance. The simulation results in Fig. 12 shows that when the algorithm detects the current network environment, the user's mobile rate is 5.1 m/s, the maximum transmission rate is 930 kbps, the minimum transmission delay is 39ms, the SINR is 13.1, the bit error rate is 7.1 × 10−6, and the packet loss rate is 0.011, it is predicted that the network download rates of these five types of networks at this moment are 1.8778 Mbps, 287.0669 Kbps, 9.6537 Mbps, 4.6285 Mbps, and 0.21892 Gbps, respectively, and numerical comparison is made. Finally, the 5G network is obtained and the performance of 5G is optimal at the moment, thus handover the network selection to the 5G network.
In the field of using intelligent methods to design network vertical handover algorithms, fuzzy logic theory is also widely used. Figure 13 shows the performance comparison between the vertical handover algorithm designed and constructed by the BP neural network framework used in this paper and the vertical handover algorithm designed by the relatively popular fuzzy logic theory. So, a simulation performance comparison of the vertical handover algorithms is constructed. In addition, the comparison results of the handover success rate between the vertical handover algorithm design of the paper and the vertical handover algorithm based on fuzzy logic theory are clearly shown in Fig. 13.
Comparison results of different kinds of algorithms
The specific content reflected in Fig. 13 is reflected in the vertical handover algorithm based on the fuzzy logic theory existing in the related references such as the current reference [3], the reference [7], and the reference [17]. The algorithm performs comparative analysis and sets up the same heterogeneous converged network environment on the MATLAB simulation platform, so that the two algorithms respectively perform 20 vertical handover decisions in the same network environment, and the 5G network is in the set network environment. The downloading rate is the largest and the performance is the precondition. Observe the handover decision of the two types of vertical handover algorithms, that is, select the 5G network as the success number of the network to be switched, and finally, the vertical handover algorithm designed in this paper is within the error tolerance. The average success of the algorithm is 18 times, that is, the average value of the handover success rate reaches 90%, and the vertical handover algorithm based on fuzzy logic theory that participates in the comparison has an average of 17 handover successes, that is, the average value of the handover success rate reaches 85%. It can be seen that under the condition that there are many types of heterogeneous wireless networks participating in the integrated and the handover environment is more complicated, the handover performance of the vertical algorithm designed in this paper is relatively better than the vertical handover algorithm based on fuzzy logic theory mentioned in the related the reference [3], the reference [7], and the reference [17].
In terms of the user's experience on the wireless network, the download rate of the network is the most important indicator that affects the user experience, and it is also the most significant manifestation of the robustness and other performances. Therefore, we use the network download rate as the final prediction standard for the network selection strategy.
The vertical handover algorithm designed in this paper introduces the BP neural network model theory and integrates the theory into its running process, thus completing the three-layer BP neural network based on UMTS, GPRS, WLAN, 4G, and 5G networks. In the same way, there are also many references presenting a vertical switching algorithm based on neural network, which is similar to the algorithm designed in this paper, and its performance is the same as or even slightly better than the algorithm in this paper in terms of handover success rate, but the vertical handover algorithm based on neural network that has been proposed is generally used in the occasions where there are few types of wireless networks that constitute heterogeneous integrated networks, which makes most of the existing neural network-based vertical handover algorithms contain candidate network types that are not comprehensive and have limited applicability. Taking reference [11] as an example, although it proposes an adaptive vertical handover algorithm based on artificial neural network and the performance is similar to the algorithm designed in this paper, the algorithm designed in reference [11] only selects two wireless networks of LTE and WLAN networks as candidate networks. Obviously, 5G networks with generally better performance than LTE and WLAN are not taken into account. Therefore, compared with the existing algorithms that also use neural networks to perform vertical switching strategies, the algorithm designed in this paper considers the types of related candidate networks that are more comprehensive and the results obtained are more accurate and correct.
To run the algorithm designed in this paper, first, the BP neural network model needs to be constructed, and then the theoretical values of the six major types of network environment attributes such as maximum transmission rate, minimum delay, SINR, bit error rate, user moving speed, and packet loss rate are combined for data learning and network training. Through the actual values of the environmental attribute are input, the network download rate value is used as the final performance measurement factor of the five major networks to predict the data, thus achieving the process of screening the optimal network to complete the handover decision, and finally successfully achieved on the MATLAB platform. The user is allowed to obtain the correct vertical handover simulation result within the scope of the theoretical error allowed in the integrated environment of the above five types of heterogeneous networks. However, the vertical handover algorithm studied in this paper is still in the simulation research stage, but only achieved certain research results in the simulation research stage, therefore in the future research work related to "BP neural network" and "vertical handover." Such algorithms need to be combined with hardware circuits and applied to the areas of the five integrated wireless networks that have similar network performance, so that the algorithm can more efficiently implement vertical handover decisions, then further adjust and improve its operation process during the hardware test in order to improve its practicality in relevant engineering applications.
Finally, based on the performance of the handover success rate measurement algorithm, the performance of the vertical handover algorithm designed in this paper is compared with the performance of other representative vertical handover algorithms. The conclusion that the handover success rate of the vertical handover algorithm designed in this paper is up to 90% is obtained, and it provides a reference for other research in related fields.
