s2fieldsofstudy: Business Psychology

text:
PERCEPTIONS OF CULTURAL INCLUSION: A STUDY OF THE WORK-FAMILY INTERFACE FOR MAORI EMPLOYEES WITHIN NEW ZEALAND

Creating a work environment that is inclusive towards diverse cultures is beneficial to employees and organizations. The present study tests the effects of Perceived Cultural Inclusion (PCI) on work-family related outcomes on a sample of Maori N=349 (the indigenous people of New Zealand) employees. Because of the strong links between inclusion and Perceived Organizational Support (POS), PCI has been based on POS; and a direct effects model revealed that PCI was directly related to POS and work-family related outcomes (conflict, enrichment and balance). However, testing different models (direct effects only, partial mediation and full mediation) indicated that a partial mediation model was superior. PCI’s ability to predict POS and also to predict enrichment in both directions (work-family and family-work), and POS’s ability to predict work-family conflict and enrichment, highlights the importance of PCI to enrichment. In addition, the final model revealed that work-family conflict and both directions of enrichment predicted work-life balance. These results emphasize the importance of cultural inclusion to the work-family interface for Maori employees, and the interpretations of, and implications for these findings are discussed in this paper.

Introduction
Work-family policies and practices need to be consistent with, and accommodating of, the core values of employees within an organization (Kossek, 2005;Ramamoorthy & Carroll, 1998). Kossek (2005) stated that "work is defined and experienced very differently across societies; organizational and societal structures construct what individuals and families perceive as possible for work-life integration" (p. 18). This statement is particularly important for Maori, as they are a collectivistic minority group working within a predominately individualistic European environment (Harrington & Liu, 2002). The cultural differences between these two groups see Maori experience significantly different work-family dynamics and demands compared with the majority of New Zealanders (Haar, Roche, & Taylor, 2011). Given that HR practices (either formal or informal) are often focused toward the majority, we expect that the creation of an organizational climate, which is inclusive of the cultural needs of individuals, will be beneficial to the work-family needs of Maori. This expectation is based on the developing area of inclusion, which suggests that workplaces that are more inclusive of a diverse range of employees tend to benefit both employees and their organizations (Shore et al., 2011).
Consideration of inclusion is important given the high levels of diversity within international (Mor Barak, 2011) and New Zealand workplaces (Houkamau & Boxall, 2011). However, traditional diversity management techniques (e.g., Cox, 1991;Cox & Blake, 1991) have failed to unleash the benefits of diverse workplaces (Pless & Maak, 2004). This might be why the inclusion discourse is expected to gain popularity over the diversity discourse (Oswick & Noon, 2012). However, despite over a decade of research and theory development on inclusion, there is still a lack of empirical testing on the subject (Roberson, 2006) and it is still considered to be a new concept (Shore et al., 2011).
Although there are a limited number of papers revealing inclusion as a promising predictor of job and well-being outcomes (e.g., Findler, Wind, & Mor Barak, 2007), to the best of our knowledge no empirical study has linked inclusion to work-family related outcomes. The present study seeks to measure how PCI influences work-family related outcomes. The following review of literature discusses work-family related theory and how this relates to Maori. We then build an argument as to how PCI is likely to benefit the work-family interface for Maori employees.

Previous Research
The Work-Family Interface It has been nearly 30 years since the introduction of Greenhaus and Beutell's (1985) seminal work on workfamily conflict (WFC). Significant factors, such as dual earner partnerships, single parents, increasing working demands and shifting of employee values, have made it more difficult for employees to participate in both work and family roles effectively (Eby, Casper, Lockwood, Bordeaux, & Brinley, 2005;Greenhaus & Powell, 2006). WFC was defined by (Greenhaus & Beutell, 1985) as "a form of interrole conflict in which the role pressures from the work and family domains are mutually incompatible in some respect" (p. 77). This incompatibility typically occurs via strain-based, time-based, or behavioral-base conflict. This bi-directional construct can occur from work to family domains (i.e., WFC) and also from family to work (i.e., family-work conflict (FWC) (Greenhaus & Beutell, 1985).
Some 20 years after the introduction of WFC, Greenhaus and Powell (2006) identified a significant gap in the literature and suggested that work and family could be allies. This was known as work-family enrichment (WFE), which was defined "as the extent to which experiences in one role improve the quality of life in the other role" (Greenhaus & Powell, 2006, p. 72). This enrichment can occur through either the instrumental path, where "skills, abilities, and values are applied effectively in another role" and/or (2) the affective path where "affect or emotion is carried over from one role to another" (Greenhaus & Powell, 2006, p. 74). Like conflict, enrichment is bi-directional, where enrichment can occur from work to family (WFE) and family to work (family-work enrichment (FWE)).
New Zealand is an excellent example of a 'cultural melting pot', as it is a diverse bi-cultural society (Harrington & Liu, 2002) boasting a diverse range of cultural backgrounds (Khawaja, Boddington, & Didham, 2007). New Zealand's indigenous people (Maori) comprise 14.6 percent of the population and New Zealand European comprise 67.7 percent of the population (Statistics New Zealand, 2007). Maori have significantly different cultural values and beliefs compared with Europeans (Lo & Houkamau, 2012;Tassell, Flett, & Gavala, 2010). Of considerable importance to this study is Maori's more collectivistic orientation towards the group and extended family networks (Harrington & Liu, 2002;Hook, 2007). Cultural differences like this are likely to have a significant impact on work family related outcomes generally (Powell, Francesco, & Ling, 2009), and we expect the same to hold true for Maori. The following section discusses conflict and enrichment and how they might relate to individual Maori employee and their families within a New Zealand context.

