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.
One of the essential characteristic of the standard DSC problem is that the information sources, also called encoders or informants, are not allowed to interact or cooperate with each other, for the purpose of compressing their information. There are two approaches to solve the DSC problem. First, allow the data-gathering node, also called decoder or recipient, and the informants to interact with each other. Second, do not allow the interaction between the recipient and informants. Starting with the seminal paper @cite_11 , almost all of the work in the area of DSC has followed the second approach. In the recent past, Pradhan and Ramchandran @cite_0 and later @cite_9 @cite_18 @cite_2 @cite_10 @cite_15 @cite_17 have provided various practical schemes to achieve the optimal performance using this approach. An interested reader can refer to the survey in @cite_12 for more information. However, only a little work @cite_13 @cite_7 , has been done towards solving DSC problem when the recipient and the informants are allowed to interact with each other. Also, this work stops well short of addressing the general multiple correlated informants - single recipient'' interactive communication problem, which we are concerned with addressing in this paper.
@cite_13 @cite_18 @cite_7 @cite_9 @cite_17 @cite_0 @cite_2 @cite_15 @cite_10 @cite_12 @cite_11
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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. > This paper proposes a practical coding scheme for the Slepian-Wolf problem of separate encoding of correlated sources. Finite-state machine (FSM) encoders, concatenated in parallel, are used at the transmit side and an iterative turbo decoder is applied at the receiver. Simulation results of system performance are presented for binary sources with different amounts of correlation. Obtained results show that the proposed technique outperforms by far both an equivalent uncoded system and a system coded with traditional (non-concatenated) FSM coding. We address the problem of compressing correlated distributed sources, i.e., correlated sources which are not co-located or which cannot cooperate to directly exploit their correlation. We consider the related problem of compressing a source which is correlated with another source that is available only at the decoder. This problem has been studied in the information theory literature under the name of the Slepian-Wolf (1973) source coding problem for the lossless coding case, and as "rate-distortion with side information" for the lossy coding case. We provide a constructive practical framework based on algebraic trellis codes dubbed as DIstributed Source Coding Using Syndromes (DISCUS), that can be applicable in a variety of settings. Simulation results are presented for source coding of independent and identically distributed (i.i.d.) Gaussian sources with side information available at the decoder in the form of a noisy version of the source to be coded. Our results reveal the promise of this approach: using trellis-based quantization and coset construction, the performance of the proposed approach is 2-5 dB from the Wyner-Ziv (1976) bound. In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding. We propose the use of punctured turbo codes for compression of correlated binary sources. Compression is achieved because of puncturing. The resulting performance is close to the theoretical limit provided by the Slepian-Wolf (1973) theorem. No information about the correlation between sources is required in the encoding process. The proposed source decoder utilizes iterative schemes, and performs well even when the correlation between the sources is not known in the decoder, since it can be estimated jointly with the iterative decoding process.