Back propagation
Universal mobile telecommunications system
General packet radio service
WLAN:
Wireless local area network
The 4th generation mobile communication technology
SINR:
Signal to interference and noise ratio
MILP:
Mixed integer linear programming
BBO:
Biogeography-based optimization
Long-term evolution
Wi-Fi (wireless online)
WiMAX:
World Interoperability for Microwave Access
IEEE:
WLAN_UMTS:
Wireless Local Area Network and Universal Mobile Telephone System
L. Shufei, Z. Yuexia, F. Bo, et al., Multi-attribute vertical handover decision-making algorithm in a hybrid VLC-Femto system. IEEE Commun. Lett. 21(7), 1521–1524 (2017)
C. Jiamei, W. Yao, Y. Li, et al., QoE-aware intelligent vertical handover scheme over heterogeneous wireless access networks. IEEE Access. 6, 38285–38293 (2018)
R. Gongye, Q. Hua, J. Zhao, Decision-making algorithm for vertical handover based on multi-terminal cooperation based on fuzzy logic. J. Commun. 35(9), 67–78 (2014)
E. Obayiuwana, O.E. Falowo, Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: a review. Wirel. Netw 23(8), 2617–2649 (2017)
A. Chinnappan, R. Balasubramanian, Complexity–consistency trade-off in multi-attribute decision making for vertical handover in heterogeneous wireless networks. IET Networks 5(1), 13–21 (2016)
P. Dinesh, H.K. Beom, P. Jae-Young, et al., Maximizing network utilization in IEEE 802.21 assisted vertical handover over wireless heterogeneous networks. Journal of information processing systems 14(3), 771–789 (2018)
X. Liu, L. Jiang, C. He, A Pre-decision aided vertical handover algorithm based on fuzzy logic. Electr. J. 35(10), 1889–1993 (2007)
Suresha V., and Shankaraiah: Multi attribute vertical handover algorithm for heterogeneous networks. International Journal of Advances in Computer and Electronics Engineering., vol.2, no.3,pp.22-27(2017)
A.M. Miyim, M. Ismail, R. Nordin, Performance Analysis of Multi-level Vertical Handover in Wireless Heterogeneous Networks. Wirel. Pers. Commun. 95(2), 1109–1130 (2017)
G. Shidrokh, H.W. Haslina, A.M. Hossein, et al., MDP-based network selection scheme by genetic algorithm and simulated annealing for vertical-handover in heterogeneous wireless networks. Wirel. Pers. Commun. 92(2), 399–436 (2017)
Bin Ma., Shangru Li., and Xianzhong Xie.:Adaptive vertical handover algorithm based on artificial neural network in heterogeneous wireless networks. Journal of Electronics and Information., vol.41,no.0, pp.1-7(2019)
J.A. Huertas, Y. Donoso, Multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks. International Journal of Computers Communications & Control 13(6), 956–971 (2018)
Chandra I., Prabha K. Helen, amd Sivakumar N.: Optimization of QoS parameters using scheduling techniques in heterogeneous network. Pers. Ubiquit. Comput.., vol.22, no.5-6, pp.943-950,(2018)
Baojun LI.: Research on vertical handoff algorithm for heterogeneous converged networks. M.S.thesis, Harbin Engineering Univ., Harbin, China(2014)
Almutairi Ali F., Hamed Mohannad., and Landolsi Mohamed Adnan. et al.: A genetic algorithm approach for multi-attribute vertical handover decision making in wireless networks. Telecommun. Syst.., vol.68, no.2, pp.151-161(2018)
Hewei Yu., and Biao Zhang.: A heterogeneous network selection algorithm based on network attribute and user preference. AD hoc Networks., vol.72, pp. 68-80(2018)
X. Qi, J. Liang, N. Xiao, et al., Research on heterogeneous network selection algorithm based on fuzzy multi-attribute comprehensive weight. Modern Defense Technology 41(6), 76–82 (2013)
O. Nouri, B. Imen, A. Gharsallah, et al., Smart mobility management in 5G heterogeneous networks. IET Networks. 7(3), 119–128 (2018)
Shichen Li.: Design of several kinds of neural network stability analysis simulation system based on MATLAB. M.S.thesis, Chinese Marine Univ., Qingdao, China(2013)
Shidrokh Goudarzi., Wan Haslina Hassan., and Mohammad Hossein Anisi.: Intelligent technique for seamless vertical handover in vehicular networks. Mobile Networks & Applications.,vol.23, no.6, pp.1462-1477(2018)
P. Cunhua, Z. Huiling, N.J. Gomes, et al., Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN. IEEE Trans. Wirel. Commun. 16(5), 2891–2906 (2017)
Wang Li.: New Service Application and Key Technology of 5th Generation Mobile Communication Network. Message Communication., no.8, pp.243-245(2018)
Jyoti Madaan., and Indu Kashyap.: An Overview of Vertical handover Decision Algorithm. International Journal of Computer Applications., vol.111, no.3, pp.10-15(2015)
Huawei Li.: Application of 5G technology in mobile communication network. Computer Products and Circulation., no.5, pp.72(2018)
P. Cunhua, E. Maged, J. Wang, et al., User-centric C-RAN architecture for ultra-dense 5G networks: challenges and methodologies. IEEE Commun. Mag. 56(6), 14–20 (2018)
Siyu Tao., Hongyi Yu., Qing Li. et al.: Performance analysis of user association strategy based on power-domain non-orthogonal multiple access in visible light communication multi-cell networks. EURASIP Journal on Wireless Communications and Networking volume 2020, 80 (2020). https://doi.org/https://doi.org/10.1186/s13638-020-01688-3
Marwan Alakhras., Mourad Oussalah., Mousa Hussein.: A survey of fuzzy logic in wireless localization. EURASIP J. Wirel. Commun. Netw. volume 2020, 89 (2020). https://doi.org/https://doi.org/10.1186/s13638-020-01703-7
R. Shinkuma, T. Nishio, Y. Inagaki, et al., Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learning. EURASIP Journal on Wireless Communications and Networking volume 2020, 92 (2020) https://doi.org/https://doi.org/10.1186/s13638-020-01709-1
Qing Yang, Shijue Zheng, Ming Liu. et al.: Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication. EURASIP Journal on Wireless Communications and Networking volume 2019, 275 (2019). https://doi.org/https://doi.org/10.1186/s13638-019-1601-3
Yansong Liu, Li Zhu: A new intrusion detection and alarm correlation technology based on neural network. EURASIP Journal on Wireless Communications and Networking volume 2019, 109 (2019). https://doi.org/https://doi.org/10.1186/s13638-019-1419-z
Mehdi Aslinezhad, Alireza Malekijavan, Pouya Abbasi: ANN-assisted robust GPS/INS information fusion to bridge GPS outage. EURASIP Journal on Wireless Communications and Networking volume 2019, 129 (2020). https://doi.org/https://doi.org/10.1186/s13638-020-01747-9
Mingxiang Guan, Zhou Wu, Yingjie Cui. et al.: An intelligent wireless channel allocation in HAPS 5G communication system based on reinforcement learning. EURASIP Journal on Wireless Communications and Networking volume 2019, 138 (2019). https://doi.org/https://doi.org/10.1186/s13638-019-1463-8
This work was supported by the National Natural Science Foundation of China under Grant No. 61701284, the China Postdoctoral Science Foundation under Grant No. 2016M602165, the Innovative Research Foundation of Qingdao under Grant No. 19-6-2-1-cg, the Opening Project Fund of State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology under Grant No. MDPC201715, the Key Research and Development project of Shandong Province under Grant No. 2018GHY115022, the Shandong Natural Science Foundation under Grant No. ZR2017MF070, the Qingdao Postdoctoral Application Research Project under Grant No. 2016113, and the Science and Technology Support Plan of Youth Innovation Team of Shandong higher School under Grant No. 2019KJN024.