Maori and the Work-Family Interface
Culture is a significant factor to consider when focusing on the work-family interface, and we expect Maori culture to have a significant effect on the way Maori perform in both work and family roles. Durie (1997) discussed many examples of how Maori culture might affect Maori family demands which will have an effect on the individual and their work. For example, Maori place a high emphasis on whānau and whanaungatanga (Durie, 1997) and interacting with their relatively large social network. Europeans are in general more individualistic focusing on self and immediate family (Harrington & Liu, 2002;Hook, 2007). Although there are many definitions of whānau, it is generally defines as a cohesive unit working towards similar goals, a support group / team (Durie, 2003) connected through group solidarity, warm interpersonal interactions and cheerful cooperation (Bishop, 2005;Durie, 1997). "Whanaungatanga is the process by which whānau ties and responsibilities are strengthened" (p. 2), where all genders and generations work alongside each other (Durie, 1997). Durie (2003) stressed the importance of whānau for the development of Maori well-being. Whānau and whanaungatanga, along with other cultural beliefs and practices (tikanga), are likely to affect the work-family interface significantly compared with the European majority. For example, maintaining a strong relationship with extended family is likely to be challenging yet rewarding for Maori.
Recent research by the Ministry of Social Development (2008) revealed that Maori reported the lowest levels of work-life balance. The urbanization of Maori, longer working hours and wider social connections (Durie, 1997;Tassell et al., 2010) might make it difficult for Maori to fully participate with their families in the way they want to (Haar et al., 2011). Furthermore, practice of whanaungatanga might offer potential negatives or conflicts (e.g., time demands and sacrifice of personal freedom) to Maori (Durie, 1997). For example, work can make it difficult for employees to practice whanaungatanga and see whānau on a regular basis, creating considerable time and strain related demands for Maori. Alternatively, family demands may place strain and time demands on a Maori employee's ability to work. More recently, Haar et al. (2011) found that both WFC and whānau support had significant effects on turnover intentions.
Despite the occasionally conflicting nature of work and whānau, there are many positives with practicing whanaungatanga.
These positives include support/guidance in times of crisis, education and strengthening of cultural identity (Durie, 1997) which would likely benefit FWE. Organizations that demonstrate an understand of cultural differences by respecting and supporting the cultural needs of Maori, may, in turn, create practices and policies that have a positive effect on both the work and family domains of Maori employees. For example, when an organization understands and supports the cultural needs of Maori it is easier for Maori to interact with family, which promotes WFE. This is known broadly as inclusion, which we consider to be a form of support, and is an antecedent of both WFC and WFE (Allen, 2001;Tang et al., 2012). Support from the organizational level has been shown to improve work-family related outcomes (Aryee et al., in press). We focus specifically on cultural inclusion, which is related to supporting and including one's cultural needs (detailed below). Overall, the benefits that whānau offer Maori are likely to outweigh the negatives if workplaces can be more supportive towards the culture needs of their employees. The following section discusses how inclusion of one's culture in the workplace could benefit such work-family outcomes.

Inclusion
Inclusion is an evolved form of diversity management (Shore et al., 2011), which is set to overtake traditional diversity management (Oswick & Noon, 2012). Shore (2011) suggested that although diversity made people feel a sense of belongingness, they failed to acknowledge their uniqueness. Although there are many definitions of inclusion, a review of the literature led Shore et al. (2011) to state that inclusion should be defined "as the degree to which an employee perceives that he or she is an esteemed member of the work group through experiencing treatment that satisfies his or her needs for belongingness and uniqueness" (p. 1265). Although inclusion has existed for over a decade, it is still considered to be a new concept (Shore et al., 2011) for which there have been limited empirical studies (Pless & Maak, 2004;Roberson, 2006).
The few studies that have empirically tested inclusion have shown it to predict job outcomes, such as organizational citizenship behaviors, job satisfaction, turnover intentions, and well-being (Acquavita, Pittman, Gibbons, & Castellanos-Brown, 2009;Findler et al., 2007;Mor Barak, Findler, & Wind, 2001;Mor Barak & Levin, 2002;Nishii & Mayer, 2009). Although several papers discuss inclusion and elements of the work-family interface in a very limited way (e.g., Bilimoria, Joy, & Liang, 2008;Lirio, Lee, Williams, Haugen, & Kossek, 2008;Ryan & Kossek, 2008), to the best of our knowledge there are no empirical studies that link workfamily related outcomes with inclusion. Ryan and Kossek (2008) suggested that work-family policies and support could predict perceptions of inclusion, which would, in turn, predict employee outcomes. While these hypotheses were not validated empirically, they do support the idea that support at a basic level promotes feelings of inclusion, which can then affect outcomes (Ryan & Kossek, 2008).
The present study focuses specifically on perceptions of cultural inclusion (PCI). We suggest that PCI relates to how employees perceive organizations' support for their cultural backgrounds, values, and beliefs. Organizations can demonstrate their support by promoting difference and showing understanding, tolerance, and support for individuals' cultural needs and obligations. We have based PCI on POS because much of the inclusion literature discusses POS and aspects of social exchange theory (Pless & Maak, 2004;Shore et al., 2011;Stamper & Masterson, 2002;Wayne, Shore, Bommer, & Tetrick, 2002). POS is based on social exchange theory and it relates to the way employees perceive that "the organization values their contributions and cares about their well-being" (Eisenberger, Huntington, Hutchison, & Sowa, 1986, p. 501). Social exchange theory and POS have also been the basis for many measurement models. For example, Allen (2001) based family-supportive organization perceptions (FSOP) on POS which in turn predicted WFC. Furthermore, a study by Thompson et al. (1999) developed a measure of work-family culture that related to "the shared assumptions, beliefs, and values regarding the extent to which an organization supports and values the integration of employees' work and family lives" (p. 392). They found that when an environment was more supportive, employees were more likely to utilize work-family benefits, which, in turn, predicted WFC.
Organizational support has also been found to predict WFE (Tang et al., 2012). We expect that if an organization respects its employees' cultural values and actively works with employees to meet their cultural needs, a sense of cultural inclusion will be instilled in the employees. We expect this to have a beneficial effect on work-family related outcomes as employees would be able to engage more readily with family. As such, we suggest PCI, like POS, will play a beneficial role on the work-family interface, as it is negatively related to conflict and positively related to enrichment and worklife balance. This leads to our first set of hypotheses.
Because PCI is based on POS, both measures are likely to have a strong positive relationship with one another. Thus, employees who have high levels of PCI are likely to have high levels of POS, and the two measures are likely to correlate highly. High correlations between POS and other constructs, such as job satisfaction, have been evidenced within earlier POS research (e.g., Eisenberger, Cummings, Armeli, & Lynch, 1997). However, measures that have used POS as a basis (e.g., FSOP, Allen, 2001) typically have not tested their new measure in relation to POS. Consequently, we test the relationship between PCI and POS to alleviate this short fall.

