1. Field
The present application relates to systems and methods for telescopic data compression in sensor networks.
2. Background Art
The primary goal of sensor networks is to collect data of a physical phenomenon over a region. Sensor networks come in a wide variety, covering different geographical areas, using devices with different energy constraints, and implementing an assortment of applications. Some such networks have a large number of energy constrained sensors randomly distributed over a large area. The sensors forward data to nearby sensors until it reaches a sink, which is a collection point connected to the data processing center through a wired network. Such a network may be useful for measuring many different kinds of physical phenomena.
The field of most physical phenomena can be described by a smooth continuous signal. Even though spikes may occasionally occur, they are mostly confined to sporadic patches in a greater region of a smooth profile. This can be readily illustrated by simple examples, like the temperature distribution over a heated surface, the intensity of seismic vibrations decreasing gradually away from the epicenter, and in the case of a chemical leakage, the chemical concentration thinning out as the distance from the leak increases.
Many sensor network applications are characterized by an uneven distribution of information content, with useful information concentrated in isolated target regions. In such cases, data collection can be improved if target regions can be identified by sampling sparsely at a low energy cost, and then raising the sampling density progressively only in those regions of interests until the desired resolution is reached. There are several major technical challenges that inhibit the direct application of existing sampling-theoretic techniques to the compression of data in sensor networks, including: (i) random placement of sensors, (ii) noise in sensor measurements, (iii) limitations in the computational capacity of individual sensors, making complicated non-uniform sampling techniques unpractical, and (iv) the need that compression be done in a distributed manner. Thus there is a need for technique which overcomes these challenges.