College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
Xiaonan Tan, Geng Chen & Hongyu Sun
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China
Hongyu Sun
Xiaonan Tan
Geng Chen
Conceived and designed the experiments: XNT, GC and HYS. Performed the experiments: XNT. Analyzed the data: XNT and GC. Wrote the paper: XNT, GC and HYS. All authors have contributed to this research work and read and approved the final manuscript.
Xiaonan Tan received the B.S. degree in communication engineering from Shandong University of Science and Technology, Qingdao, China, in 2018. Now, he is an academic master student in the College of Electronic and Information Engineering of Shandong University of Science and Technology. His research interests are in Internet of things, vertical handover algorithms, neural networks, MPPT, and communication and information system.
Geng Chen received the B.S. degree in electronic information engineering and the M.S. degree in communication and information system from Shandong University of Science and Technology, Qingdao, China, in 2007 and 2010, respectively, and the Ph.D. degree in information and communications engineering from Southeast University, Nanjing, China, in 2015. He is currently an Associate Professor at the College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China. His current research interests are in the areas of heterogeneous networks, ubiquitous networks and software-defined mobile networks, with emphasis on wireless resource management and optimization algorithms, and precoding algorithms in large-scale MIMO.
Hongyu Sun received his Ph.D. degree in Computer Science from Tongji University in 2012. Now, he is an associate professor in the College of Electronic and Information Engineering of Shandong University of Science and Technology. His research interests are in wireless communication theory and advanced digital signal processing system.
Correspondence to Geng Chen.
Tan, X., Chen, G. & Sun, H. Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network. J Wireless Com Network 2020, 202 (2020). https://doi.org/10.1186/s13638-020-01822-1
Multi-attribute
Vertical handover


text:
Assessmant
These 4 New Yorkers Are Experts in Living. What Do They Know That We Don't?
In 2015, we began following six people over age 85. Last year the four remaining showed that old age is not what you think.
Nearly four years ago, I began following six people over age 85 to see what their lives were like: what kept them going, what they hoped for or feared. This past year, I asked Jonas Mekas, now 96, about death and the afterlife.
The question had particular resonance. In summer Mr. Mekas had been hospitalized for a blood disorder that was still mysterious to his doctors. It was the first sign in four years that he was mortal. He canceled a trip to Berlin because he was tired and short of breath, and was now walking with a cane, his complexion grayish. Since his 20s he had used his movie camera to protect him from the outside world. Now his doctors were using cameras to explore the worlds within.
He did not think much about death, he said. He had grown up next to two cemeteries in rural Lithuania, where the gravediggers drank beer and talked between jobs, and so that was how he thought about death, as something commonplace.
"It's a very normal transition," he said. "What's beyond that line, it's where the mystery begins, where it becomes interesting. There are glimpses in the messages that come from there, some of the old Scriptures. Indications are there, and I believe it all. I believe it much more than anything that's written since the 12th century."
We were onstage at Anthology Film Archives, the nonprofit theater he helped start in 1970, and he turned to address his audience. "All I can tell you," he said, "is that life does not end in this room."
So closes another year for some members of what gerontologists have called the "oldest old," one of the fastest-growing age groups in the country. Their world got a little smaller, its mysteries a little more pressing. Two of the series' six subjects died in 2016 — Fred Jones, who asked God for 110 years, and John Sorensen, who missed his partner of 60 years, and who said every time we got together that he wanted to die. Both gone within a few months of each other — death, apparently, makes its own plans.
For the others, 2018 was a challenge. Ping Wong turned 93 in a nursing home in southern New Jersey: dementia. Helen Moses, still not married to Howie Zeimer, turned 94 at the Hebrew Home in the Riverdale section of the Bronx: a fall, some Parkinson's-like symptoms, then internal bleeding. Ruth Willig turned 95 in Sheepshead Bay, Brooklyn: a fall on a bus, another in the shower, an infection that took two courses of antibiotics to curb.
[Read earlier installments of John Leland's '85 and Up' series.]
But this is an accounting of maladies, not of lives. Ms. Wong spoke by video with a goddaughter in China she hadn't seen in 50 years; Ms. Moses and Mr. Zeimer very nearly had a religious commitment ceremony in October, before health concerns intervened; Ms. Willig traveled to the wedding of her granddaughter and celebrated her great-granddaughter's first birthday.
"She was going come hell or high water," Ms. Willig's daughter Judy said of the wedding. In November, when the family gathered for a small 95th birthday celebration, Ms. Willig said that if she made it to 100, they could hold a big one.
And for Mr. Mekas, it was a year like no other. He paid tribute to a forgotten cinematic trailblazer, raised millions of dollars to expand Anthology Film Archives, played a major role in a New York exhibit about the Velvet Underground and was accused, in The New York Review of Books, of downplaying his interactions with anti-Semitic elements in Lithuania during the Holocaust. After the last, he gave a detailed, six-hour interview to the United States Holocaust Memorial Museum, describing a time period about which he has rarely spoken in public.
What a year. What a four-year stretch. If they'd been freshmen when the series started, they would be new graduates now, with diplomas and plans. Instead, their four years have been extended improvisations, their goals and achievements rarely reflected in the broader culture.
Memories kept bubbling up, an abundance compared to the thinning years ahead. For them, memory remains something in the head, not in the device, and when they depart, the process of remembering will change without them. So much is said about memory loss in old age. But what about memory muscle, which younger generations have not needed?
They were born when American life expectancy hovered around 60 years, and they surpassed current expectations around the time they rounded 80. But life, too, has plans: at age 80, an American man can expect eight more years, a woman almost ten. That's longer than adolescence.
Ruth Willig turned 95 in November at a small family gathering. She would consent to a bigger celebration, she said, if she makes it to 100.CreditEdu Bayer for The New York Times.