Hypothesis 6: PCI will be positively related to POS.
We also test whether PCI has a direct or indirect effect on work-family related outcomes when POS is included. Given the potential for PCI and POS to be linked, the potential mediating effects of POS are also tested. Wayne et al., (2002) who showed that POS mediated the influence of inclusion towards commitment and organizational citizenship behaviors. Similarly, Stamper and Masterson (2002) revealed that perceived insider status mediated inclusion towards citizen and deviant behaviors. Based on Wayne et al's (2002) and Stamper and Masterson's (2002) research, we offer the next hypothesis.

Hypothesis 7: POS will mediate the influence of PCI towards outcomes.
Finally, based on the idea that reducing conflict and increasing enrichment is likely to have a positive effect on work-life balance (Haar, in press). we expect workfamily conflict to be detrimental and work-family enrichment beneficial towards work-life balance. This leads to our last hypotheses.
Hypotheses: (8) work-family conflict will be negatively related to work-life balance, while (9) work-family enrichment will be positively related.

Method
This Purposeful sampling (Coyne, 1997) was used by research assistants familiar with Maori protocol to attract Maori participants. Physical surveys or a link to an online survey were distributed to a wide range of organizations and networks. Online surveys have been utilized before by inclusion researchers (e.g., Acquavita et al., 2009). In total, over 200 organizations throughout New Zealand were approached and the study and its requirements were explained to them. Over 700 surveys were hand distributed or emailed to Maori participants. Survey one contained measures: POS, PCI and demographic variables. Survey two contained the dependent variables: WFC, FWC, WFE, FWE and work-life balance. The study achieved a response rate of 49.8%: 349 out of the 700 surveys distributed were completed and returned. Responses from the physical and online surveys were combined, and with no significant differences were found between the two. On average, respondents were married (66%), 38.9 years old (SD=11.9), worked 40.1 hours per week (SD=9.7 hours), and had a tenure of 5.3 years (SD=6.4 years). The public sector was overrepresented in our sample: 70% were employed in the public sector, 22% in the private sector and 8% in not-for-profit. Respondents were highly educated with 18% holding high school qualifications, 14% technical college qualifications, 44% university degrees and 24% postgraduate qualifications.

Measures
POS was measured with a 6-item measure by Eisenberger et al. (1986) coded 1=strongly disagree to 5=strongly agree. Questions followed the stem "My organization…" and a sample item was "Takes pride in my accomplishments at work" (α= .84). Eisenberger et al. (1986), coded 1=strongly disagree to 5=strongly agree. Questions followed the stem "My organization…" and the items were: "Considers my cultural goals and values", "Takes pride in my cultural accomplishments at work", "Really cares about my cultural well-being" (α= .95). The psychometric properties of this measure have been validated and established in previous research (see , Haar & Brougham, 2010;Haar & Brougham, 2011a, 2011b.

PCI was measured with 7-items based on
Strain Based WFC and FWC were measured by Carlson, Kacmar and Williams (2000) measures, coded 1=strongly disagree to 5=strongly agree. A sample item for WFC was "I am often so emotionally drained when I get home from work that it prevents me from contributing to my family" (α= .87) and a FWC sample item was "Because I am often stressed from family responsibilities, I have a hard time concentrating on my work" (α= .90).
Affective WFE and FWE were measured with the 3-items by Carlson, Kacmar, Wayne and Grzywacz (2006), coded 1=strongly disagree to 5=strongly agree. WFE questions followed the stem "my involvement in my work…" and a sample item was "Makes me feel happy and this helps me be a better family member" (α= .94). FWE questions followed the stem "my involvement in my family…" and a sample item was "Puts me in a good mood and this helps me be a better worker" (α= .95).
Work life balance was measured with 3-items by Haar (in press) coded 1=strongly disagree to 5=strongly agree. A sample item was "I am satisfied with my work-life balance, enjoying both roles" (α= .88).

