We are concerned with the problem of maximizing the worst-case lifetime of a data-gathering wireless sensor network consisting of a set of sensor nodes directly communicating with a base-station.We propose to solve this problem by modeling sensor node and base-station communication as the interactive communication between multiple correlated informants (sensor nodes) and a recipient (base-station). We provide practical and scalable interactive communication protocols for data gathering in sensor networks and demonstrate their efficiency compared to traditional approaches. In this paper, we first develop a formalism to address the problem of worst-case interactive communication between a set of multiple correlated informants and a recipient. We realize that there can be different objectives to achieve in such a communication scenario and compute the optimal number of messages and bits exchanged to realize these objectives. Then, we propose to adapt these results in the context of single-hop data-gathering sensor networks. Finally, based on this proposed formalism, we propose a clustering based communication protocol for large sensor networks and demonstrate its superiority over a traditional clustering protocol.
In @cite_13 , only the scenario in which two correlated informants communicate with a recipient is considered. It assumed that both the informants and recipient know the joint distribution of informants' data. Also, only the average of total number of bits exchanged is minimized. In @cite_25 , only two messages are allowed to be exchanged between the encoder and a decoder, which may not be optimal for the general communication problem. Conversely, it does not address the problem of computing the optimal number of messages exchanged between the encoder and a decoder as well as the optimal number of bits sent by the encoder and a decoder for the given objective of the communication in an interactive communication scenario. Also, unlike @cite_13 , this work concerns itself with the lossy compression at the encoders.
@cite_13 @cite_25
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X and Y are random variables. Person P sub x knows X, Person P sub y knows Y, and both know the joint probability distribution of the pair (X,Y). Using a predetermined protocol, they communicate over a binary error-free channel in order for P sub y to learn X. P sub x may or may not learn Y. It is determined how many information bits must be transmitted (by both persons) on the average. The results show that, when the arithmetic average number of bits is considered, there is no asymptotic advantage to P sub x knowing Y in advance and four messages are asymptotically optimum. By contrast, for the worst-case number of bits, communication can be significantly reduced if P sub x knows Y in advance, and it is not known whether a constant number of messages is asymptotically optimum. > Abstract We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm. 1 While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [C. Toh, IEEE Commun. Mag. June (2001) 138; R. Shah, J. Rabaey, Proc. IEEE WCNC, March 2002], in this paper, we propose an orthogonal approach to complement previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Applying the algorithm to testbed data resulted in energy savings of 10–65 for a multitude of sensor modalities.