And still, it has been left to them to invent life at their age, without the guideposts or role models of their earlier years. As the British novelist Penelope Lively wrote when she was 80: "Our experience is one unknown to most of humanity, over time. We are the pioneers."
Having gotten old in an aging culture that still worships youth, they have done the unthinkable: gotten older.
"Did you ever meet anyone like me?" Ms. Moses asked one day at the Hebrew Home. "I'm special. A lot of people know me here. I don't know them. They go, Hi, Helen."
Ms. Moses started the year visibly slower than she'd been at the start of the Times series, no longer boasting about being in trouble with this or that nurse. But she was also quick to express gratitude toward her daughter and Mr. Zeimer, who were often at odds.
"Howie," she said, addressing Mr. Zeimer, "aren't I kind to you? You better say yes."
He did, adding that over the last year, "She's grown older and more wiser and more perfect for me."
"He's very kind," Ms. Moses said. "We never had a fight. We never left each other where he just said good night. Always with a kiss.
"I kiss him good-night," she said. "Want to see?"
"No," said her daughter, Zoe Gussoff, before reversing herself: "Whatever makes them happy."
It was lunch hour, but Ms. Moses pushed her sandwich away after a few bites. This year she lost much of her appetite and has lost some weight. At 94, she still does not need glasses, but wears hearing aids, which the nursing home staff stores at night. When she gets ambulating with her walker, it is hard to keep up with her.
"You're going to be here 10 years in February," Ms. Gussoff told her.
"That's a long time," Ms. Moses said. Most of the friends she made when she first got to the home had died. "It's a nice place," she said. "It's clean. Most people are nice. Some are not. That's it."
In spring, she fell and required four stitches by her eye. The fall meant increased supervision from nursing home staff, and for the first time, a wheelchair — a severe drop in autonomy for Ms. Moses. During treatment, doctors also noted Parkinson's-like symptoms, and they were uncertain whether she would ever walk again.
Ms. Gussoff took her to an outside neurologist, who enabled her to walk using a walker. But the turn in Ms. Moses's health had a bigger consequence: Ms. Gussoff, who had vehemently opposed any marriage between her mother and Mr. Zeimer, was now willing to bless a commitment ceremony, as long as the couple didn't live together, and as long as Ms. Gussoff would remain in charge of her mother's health care.
"My daughter convinced me," Ms. Gussoff said. "She said, 'What would be the harm? Grandma's 94, what's the difference? Make her happy.'"
The couple, who are fans of the show "Say Yes to the Dress," made plans. On the internet, Ms. Gussoff's daughter found something called a "Short Scoop Bridesmaid Floral Lace Dress Cocktail Formal Swing Dress." They lined up a rabbi and picked a couple of dates in October for the ceremony.
"Commitment," Ms. Gussoff reiterated. "We don't say the word 'marriage.'"
Then life intervened.
As the dates approached, Ms. Moses started complaining about stomach pains. When she finally got to a hospital emergency room, doctors found bleeding from her lower intestine. She spent two and a half weeks in intensive care or recovery.
Helen Moses and her boyfriend Howie Zeimer. They have moved closer over the series and are planning a commitment ceremony.CreditEdu Bayer for The New York Times.
"I was lonesome," Mr. Zeimer said. "I wondered whether it was possible to come and see her but they told me no."
When Ms. Moses returned to the nursing home, life intervened again. This time, the rabbi got sick.
She and Mr. Zeimer have not given up hopes to get hitched, as Mr. Zeimer puts it. In the meantime, he moved to a room closer to hers, and still watches television with her at night, though he leaves earlier than previously because they get tired.
As in past years, Ms. Moses talked often about her own mother and how much she missed her. Her mother had taught her so many things, but not how to be 94: how to manage a daughter who liked to take charge and a boyfriend who made her feel needed again, all set against the daily wear of aging. In this act she was on her own.
Summing up, Ms. Moses said, "If it wasn't for the hospital I had a good year."
Two hours south, in Voorhees, N.J., Ms. Wong asked after my mother, who turned 90 this year, and broke her hip. Did she have activities where she lived? Ms. Wong asked.
"Here you have three meals, a bedroom and then a lot of activities," she said, describing her days in the nursing home, where she moved in 2016 to be near her daughter. "You can pass time, enjoy yourself. Usually this kind of activity is good for the elderly, but your mom is too far from this place, otherwise it's better to come here to enjoy herself."
Four years ago, at the start of the series, Ms. Wong had lived in a subsidized apartment near Gramercy Park in Manhattan, where she spent her days playing mah-jongg with three women from the building. The onset of dementia and consequent move to the nursing home had been very difficult for her, and this year her spirits were up and down. Though she has made new friends, she also had recurring bouts of anxiety, including one that landed her in the hospital.
"She kept telling the people they put hot water on her and burned her skin," Ms. Wong's daughter, Elaine Gin, said. "Delusions. Finally they sent her to the hospital. I was there at 4 or 5 o'clock in the morning. I entered the emergency-room door and I can hear her talking. She's nice, she says, 'Thank you very much,' 'You're so nice.' They do all the tests. She's fine, but she's talking and talking and talking. At 11:40 my friend came so I could go to work, and she's still talking. They moved her to a room, and she couldn't stay by herself. I got off early, at 4 o'clock, and she's still talking. At 6:30 she finally calmed down. She wanted to stand up, she wanted to walk around."
In late November she was back in the hospital, this time for dehydration and kidney malfunction. When I asked what was wrong, she said: "I think I fell down, because I am very weak. Now I can't take very big steps."
But even with her setbacks this year, Ms. Wong remained proudly resourceful, insisting that her rewards at the home outweighed the hardships.
"I'm the oldest, so I know everybody when they are admitted," she said. "I have a lot to talk about what we are facing."
And she found a new way to begin our conversations: "I remember you," she'd say, and the act of remembering — and the joy of declaring it — seemed to fill the moment with meaning, a small gift near the end of a long life.
In Sheepshead Bay, Ruth Willig gave thanks for larger gifts this year: as she stayed mostly the same, her family changed around her.
"I dare not talk about not surviving," she said one afternoon in her apartment, where balloons in the shape of a 9 and 5 held their last whiffs of helium. "My children, my son especially, say, 'Oh, Ma, you're going to keep going forever.' The thought of my passing is very upsetting to him."