Measurement Models
Confirmatory factor analysis (CFA) confirmed that PCI and POS were separate dimensions. The comparative fit index (CFI >.95), (2) root-mean-square error of approximation (RMSEA < .08), and (3) standardized root mean residual (SRMR <.10) were utilised to show the goodness-of-fit for the structural model consistent with Williams, Vandenberg and Edwards (2009). The hypothesized measurement model and three alternative models are shown in Table 1.
The CFA was re-analyzed, which revealed a combination of alternative models. We followed Hair, Black, Babin and Anderson's (2010) instructions, and tested comparison models, which showed that the alternative models were all significantly different (all p< .001) and a poorer fit than the hypothesized model.

Analysis
Hypotheses 1-9 were tested using structural equation modeling in AMOS to assess the direct and potential meditational effects of the study variables.

Results
Descriptive statistics for the study variables are shown in Table 2. Table 2 shows that POS and PCI are highly correlated (r=.68, p< .01), but still below the threshold of concept redundancy, which occurs at r >.75 (Morrow, 1983). In addition, the CFA in structural equation modeling revealed that PCI and POS were distinct. Overall, PCI and POS were significantly related to all work-family related outcomes (all p< .01).

Structural Model
A number of alternative structural models were tested, to determine the optimal model based on the data. The number of alternative models tested included: (1). A direct effects only model, with cultural POS predicting all work-family related outcomes, including POS.
(2). A partial mediation model, with PCI and POS predicting all work-family related outcomes.
(3). A full mediation model, with PCI predicting POS, and, in turn, POS predicting all work-family related outcomes.

Discussion
This paper, using a sample of Maori employees, highlights the importance of inclusion to the work-family interface. We found that PCI had significant direct and indirect effects on conflict, enrichment, work-life balance and POS. Our findings are promising for both the prediction of work-family outcomes and, more importantly, for the inclusion literature, which has limited empirical papers (Roberson, 2006;Shore et al., 2011). Figure 1 shows and supports our first set of hypotheses: that PCI has direct effects on WFC, FWC, WFE, FWE, work-life balance, and POS. This finding aligns with the limited empirical inclusion literature, which found that inclusion predicted job related (e.g., job satisfaction) and life related (e.g., well-being) outcomes (Findler et al., 2007). Furthermore, we view PCI as a form of cultural support, and increasing levels of support can reduce conflict (Allen, 2001) and increase enrichment (Tang et al., 2012). However, the results from SEM revealed that our hypothesized partial mediation model (figure 2) was superior to the direct effects model (figure 1).
Testing POS within a model that uses a measure of PCI, which using POS as the foundation, was a methodological improvement on existing approaches, such as FSOP (Allen, 2000). We expected POS to have a significant effect on our overall model, based on similar effects shown in the literature (e.g., Stamper & Masterson, 2002;Wayne et al., 2002). Although Stamper and Masterson (2002) and Wayne et al (2002) tested POS as a mediator within the inclusion literature, our findings offer a unique picture within the inclusion literature, as we measured something different from these authors. The following sections discuss the partial mediation model effects in more detail. Figure 2 shows that PCI still has direct effects on WFE and few. PCI has a direct positive influence on workfamily enrichment, as well as an indirectly effect, through enhancing POS, which, in turn, predicts three of the four work-family variables. This finding shows the importance of PCI over POS for work-family enrichment. Those organizations that support culture also support the dynamic and complex family needs of Maori. Furthermore, our findings add a deeper insight into the predictors of enrichment, which are still under explored (Wayne et al., 2006). In addition, PCI, working through POS, reduced WFC. This shows the importance of POS for the work-family interface, which is consistent with (Aryee et al., in press). Looking at Figure 2 as a whole, we can see that when conflict is reduced and enrichment is heightened which both benefit work-life balance. This aligns with Haar (in press). We expect POS to be highly beneficial to Maori given their complex family/whānau structure, which can see them experiencing difficulties with balancing their work and family obligations (Durie, 1997(Durie, , 2003. However, the benefits of whānau might not be accessible unless Maori employees are able to fully fulfill their duties to, and spend 'adequate' time with, their families. As such, PCI might signal to Maori employees that it is acceptable for them participate in cultural events and support their families. This will in turn enrich the family lives of Maori employees, and their families will then reciprocate this support to them. This can only be achieved if organizations realize the importance of inclusion and the support of culture in the workplace.

Implications for Managers
Our findings reflect the importance that managers supporting and including employees' cultural values has on the employees' work and family lives.. Research has shown that being from a different culture from one's manager often has a negative effect on levels of support (Foley, Linnehan, Greenhaus, & Weer, 2006) and inclusion (Pelled, Ledford, & Mohrman, 1999) employees receive, and can also affect job outcomes (Greenhaus, Parasuraman, & Wormley, 1990), life satisfaction and overall well-being (Verkuyten, 2008). Foley et al. (2006) found that "supervisors provided more family support to subordinates who were similar in either gender or race than to those subordinates who were dissimilar" (p. 420). Masuda et al. (2011) highlighted the importance of supporting collectivistic employees by providing flexible work arrangements and other forms of support to "help them build social ties and spend time with their family" (p. 24).
Training managers to understand cultural differences is highly important. It has been suggested that informal support, such as managers getting to know about employees' cultures and cultural needs, might be the best way to support (Wayne et al., 2006) and include culture in the workplace. This type of support shows that the organization/supervisor is acknowledging the importance of culture in the workplace. Taking steps to support the cultural needs of individual employees, such as flex-time, which may enable employees to meet their cultural obligations, will increase PCI, It is also important for managers to view cultures as dynamic, and consider employees, and their cultural needs, on a case by case basis.