Ms. Willig could not help noting the passage of time, especially the absence of her three siblings. Once the youngest, she was now the last of her generation. "It's weird to be the only one left, it really is," she said. "I can't really call anyone: do you remember this? It was not easy at first. I'm getting used to it."
It was a difficult year for Ping Wong, 93, who faced the onset of dementia.CreditEdu Bayer for The New York Times
Patterns held: she avoided most activities in her assisted living building, and she went to the New Jersey Shore with her children for a summer vacation. She canceled her weekday newspaper subscription because the papers were adding up — "And how much can you read, really?" she said — but then renewed it because she missed it.
Some things got harder. Photographs of her great-granddaughter abounded, but only in people's cellphones, "so what does it do for me?" she said. The house at the shore was advertised as accessible for people with disabilities, but there was no grab bar in the shower, and the high-end mattress in her bedroom was too high for her to climb onto.
"They haven't walked a mile in the shoes of someone who needs it," Judy Willig said.
Then Ms. Willig banged her shin on the steps of her building's shuttle bus — a small injury for a younger person, but the impact shredded the thin skin on her leg, and because she was on baby aspirin to prevent a stroke, there was a lot of bleeding.
The wound required her to cover the leg in waterproof wrap when she showered, which led to another fall.
"When the cut got infected, we both worried," Judy Willig said. "We didn't talk about being worried, but we were."
Still, there was a wedding and a birthday. And the unlikely fact of another year, with its ups and downs. Ninety-five, who would have thought?
"I find it amazing, how we're living so long," she said. "We keep going on."
Jonas Mekas glided into 2018 on the wings of other artists, looking backward at departed associates. Through a German publisher, he resuscitated his defunct magazine Film Culture for a book-length issue devoted to Barbara Rubin, an extraordinary character who blew into New York and Mr. Mekas's life in 1963 as a fearless teenager, and wound up connecting Bob Dylan to Allen Ginsberg and Andy Warhol to the Velvet Underground. After making a taboo-busting underground film called "Christmas on Earth," she left the scene for France and ultra-Orthodox Judaism, and died at age 35 of an infection after giving birth to her fifth child. Like Mr. Mekas, she'd been one of the vital background players who made better-known artists like Mr. Warhol or Lou Reed possible.
In midyear Mr. Mekas's backward glances took an unwelcome turn. In a long article in The New York Review of Books, Michael Casper examined Mr. Mekas's work at two Lithuanian publications during the Nazi occupation, where his poems or reviews sometimes ran beside virulently anti-Semitic calls to violence. The article noted that Mr. Mekas's writing was not anti-Semitic, but argued that his proximity to pro-Nazi hate speech clashed with the image he has created for himself. Mr. Casper wrote that Mr. Mekas "has repeatedly manipulated his story, taking advantage of people's ignorance of wartime Lithuania, to make himself appear, when useful, as victim, hero, or oblivious bystander."
Mr. Mekas denounced the article as "dirty" and inaccurate, arguing that the toxic material in those newspapers ran only briefly and that the papers were principally devoted to local news and culture, a tacit form of resistance to occupation. After 75 years, he said, that period remained unreal to him, because it was "such an unnatural intrusion, that I cannot identify myself. It's like it does not belong, that period."
At an art opening shortly after the article appeared, Mr. Mekas looked weak and nearly broken — a 95-year-old crushed by a ghost from his past. But another culprit surfaced: a hemoglobin deficiency and polyps in his digestive tract. By fall, between monthly blood transfusions, he was gushing about new music by Eminem and Cat Power. As the shock of the article receded, he focused on the miracle of his care.
"Which translates into strengthening my trust in the essential goodness of humanity," he wrote in an email health update to friends. "No, humanity is not yet lost. It's only confused. Yes, the bad news dominate all the media. But there are unseen, invisible millions of men and women who daily perform invisible but essential human acts that, I firmly believe, is moving the human evolution along the lines envisioned by the Poet of the Eternity."
Would Mr. Mekas have had this perspective at a younger age? Perhaps not. But at 96, it defines him.
He ended the year tired from another trip to the hospital, his blood condition seemingly stabilized but his appetite scarce from acid reflux. "There are aspects that are improving," he said on New Year's Day. "Maybe nothing is getting worse, but remains the same."
So begins a new year for four pioneers on an alien frontier, and for the exceedingly grateful reporter who has followed them through 17 articles, one video, a book and countless glimpses of past and future. Their stories are a year closer to the end, as are all of ours. But all discovered something in 2018, and all said they were glad to have experienced it.
Onward to 2019, whatever it may hold.
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text:
Record of Lodoss War Fansite.
Geo El Orin
Pronunciation: GEE-oh ehl oh-REEN
Forum ID Mana Link
Plot Liberation of Ales
Geo is © Mana Link.
Carry on Wayward Son - Kansas (Listen ♫)
Update Thread (Use for updates, corrections, etc.)
Geo's Gallery
Birth Year NRC 508
Homeland Durhalem Village, Isle off Alecrast
Geo stands at an average human height of five-foot, six inches tall, with a wiry frame that makes him look weaker than he truly is. His green hair is a mess that should, if brushed and washed normally would be straight and shoulder length. Due to its rather unkempt manner however, his hair stands up in the front, and in the back it's held down by a long dark-blue bandanna. He has a boyish face for his age, with a small, slightly upturned nose and a thin-lipped wide mouth.
Height 5'6" Hair Color Green
Weight 125lbs Eye Color Red
Build Wiry Skin Color Tanned Peach
Clothing and Armor
Geo's fashion-sense is seriously stunted, to say the least. For a shirt he wears an purple maroon ethnic rug wrapped around his torso and cinched off at the waist with a sand-colored tie belt. His pants are loose and purposely tattered, cuts starting at the knee and measure half-way down his shin before being cut horizontally at boot height. Under his makeshift clothing, he wears a piece-mail suit of armor, most of it hidden by the clothing, but some, like the plated leather sleeve-gauntlet, and the leggings, are out in the open for show. Over his right eye he wears a black eye-patch to purposely give himself a blind spot.
His piece mail armor consists of a steel breastplate with thick leather straps, a steel-plated and leather sleeve-gauntlet, a right-handed black leather swordsman's glove with a steal backing and knuckle guard, and steel shin and foot guards with limited leather padding. This suit of armor, most of which he scrounged together himself, was found while he was in service to Mora.