Limitations and Future Research
Our research has several limitations. Firstly, data was self-reported and not collected longitudinally, which increased the risk of common method variance. However, structural equation modeling has the potential to mitigate this slightly. Secondly, data only included Maori employees, and future researchers might want to include a diverse range of employees, such as New Zealand's European, Pacifika and Asian populations (Khawaja et al., 2007). In addition, the Maori within the sample were highly educated, with a large proportion working within the public sector; this is not representative of Maori as a whole. Future studies may need to consider attracting employees from more diverse educational backgrounds as well as more private sector employees. Despite these limitations, we still interpret the findings as significant. Overall, supporting and including the cultural needs of Maori employees is likely to have a highly beneficial effect on work-family related outcomes and work-life balance. These benefits will, in turn, benefit both employees and organizations. transient


Energy Efficient Spectrum Sensing and Handoff Strategies in Cognitive Radio Networks

The limited spectrum resources and dramatic growth of high data rate communications have motivated opportunistic spectrum access using the promising concept of cognitive radio networks. Although this concept has emerged primarily to enhance spectrum utilization, the importance of energy consumption poses new challenges, because energy efficiency and communication performance can be at odds. In this paper, the existing approaches to energy efficiency spectrum sensing and handoff are classified. The tradeoff between energy consumption and throughput is established as function of the numerous design parameters of cognitive radio networks, both in the case of local and of cooperative spectrum sensing. It is argued that a number of important aspects still needs to be researched, such as fairness, dynamic behavior, reactive and proactive schemes for energy efficiency.

I. INTRODUCTION
The great popularity of devices such as smartphones, tablets, and laptops all wirelessly connected to Internet as well as the recent development of the Smart Grids paradigm has caused an exponential traffic increase over networks, which is exacerbating the overall global energy consumption. Since wireless communications account for half the total energy consumption in ICT, energy efficiency is becoming a figure of merit for all wireless networks, which is now being researched in the area of green communications. Energy efficiency is also motivated by prolonging the battery lifetime of wireless devices, especially considering the increasing data rates. Therefore, there is a consensus in academia, industry, policy makers, and standardization bodies on the need of energy efficient wireless communications [1].
In traditional wireless networks, energy efficiency considers the utilization of the available spectrum space, through appropriate protocol design and resource allocation. This is becoming impellent also in the emerging spectrum sharing techniques based on opportunistic access. Such techniques have been proposed to alleviate the spectrum shortage problem caused by the rapidly growing spectrum requirement of emerging wireless networks. The cognitive radio network (CRN) is a key concept of opportunistic spectrum sharing, where secondary users (SUs), with cognitive capabilities, search for spectrum resources not used by the licensed ones, the primary users (PUs), and select their transmission and channel access parameters such that PU transmissions are not disturbed.
There has been a substantial effort in investigating CRNs with focus on throughput and interference management and optimization. Given that PUs' high data rates may cause high interferences to the secondary users, the energy consumption is even worsened in CRNs due to repeated attempts in channel sensing by the SU and spectrum handoffs. It fact, channel sensing is as much energy consuming as transmission. Therefore, in spectrum sharing CRNs, it is essential to address the joint optimization of energy resources spent for exploring the spectrum access opportunities and for transmission.
However, energy consumption cannot be considered as the only metric of figure in wireless networks in general and in CRNs in particular. Spectral efficiency is essential to achieve adequate quality of services. This is particularly important for spectrum handoff, which is the fundamental mechanism in a CRN, by which dynamic channel access and interference avoidance mechanisms allow the coexistence between primary and secondary systems. This motivates the investigation of the tradeoff between the spectrum utilization and the energy spent for transmission and reception in spectrum sensing and handoff.
In this paper, we investigate such a tradeoff and we propose a new figure of merit, the energy efficiency, defined as bit per Joule, namely the number of bits that can be transmitted per unit of energy. We review the spectrum sensing and spectrum handoff strategies proposed in the literature and evaluate the design parameters of CRNs affecting the energy efficiency metric.

II. FUNDAMENTALS
In this section, we summarize the essential aspects of CRNs, spectrum sensing, and handoff.

A. Cognitive Radio Network
Under opportunistic spectrum sharing, two or more networks share some part of the spectrum. The primary network owns the spectrum, and has performance guarantees. The secondary network(s) can access the spectrum if no significant degradation on the primary communication is caused. To find the opportunities of spectrum access, the secondary network learns the wireless environment and adapts to it. The learning is often based on sensing the spectrum, while the adaptation includes the tuning of the parameters of the protocols [2]. As shown on Fig. 1, to find the transmission opportunities appropriately and to protect the PUs from interference, the SUs need sensing the channels regularly using local or cooperative sensing, and start a spectrum handoff procedure, if the current channel is busy.

B. Spectrum Sensing
The most important parameters impacting the performance of spectrum sensing are the time available to sense the transmission channels, the strength of the primary signals, and the a-priory information on the primary technology. The noise and the channel impairments such as shadowing and fading lead to decision errors, quantified in terms of false alarm and miss detection probabilities. A false alarm occurs when a free channel is mistakenly sensed busy, while a miss detection happens whenever an occupied channel is sensed free. To improve the sensing performance, cooperative sensing is introduced, where a group of SUs together decide about the availability of the channel.