Weapon Description
Geo's sword, made by his cousin's family, the Al Darin smiths, is a three-and-a-half-hand shortsword, having a particularly long hilt wrapped in dyed shark-skin. The blade, a simple, yet elegant three foot long single-fuller blade, is made from lodestone, dual edged and engraved with the signature of the Al Darin family in the blade's fuller. He was able to obtain the sword originally meant for him by slaying the one who took it, the pirate captain who took him and his people from Durhalem.
Class Mercenary
Occupation Swordsman/Brawler
Guild Membership Shadow Guild (2), Mercenaries' Guild (2)
Weapons Long-Hilted (3 1/2 handed) Short Sword
Armor Piece-Mail Armor; steel breastplate, left steel pauldron, left leather sleeve and gauntlet, fitted steel plates for upper arm, forearm and back of hand, right handed swordsman's glove, with plate backing and knuckle guards, steel shin and feet guards
Equipment Clothing, eyepatch
Horse No
LU 14
Geo is a full-fledged swordsman, relying on his natural born agility rather than brute strength, as many swordsmen before him have. His unnerving speed and lack of displayed emotion make his chaotic and erratic movements seem almost inhuman. His favored tactic is to run into the fray of battle and strike from the countless blind spots he sees in his enemies defenses. His fists and feet are his secondary weapons, but act almost as if they were his primary. His utilization of precise and swift kicks and punches to vital areas leave his enemies exhausted and crippled at death's door. To overcome his natural-born muteness, he learned to speak with his hands and to read and write. His abilities are for the battlefield however, and he lacks the proper attitude towards learning things that either don't let him communicate or fight.
Geo's eyes betray his lack of emotional response, despite what his face and body speaks. His personality can be summed up with four words: passive, methodical, honorable, and chaotic. Despite being mistreated by others in the past, he seems to trust people. He has learned to make educated judgments about people based on their appearance, first-impression on him, and their speech. If any of his instincts tell him otherwise, his normally docile attitude will flip and he'll become hostile to the person offending him. Derogative terms, as well as the concept of slavery irritate him to no end.
Alignment: Chaotic Good
Quotes: "..."
Geo was born in the late spring in the village of Durhalem, on an islet off the southern most shores of Alecrast, to Jer Adul El Orin, the village guardian and Katarin Al Darin, the daughter of a long line of smiths. When Geo was old enough to walk on his own, Jer Adul and Katarin left Durhalem on a quest taking them to the far north of Alecrast. Despite being born a mute, the fellow villagers of Durhalem treated Geo with utmost respect, but disciplined him none-the-less if he ever got into trouble. After a decade of tutelage under the interim guardian of the village, Geo was sent on a quest outside Durhalem's borders to live a week in the monster infested wood as his rite of passage into adulthood.
The task itself wasn't impossible, and it taught many of the other children how best to survive of a bad situation. Geo was no exception to this. With a dagger to defend himself and a knowledge of what plants were edible, where some fresh water was and where most of the non-migratory settlements of monsters were, Geo was able to spend most of the week out in the woods without suffering any harm or injury. The night before he was allowed back inside he was forced to confront a trio of goblin scouts. Utilizing his agility and previous training he was able to dispatch them without suffering any serious injury, save for a few scrapes and a broken left arm. Even with the broken arm, he stayed out of the village 'til morning light where he was welcomed back as a man.
Despite his uncanny fighting ability and aptitude for battle, at twelve he was still too young to be taken seriously as the Guardian of Durhalem, so they reserved to give him the title in two years when he'd be a little more experienced. For those two years he studied hard as a swordsman and learned a little of how to read and write, to allow him to communicate more than with the sign language he used already. At the age of fourteen, Geo El Orin, son of Jer Adul, was proclaimed Guardian of Durhalem and betrothed to the village's eldest daughter, Mielluire Atapos, who was four years his senior. As it was custom, his cousins, the Al Darin family of smiths, were to forge him a sword, and when it was to be finished, the marriage of Mielluire and Geo would take place at the village's holy site, a large stone altar that the village was built around.
It took a half a year to complete, Geo's sword, dubbed Al Arin, forged from lodestone and blessed upon the village's altar to their long forgotten deity. On the day of the wedding, while the guard was down, pirates raided the village. In the struggle Mielluire was killed and Geo and a few other villagers were captured to be sold as slaves in Alecrast. Mourning the loss of life of the people he knew and loved, as well as the loss of freedom for him and the handful of villagers that survived the raid, Geo was ill prepared for what happened next. One of the pirates came below deck to fool around with one of the women they had captured. While the pirate was in the midst of violating the young woman, Geo broke free of his bondage and killed the pirate man, however he was unable to save the young woman, as she chose to end her life by biting her own tongue off than to live with the shame of being violated. Not wishing for any more of his village people to suffer the same fate of being violated, Geo freed his fellows and lead them in a revolt against the pirates that captured them. Although the revolt itself wasn't successful, many of the pirate crew was injured and the captain knew best where to send the young man, Geo, who caused the ruckus.
Geo was sold last out of all the slaves, and not through the market as the others were. The pirate captain wanted to see the young boy suffer for hurting his men and daring to lead a revolt on his ship so he sold Geo to the coliseum to be a gladiator slave. In his gladiatorial debut he fought against an ogre, armed with a fishing spear and a flimsy wooden shield. The pirate captain who wished to see Geo in a great deal of pain arranged the fight. Despite the obvious strength difference between Geo and the ogre, Geo's speed and agility, as well as the extended reach of the fishing spear and the ogre's natural clumsiness made the battle rather one sided in Geo's favor. With the simple bout over and Geo the victor, the pirate captain arranged a second bout to follow immediately after. The second bout was against a gladiola taken from the Azarns. While she had more experience and strength than him, as well as she could almost match him for speed, Geo's determination to succeed drove him to kill her with his bare hands.
The pirate captain was furious, but was running low on funds to continue to set him up against what seemed like impossible odds for a child to overcome; however, The pirate captain's financial burden was alleviated by a rich noblewoman who had a taste for younger men. Having seen young Geo fight two fights with fierce determination and remarkable grace, she knew that she had to have him. Geo was glad to leave the blood and sand of the coliseum but wasn't fully ready for what was in store for him at the noble woman's villa. She had bought him for the sole purpose of serving as a sex slave, and at the first inappropriate touch in private, Geo flipped out and broke her neck. Fueled by fear for the consequences of his action he ran out into the streets to get as far away from the scene of the crime.
To escape persecution Geo sneaked aboard a ship destined to anchor in Raiden, Lodoss. With all the cursed luck he happened to stow away on the same ship that brought him from his home to begin with. His cursed luck allowed him to regain something lost at the raid, the sword forged for him by his cousins, the Al Darin smith family. Since the marriage was never completed, the sword had no sheath, which was to be a gift from wife to husband, to complete the sword and complete the marriage. Realizing at last where he was, Geo concocted a plan to dispatch of the very raiders that killed his betrothed and enslaved his island people. Over the course of the months at sea, Geo kept stealing food and water from the hold and throwing what he didn't eat into the waters behind them, with food and water supplies dwindling on the ship, many of the pirates were at each other's throats, thinking that one another were stealing rations without the captain's permission. Soon the ship was in bad shape, the pirates all distrusting one another, as well as the captain.
Regardless of the lack of morale and distrust amongst the crew, they managed to get to port in Raiden. As soon as the ship had docked, Geo leaped from his hiding spot, and went about killing each and every one of the pirates for the sake of revenge of his people. When the blade was heavy and slick with blood, stained crimson with Geo's anger and the blood of the wrongdoers, the captain graced Geo's appearance. At once the two locked eyes. Geo's hatred for the man who stole him from his land, and the hatred of the captain for losing his crew, made the two bitter enemies despite how short they knew each other. Geo waited for the man to draw his sword, not wishing to strike down an unarmed man. The pirate captain drew, thus sealing his fate. Ushering all the strength and speed he had left, Geo lunged forward with terrible speed and bisected the captain with his sword. Exhausted and alone, the lone survivor of the massacre aboard the pirate vessel, Geo crawled off with his sword in tow, and nestled himself amongst the discarded fishing nets to sleep the night through.
When Geo awoke, he found himself in a bed. An older man dressed in rags had fished him from the fishing nets before authorities had found him and spirited him away to his shack. He explained that he was just a humble fisherman who had lost everything out on the sea. A fortune, a career, his woman. All things that he took for granted were swallowed by the merciless sea. He and Geo spent several months together, getting to know each other and trying to establish a repertoire, considering Geo's muteness. When the two trusted each other enough, the older man brought Geo into the Shadow Guild, where upon his recommendation, Geo was admitted into the ranks of the guild. For the first few missions that Geo accepted, the older man accompanied him on them, making sure that he did the assassination quickly and efficiently. When Geo's probational period was over, Geo was allowed to take his own missions if he wanted. With tensions increasing in the south, Geo was drawn to the conflict between the nations of Lodoss and Marmo, and took his business into Kanon to assassinate corrupt officials and other wrongdoers that Geo was able to obtain contracts for their lives.
When the front of the war moved to Marmo, as well as most of the attention, the now seventeen year old boy stayed roaming within Valis, Kanon and Alania, dealing out justice within the confines of the contracts of the Shadow Guild. With the closing of the year, and the end of the war, many of the jobs that Geo would undertake had dried up. Knowing that a great deal of jobs could still be waiting for him he moved back towards Raiden. What was awaiting him in Raiden was anarchy and chaos, most of which he became unused to in the southern reaches of Lodoss. With the war over, most of the war profiteers had set up shop in Raiden, due to its status as a free city. Returning to Raiden had brought another calamity down on Geo's head. Many of the people he offed had family who decided to hire the shadow guild to take out one of their own. The man who inducted Geo into the shadow guild took the job, believing that he was the only one capable of beating the young mute in a fight, if it came to it.
Geo's old master came up to Geo personally, taking him to the outskirts of the city and explaining the situation to him. As a final courtesy, Geo's old master gave Geo the chance to run away. Geo didn't take the offer and set his cold eyes on the man who fished him from the nets and gave him a purpose in this new land. The fight was over in mere seconds—Geo mustering all his incredible speed to flash forward towards his old master, striking him down before he had the chance to draw his sword. The fight was not over, however. Fearing that the old man wasn't enough, a few of the guild officers had hired some ruffians to finish off Geo if the old man didn't already finish the job. Vastly outnumbered by properly fed mercenaries, Geo was at a sore disadvantage, even with his speed and agility considered. It was while fighting this uphill battle that he met his future companion, Mora, a centauress that came in like a crashing wave on a wave break. Flattening some of the men beneath her hooves, and picking off others with her weapons, the fight was over with few survivors on the opposing side.
Having been saved by the woman, Geo followed her around, watching her from the shadows and seeing just what kind of person she was. After a while he found himself inspired by how she acted, and decided to approach her cautiously. Having no way to communicate other than writing and sign language, Geo wrote a request to join up with Mora, to become one of her fellows, and that he wouldn't take no as an answer. After several months of him pestering her, she finally agreed, and their partnership began there. He worked the next four long years beside Mora, acting as an extension of her own arms and legs, as he continues to do to this day.
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Record of Lodoss War is © Ryo Mizuno, Group SNE, Kadokawa Shoten and all other applicable groups.
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Raymond Chabot Grant Thornton, Proud to Team Up With the New TV Series Métier Dirigeant
MONTRÉAL, March 31, 2017 – Raymond Chabot Grant Thornton is proud to be involved with the new Métier Dirigeant TV series on management leadership, which will air on V-Télé on Saturday, April 1, 2017, 12 p.m.
In this series, Éric Paquette, President and Co-founder of the Institute of Leadership and series host, will introduce six corporate leaders who took Institute of Leadership training.
"The docu-reality TV show is quite dynamic. In this series we meet and get to understand and see what defines exceptional individuals who become leaders in life. With the development of dynamic, local businesses at heart, we continue to support and associate ourselves with these inspiring shows that make viewers aware of the realities of entrepreneurship throughout Quebec, a pillar of our collective growth," stated Emilio B. Imbriglio, President and CEO of Raymond Chabot Grant Thornton.
In addition to the TV series, viewers can also watch the advisory capsule by Nancy Jalbert, Strategy & Performance Consulting Partner at Raymond Chabot Grant Thornton, which explains the qualities of a good leader. This capsule, airing on TV in the coming weeks, can be viewed on the Raymond Chabot Grant Thornton Youtube webpage.