C. Multichannel Spectrum Sensing
As typically there is more than one channel available for secondary access, spectrum sensing is divided into wideband and narrowband. Under wideband spectrum sensing, an SU senses multiple channels simultaneously. Although this may allow short sensing duration, it requires complex hardware implementation, high power and computational complexity. Under narrowband sensing, only one channel can be sensed at a time, leading to easier implementation, lower power consumption, and less computational complexity. This is the reason why there has been great interest about such a sensing. By the narrowband sensing method, SU tries to find a free channel by sensing new channel sequentially according to a predetermined set of channels called sensing order. This has the potential drawback of a longer sensing duration.

D. Spectrum Handoff Strategy
The spectrum handoff strategy needs to answer the questions: When should an SU vacate the current channel? Should the SU wait on this channel or start finding another one? Which channels should be sensed and in what order?
The strategies can be categorized as reactive, proactive, or hybrid (a combination of the first two). Under reactive spectrum handoff, the SUs initiate searching among the channels to find transmission opportunities and pursue their unfinished transmissions, whenever handoff is required. Although a larger delay becomes inevitable, reactive handoff leads to up-to-date channel status estimation. Proactive schemes exploit the long term traffic statistics of the channels to establish a proper policy for future handoffs. As handoff can be carefully planned beforehand, very short handoff latency can be achieved. However, these schemes usually impose two radios on the SUs, one for transmission and another for channel observation. Also, as the sensing order is determined in advance, the predetermined target channels may not be longer available when handoff is triggered. Most of the proposed solutions follow a hybrid strategy combining the advantages of the two basic schemes.

E. Energy Efficiency
Energy efficiency is generally defined as information bits transmitted per unit of energy, called "bit-per-Joule" [1]. This definition calls for a cross-layer optimization across all the protocol layers and networking functions, such as transmission and spectrum handoff in our case. The energy consumed in the secondary network consists of consumption for i) data transmission, ii) spectrum sensing, iii) the communication protocol, including information exchange for cooperative spectrum sensing and for organizing the secondary transmissions. Additionally, the circuit power consumed by the transmitter and the receiver, and the power consumed for tuning to the channel to be sensed can give a substantial part of the energy consumption [3], [4].
By the Shannon's capacity formula, it is known that in dedicated spectrum, linearly increased transmission power leads to a logarithmic increase of the achievable transmission rate, and consequently the energy efficiency, as the ratio of the rate and the invested energy has an optimum value [1]. The power consumption and transmission rate tradeoff changes in cognitive radio networks due to the additional power consumption and also to the time needed for spectrum sensing and handoff.
In the following two sections, we analyze the energy efficiency of local and cooperative spectrum sensing, with focus on energy consumption. The key design parameters that affect the energy of these strategies are characterized.

III. LOCAL SPECTRUM SENSING AND HANDOFF STRATEGIES
Local spectrum sensing can provide adequate sensing performance if the primary signals are sufficiently strong. Research on optimizing the spectrum handoff process under local sensing typically focuses on the SU and PU performance, in terms of maximizing the average throughput or similarly minimizing the secondary delivery time, for a single SU, or for a large secondary network. Below, we discuss how the mutual relationship between the key design parameters in the spectrum handoff process affect the energy.

A. Channel Sensing Time
The channel sensing time usually has a minimum value, which is required to achieve acceptable false alarm and miss detection probabilities. However, this minimum value does not lead to optimal energy efficiency. In fact, it affects the energy efficiency of the SU in a complex way. Increasing sensing time increases the energy consumption of that single sensing process, and decreases the time left for secondary transmission. However, as it also increases the probability of correctly detecting an idle channel, it leads to lower number of spectrum handoff, and consequently less energy consumption for sensing, and increased transmission time.
The tradeoff between sensing time, achievable throughput, and energy efficiency for a sequential spectrum sensing is evaluated in [5]. As shown on Fig. 2(a), the energy efficiency first increases with the sensing time, due to a more accurate spectrum sensing, and reaches a maximum value. After the point, the energy efficiency falls, as the increased sensing performance can not compensate for the increased energy consumption and for the decreased time available for transmission. Comparing the optimal sensing time values for energy efficiency and for throughput maximization, we see that the optimal sensing time values for throughput or energy efficiency maximization are indeed different.

B. Maximum Number of Handoffs
The energy consumption of the spectrum handoff however, depends not only on the time used for sensing a single channel, but also on the number of channels that are sensed before an opportunity is discovered and the transmission starts.
Clearly, allowing the SU to discover more primary channels increases the chances of finding an empty channel and thus increases the throughput. However, as we see on Fig. 2(b), the energy consumption cost of this increase can be tremendous, once the system is close to the throughput limit. For instance, to increase the throughput above 0.85, only 3% transmission rate enhancement is achieved by 81% more energy consumption, which devastates the energy efficiency. Therefore, the maximum number of handoffs needs to be carefully optimized based on the SU throughput requirements and available energy resources.

C. Sensing Order
With narrowband sensing, the SU sequentially senses the channels until an idle one is found. The order of channels to be sensed affects the average throughput and the average energy consumption. As a result of an improper sensing order, an SU may sense several channels to find a transmission opportunity, and thereby may suffer from more energy consumption and less transmission time. In [3], the energy efficient spectrum handoff for a CRN with one SU investigates a hybrid handoff strategy, wherein the SU learns the channel occupancy and transmission channel quality statistics, and defines the sensing order accordingly. It is shown that optimizing only based on one of the above parameters can be highly sub-optimal, with a loss of energy efficiency up to 5 − 20% for the considered scenario. Further, [6] shows that significantly higher gain can be achieved by excluding from the sensing order the number with high occupancy probability or bad channel conditions.

D. Sensing and Channel Access
Finding an idle channel, however, does not guarantee successful transmission in secondary networks with several, uncoordinated users. Here, all SUs may sense the popular primary channels (like the ones with low load and good transmission quality), and then compete for accessing the same channel, while other channels might be idle too.
To solve this problem, [7] suggests to couple sensing and channel access control. They introduce a randomized scheme, were the SUs sense and then access the channels with some access probability. As Fig. 3(a) shows, the access probability has a significant effect on the energy efficiency, due to the tradeoff between throughput enhancement at more intentions to access the channels and the consequent contention level increment. The optimum probability depends on the size of the secondary and primary networks, and should be tuned accordingly. Further improvement can be achieved by randomizing the order of the channels to be sensed, as shown on Fig. 3(b). Thereby, the SUs are statistically distributed among all primary channels, and each SU can achieve substantially higher energy efficiency due to much lower contention level in the target primary channel.
Finally, [8] shows that in large CRNs, optimizing the access strategy jointly with sensing order, based also on the channel availability statistics, may be efficient. However, as high contention on popular channels needs to be avoided, the decision schemes become more complex.

E. Waiting or Handoff
Once a primary user returns to its channel, the SU may decide to wait until the channel becomes idle again, or invest some time and energy to start the handoff procedure and migrate to an idle channel. As [4] suggests, the decision should be based on the throughput and delay requirements of the SU. Unless the secondary quality requirements are very strict, optimizing the probability of waiting instead of migrating can decrease the energy consumption of the SU up to 20%. Clearly, the gain decreases as the throughput or delay requirements get strict, and the SU can not afford to simply wait for the new transmission opportunity in the current channel.
Given that a waiting SU needs to discover that the channel become idle again, [9] investigates how often the channel should be sensed. More frequent sensing allows the SU to start to transmit with lower discovery delay and thus achieve higher throughput, at the same time, it requires higher energy consumption. [9] shows that sensing does not need to be periodic, and an adaptive sensing interval, based on some knowledge on the distribution of the PU busy time can halv the discovery delay, while keeping the same sensing energy budget as periodic sensing.

IV. COOPERATIVE SPECTRUM SENSING
In the case of strong primary signals, local sensing may be sufficient to ensure good performance. However, the cooperation of several spectrum sensing nodes, that is, secondary users in the area, is needed if the primary signal is weak, or if the radio propagation environment is harsh. There, the spatial diversity among the secondary users mitigates the effect of link impairments due to fading and shadowing phenomena, and the secondary users, together, can discover spectrum access opportunities.
The design factors discussed for local spectrum sensing can be further optimized in cooperative sensing scenarios, considering the wireless environment of the individual users. However, there are now additional open questions affecting the energy efficiency of the cooperating users, as we see in the following subsections.

A. Sensing Resource Allocation
Under cooperative sensing the sensing resource is not only the sensing time but also the set of secondary users cooperating to discover a spectrum access opportunity. Considering a single channel, increasing the number of cooperating users increases linearly the energy cost for detecting channel availability. If several channels need to be sensed, increasing the number of cooperating users means that each user needs to sense more channels. This decreases the local sensing performance, or the time available for secondary transmission, as discussed already for local sensing. Therefore, the careful assignment of sensing duties to the existing secondary users becomes a key design factor of CRNs.
Accordingly, [10] proposes an iterative solution to involve SUs in sensing, until the desired overall sensing performance in terms of miss detection and false alarm probabilities are met for all channels. Clearly, the gain of such strategy increases together with the number of SUs in the area, and therefore is important in dense secondary networks.
As discovering spectrum opportunities requires effort from a set of cooperating users, the SUs now need to decide how large part of the spectrum space, dedicated for secondary access, they want to utilize. On one side, they may increase the number of channels to sense, so that there are more transmission opportunities to share. On the other side, this requires more sensing efforts from each SU. This shows that there has to be an optimal spectrum space to be sensed both for maximising per user throughput or energy efficiency. This optimal value is evaluated in [11], where it is shown that the (a): The energy cost per unit of secondary use throughput is minimized when the number of sensed bands is optimal. The optimum depends on the user density. (b): The energy cost is lowest at optimal cognitive network density, above which the sensing performance improvement does not compensate for the increased demand for cognitive capacity. Based on [11]. density of the secondary network is an important design factor ( Fig. 4(a)) to minimize the energy cost of each individual user. Such a cost is the sensing energy invested by the user to gain a unit of transmission opportunity. Moreover, the energy cost, even if minimized, strongly depends not only on the primary network quality requirements, but also on the density of the secondary network. Networks with moderate density are worst off, where the cooperative sensing performance is moderate, but the gained access opportunities has to be shared by a large set of nodes ( Fig. 4(b)).