"Leadership is an essential characteristic that sets dynamic and passionate managers apart. What motivates us every day is to properly foster this aptitude in the people of talent on our teams, so that they can fulfil their ambitions. This association with the TV series underscores our desire to emphasize leadership and boldness, undeniable assets for success," concluded Yasser Waly, Partner, Business Development & Strategy.
Anne-Marie Guertin, Regional Manager, Marketing & Communications, Lise Monette, Vice-President Marketing & Communications and Ghyslain Cadieux, Senior Manager, Management Consulting, in the company of the leaders highlighted in the series and the producer of this TV show.
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2017-2018 Quebec Budget: Raymond Chabot Grant Thornton Publishes Its Tax Bulletin with Observations
Marc Audet
President and Chief Executive Officer | AURAY Capital and AURAY Sourcing | Human resources consulting
QUÉBEC CITY, March 28, 2017 – In response to the Quebec budget tabled today by Finance Minister, Carlos Leitão, Raymond Chabot Grant Thornton publishes its summary of the main tax measures announced with a number of observations. The reference document prepared by a team of tax specialists in the lock-up may be consulted here. Additionally, the firm highlights some budget measures and invites the government to act on a number of issues.
A budget focussing on services to the population…
Raymond Chabot Grant Thornton lauds significant investments in health and education and public transit that will foster job creation.
"There is no doubt about the benefits of additional investments of $289M for labour development measures. Additionally, improvements to two measures to support businesses, i.e. three-year extension of the tax holiday eligibility period for large investment projects and the introduction of an additional capital cost allowance—representing tax relief of $165M over five years—should bring a breath of fresh air," indicated Tax Partner, Luc Lacome.
"In a context where entrepreneurs—our wealth creators—need additional means to help them excel in line with their ambitions, we applaud these measures. The government is continuing its tight public fund management while investing in education and health, pillars of our growth. The government is also focussing on the "4Es": the entrepreneurship, employment, exploiting natural resources and education. However, this budget could have lightened the tax burden of our SMEs, since only those in the primary and manufacturing sector benefit from a 4% tax rate while all others are taxed at 8%, the highest rate in the country," stated President and CEO, Emilio B. Imbriglio.
Measures We Would Have Liked to See to Further Support Businesses and Investments!
To further stimulate the Quebec economy, Raymond Chabot Grant Thornton, among others, incites the Quebec government to abolish corporate income tax on the first $500,000 of an SME's taxable income, provided that the savings be invested in productivity, employment and innovation in a manner that avoids abuse. Investments could be subject to accelerated CCA or to a tax credit.
"Another major issue for the firm is the preparation of succession plans. First, we salute the additional measures announced today for entrepreneurs, in particular those in the upcoming Action plan for entrepreneurship. The recent announcement of the Caisse de dépôt et placement du Québec's (CDPQ) creation of the Fonds Relève is excellent news to support successful business transfers," commented Éric Dufour, Regional Vice-President and National Business Transfer Leader.
While the CDPQ has elected primarily to invest in businesses by way of an equity investment, when they have a well-defined transition plan, the Quebec government should also be supporting businesses that, in fact, want to develop this type of formal business plan. "Since the key to successfully passing the torch and ensuring our businesses' longevity is a business transfer plan, the government would do well to create a fund to support businesses so they can get professional support in preparing a formal and comprehensive business transfer plan. Only 9% of business owners have a formal, written succession strategy, which is a cause for concern. Moreover, the government should relax the eligibility criteria for various business transfer measures," added Dufour.
In the case of business immigration, Raymond Chabot Grant Thornton urges the Quebec government to increase (or abolish the quota system) for the Immigrant Investor Program (IIP). "Until 2011, under the IIP, foreign business people could invest in Quebec in accordance with certain criteria, while benefitting from permanent resident status. Since the introduction of the quota system, processing costs have skyrocketed. This strategic and highly beneficial program for the Quebec economy should be supported, particularly with the growing need for public and private investments, notably in infrastructure," concluded Marc Audet, President and CEO of Auray Capital, a subsidiary of Raymond Chabot Grant Thornton.
Consult measures proposed to the Quebec government in the firm's pre-budget notice click here.
Audet
AURAY Capital and AURAY Sourcing |
Marc Audet is the President & CEO of AURAY Capital International. He is your expert in Residency and...


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WATCH: Filipino Movies That Turn 10 Years This 2017!
The year 2007 is home to some of the most unforgettable Pinoy movies that made us fall in love, laugh and cry.
So much has changed in the Philippine cinema in the last 10 years. Some have retained their tried-and-tested formula while others have gone on to think outside of the box. Time has flown so fast that we might not have noticed that these films are already a decade old.
Check out the local movies that came out on the big screen ten years ago!
1. One More Chance, starring John Lloyd Cruz and Bea Alonzo
Video Courtesy: ABS-CBN Star Cinema
2. You Got Me, starring Toni Gonzaga and Sam Milby
3. I've Fallen For You, starring Kim Chiu and Gerald Anderson
4. Ang Cute Ng Ina Mo, starring Ai-Ai Delas Alas
5. My Kuya's Wedding, starring Ryan Agoncillo and Maja Salvador
Video Courtesy: Regal Entertainment, Inc.
6. Agent X44, starring Vhong Navarro
7. Paano Kita Iibigin, starring Piolo Pascual and Regine Velasquez
8. The Promise, starring Angel Locsin and Richard Gutierrez
9. Sakal, Sakali, Saklolo, starring Judy Ann Santos and Ryan Agoncillo
10. Apat Dapat Dapat Apat, starring Rufa Mae Quinto, Candy Pangilinan, Eugene Domingo and Pokwang
Video Courtesy: Viva Ent
Were you able to watch these films back in the day? Let us know in the comments!


text:
Hydrornis oatesi е вид птица от семейство Pittidae.

Разпространение и местообитание
Видът е разпространен в Китай, Лаос, Малайзия, Мианмар, Тайланд и Виетнам. Естествените му местообитания са субтропични или тропични влажни низини и планински и бамбукови гори. Обикновено се среща на надморска височина от над 800 m.

Източници

Питови
Фауна на Индо-Малайската област

redpajama_set_name: RedPajamaWikipedia


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