B. Sensing Coordination
Since we have shown that the number of users participating in the cooperative sensing needs to be carefully selected, the remaining issue is what should be considered when selecting which particular users cooperate. Given that the main reason for cooperative sensing is to mitigate fading and shadowing, [12] suggests that users experiencing uncorrelated link attenuation should be selected. As shown in Fig. 5, the efficiency of this correlation aware policy depends on the spatial distribution of the SUs. It can decrease the number of users required to sense the primary channel, and, consequently, the sensing energy consumption by more than 50%, without affecting, or even improving, the sensing performance, hence, the achieved throughput by the secondary system.  6. Cooperative sensing energy cost for the optimal and sub-optimal user selection schemes [13]. In case of low SNR regime (b) the sensing energy dominates the total energy consumption due to an increased required sensing time, while it drops significantly for high SNR (b). The optimal (EE) SU selection scheme outperforms the heuristic solutions (SEM, where the optimization is performed considering only the sensing energy cost and REM, when sensing assignment is prioritized over users with already assigned sensing duties), and the TXT scheme where the sensing time is fixed, regardless of the received SNR at the SUs. The performance difference is more significant in the case of high SNR on the primary-secondary user links, when sensing itself costs little energy. Based on [13].
Another factor to consider when selecting the users to cooperate is the cost of collecting the results and making the cooperative decision.
Under cooperative sensing, the reporting of the local sensing results may require a significant amount of energy and even time, particularly if the links used for reporting requires a high transmission power or needs to be transmitted on a multihop path. Therefore, [13] compares different ways to select the cooperating secondary users, considering the local sensing performance, the sensing result transmission cost, or both, with an objective to minimize the total required sensing energy cost for maintaining an overall sensing quality. As shown on Fig. 6, the gain of joint optimization is significant, if the sensing itself does not require a significant amount of energy, due to good channel conditions or high primary transmission power.

C. Sensing Report Forwarding
As reporting the sensing results may have significant cost, [14] suggests that the SUs, even if included in the cooperative sensing, should choose not report the sensing ersult, if it might have little impact on the cooperative decision, while it would raise the overall reporting cost. They show, that if the primary channel utilization statistics are a-priori known to the SUs, then the individual users can have a good estimate on the usefulness of their sensing result. In this case censored reporting can drastically reduce the total sensing energy overhead by up to 40%, while maintaining the desired sensing performance.

D. Decision Combining
In addition to introducing a significant overhead to the overall energy cost of collaborative sensing, the reporting of the individual sensing results imposes a threat to the cooperative sensing performance due to the inherent lack of reliability of the wireless links used for reporting. Consequently, the cooperative decision has to mitigate the impact of the unreliability of the local sensing measurements and the errors introduced by the unreliable links.
To this end, [15] demonstrates that the quality of cooperative decision based on the individual decisions of the users (so called hard decision combining) can degrade by up to 60% if the reporting links are unreliable. Instead, using cooperative decision based on quantized raw sensing results (that is, soft decision combining) the overall sensing performance can be maintained at a relatively high level. The granularity of the reported sensing results need to be tuned carefully to tradeoff the energy and delay of reporting and the throughput gain due to correct spectrum decisions.

V. DISCUSSIONS
Spectrum handoff is a fundamental mechanism in CRNs, by which dynamic channel access and interference avoidance mechanisms allows for a smooth coexistence between primary and secondary systems. The optimization of the spectrum handoff aims primarily at increasing the efficiency of secondary access to underutilized primary channels, while maintaining a moderate energy overhead, so to enhance the lifetime of energy-constrained cognitive networks.
The energy efficiency of spectrum sensing depends heavily on the maximum number and the order of the primary channels sensed by a secondary user, the frequency of the spectrum sensing, and the selection of the per-channel sensing time. As shown in Section III-A, the sensing time must be carefully adjusted to not let the energy resources and transmission opportunities be wasted for a marginally higher accuracy of spectrum sensing. In multi-user scenarios, the selection of the number and the order of the channels to be sensed becomes even more important.
The problem of energy efficiency becomes more complicated in harsh wireless environments, where cooperative spectrum sensing is inevitable. As the energy cost of spectrum sensing is increased, maintaining the energy efficiency factor at moderate levels requires a careful optimization of the sensing resource allocation and coordination schemes. In Section IV-A, we show that the allocation of sensing tasks to secondary users with relatively good individual sensing and uncorrelated channel conditions substantially reduces the energy consumption. A careful reporting and combining of the individual sensing results increases the overall sensing efficiency. This leads to higher energy efficiency, when taking into account the individual cost of reporting the sensing measurements, thus, allocating sensing to users with low-cost reporting links.

VI. OPEN ISSUES
Although several interesting investigations exists in the area of energy efficient spectrum sensing and handoffs, many important issues are still open.
• Fairness in energy efficiency: In heterogeneous secondary networks, it is essential to optimize the design parameters so to guarantee a fair allocation of sensing duties to the SU, each having diverse throughput requirements and/or energy resource constraints. Thus, the focus of cooperative spectrum sensing should be shifted from globally maximizing sensing performance or optimizing resource allocation to energy efficiency of CRNs, while maintaining individual performance demands. • Dynamic energy efficient design: The current researches on energy efficiency are mainly based on static knowledge of the primary network model. Considering the dynamic spatio-temporal changes of the primary network, it is desirable to integrate dynamic learning techniques into an energy efficient spectrum sensing and handoff mechanisms. The energy efficiency of the adaptive sensing and handoff schemes should be analyzed taking into account the complexity, the overhead, but also the potential impact of the learning techniques in the overall efficiency of the CRN. • Proactive or reactive: Although there are several proactive and reactive spectrum sensing and handoff proposals, a general comparison in terms of energy efficiency is missed. Based on the available information from the primary and secondary networks constraints such as the QoS level that should be guaranteed for the primary network, it would be very interesting to have a general framework for comparing different types of spectrum sensing and handoff and answering the following question: When does proactive spectrum handoff outperform reactive one in term of energy efficiency? Clearly, the answer might be different for single, centralized, and distributed cognitive radio networks, and also it might vary for local and cooperative spectrum sensing.