Elsevier

Signal Processing

Volume 87, Issue 12, December 2007, Pages 2896-2910
Signal Processing

An energy-efficient adaptive DSC scheme for wireless sensor networks

https://doi.org/10.1016/j.sigpro.2007.05.006Get rights and content

Abstract

Distributed source coding (DSC) has been proven in theory that it can be used to compress correlated signals with or without loss. Recently this coding method has been used for the application of remote signal estimation in wireless sensor networks (WSN), where multiple sensor nodes compress their correlated observations without inter-node communications. Energy and bandwidth are therefore efficiently saved. Challenges remain, however, in the design of practical and adaptive DSC schemes for WSN. In this paper, we study the problem of a random-binning based DSC scheme for remote source estimation in WSN. We design a DSC scheme and analyze its performance on the estimated signal to distortion ratio (SDR), in which observation noise, quantization distortion, DSC decoding errors and network packet losses are all taken into account. With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption. We further propose a novel adaptive control mechanism for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption by adapting the source coding and transmission parameters to the network conditions. Simulations show the proposed DSC scheme and adaptive control mechanism can either save up to 31.6% energy consumption without decreasing the SDR or maximize the SDR with saving up to 9.4% energy consumption.

Introduction

Wireless sensor networks (WSN) are becoming an enabling technology for implementing a large number of parallel-distributed systems that interact with the physical world [1]. A wide range of applications of sensor networks has already been envisioned, including geographical surveillance, reconnaissance, machine monitoring, and battlefield operations. Large scale and special application environments of WSN require the sensor applications to be highly energy and bandwidth efficient. To improve energy and bandwidth efficiency of WSN, wide research has been carried out in diverse areas such as wireless communications, network routing, and transport.

Recently, the application of distributed source coding (DSC) to remote source locating and tracking in WSN has received much attention. DSC was proposed and studied in 1970s, to compress correlated sources without intercommunications between the sources [2], [3]. It has, therefore, the potential to save bandwidth and energy for the applications of source locating and tracking in WSN, where multiple sensors may detect a target and send correlated readings to an information sink [4], [5]. DSC compresses multiple correlated readings in a distributed way, while reducing the consumption of network bandwidth and transmission power as well as the packet collision in the wireless networks. Although many data aggregation techniques have been proposed to compress identical or partially identical sensor readings transmitted through a sensor node in the transmission paths, data aggregate requires the support of a structure, maintenance of which is costive for sensor networks [6]. Moreover, different readings may be transmitted to the sink over different paths. DSC becomes, therefore, an important alternative approach to data compression in WSN.

Prior work on DSC in WSN has focused on information-theoretic aspects such as achievable rate-distortion regions [7], [8], although some existing work has addressed the construction of distributed source codes. Aaron et al. proposed a distributed source code construction by using turbo codes [9], Pradhan et al. constructed a DSC framework based on algebraic trellis codes [10], but both turbo codes and trellis codes are computation-complex and hardware-costly for sensor nodes. Other authors considered an energy-efficient DSC scheme with Lagrangrian cost function, also based on trellis codes [11]. Xiong et al. provided a nice overview over the fundamentals of DSC and the current state of the art in [5], [12]. They also investigated a distributed source code constructed with the parity-check matrix of a (n,k) binary Hamming channel code. However, the high correlation among sensor readings is different from the correlation in Hamming channel code, which makes this code construction method not suitable for DSC in sensor networks. Sartipi et al. proposed a scheme for DSC using low-density parity-check (LDPC) codes [13]. The proposed decoding algorithm prevents error propagation and heavy damage to the sources. Chou et al. constructed a simple distributed source code to adaptively compress spatially and temporally correlated sensor readings [4]. However, as pointed out in [14], the proposed DSC scheme is not efficient in terms of coding efficiency. We proposed a practical and efficient random-binning based DSC scheme for the quantized sensor readings with full leverage of the correlations between the sensor readings [14]. The proposed DSC scheme achieves high coding efficiency while maintaining low signal distortion. Challenges remain, however, as the wireless bandwidth is changing from time to time and the total network bandwidth is shared by a large number of remote sensors [15]. It is therefore necessary and beneficial to adapt the DSC scheme to the changing network.

In this paper, we continue our previous work on the DSC scheme for WSN remote source estimation, in two aspects. Firstly, in addition to the performance analysis on estimated signal to distortion ratio (SDR), we quantitatively analyze the overall network energy consumption using the detailed power consumption model for wireless sensor communications proposed in [16]. Although energy consumption is critical for WSN, the performance of energy consumption for DSC schemes has not been analyzed in the existing literature. Secondly, we propose an adaptive control mechanism for the DSC scheme, to flexibly optimize either SDR or energy consumption performance by adapting the source coding and transmission parameters to the network conditions. Simulations are carried out to validate the efficiency of the proposed DSC scheme and adaptive control mechanism for both saving energy and improving the quality of source estimation.

The remainder of the paper is organized as follows. Section 2 formulates the problem and introduces a multi-mode power consumption model for wireless sensor communications. Section 3 proposes a basic transmission scheme. Its performances for source estimation quality and energy consumption are also analyzed. Section 4 introduces the random-binning based DSC scheme and proposes a novel adaptive DSC scheme. The SDR performance of the DSC scheme is analyzed and used to find the optimal coding and transmission parameters in the adaptive DSC scheme. In Section 5, we analyze the energy consumption performance of the DSC scheme. Energy consumption is used as a constraint in the SDR optimization process of the adaptive DSC scheme. Numerical results are presented and discussed in Section 6. Section 7 concludes the paper.

Section snippets

Problem assumption

In this paper, we assume that a sensor network is designed to monitor remote targets. For simplicity, we assume there is at most one active target at any time. Once the target becomes active, it is observed by the surrounding sensors in a sensing cell. The surrounding sensors generate observation signals and transmit the signals via other sensor nodes to a sink for further processing and taking corresponding actions. As the focus of this paper is on the distributed source coding and adaptive

Basic transmission scheme

As discussed in Section 2, in the basic transmission scheme the quantized sensor readings are directly transmitted to the sink without DSC encoding. Unlike the adaptive DSC scheme, which finds the optimal DSC and transmission parameters by interactions between the sink and the sensors, in the basic transmission scheme, we assume that the sink and the sensors always know the best quantization and transmission parameters in order to achieve the maximum SDR or minimum energy consumption without

Adaptive DSC scheme

In this section we briefly introduce the random-binning coding used in the DSC scheme, and then propose an adaptive DSC scheme in which the source coding and transmission parameters are adaptively controlled to achieve the best source estimation quality under the bandwidth and energy constraints.

Energy consumption of adaptive DSC scheme

In this section we analyze the energy consumption of the adaptive DSC scheme. Similar to the basic scheme, in the current design, only the energy consumed in the sensing cell and the nr relay nodes for the active and transient modes are considered. The energy consumed at the sink and at the sensors for sampling and quantization are not considered. Furthermore, the energy consumption for DSC encoding in the sensing cell is also neglected because of the low complexity of modulo operation [4]. The

Numerical results

Simulator is implemented in Matlab to evaluate the performance of the adaptive DSC scheme. For the reported results, each value is obtained by averaging over 30 simulations. For each simulation, the network is simulated for 30 min. As the basic transmission scheme is simple and robust, it is used as a benchmark to evaluate our proposed DSC scheme. The values of simulation parameters, used to obtain the numerical results, are summarized in Table 1.

In the simulations, the mean of the Gaussian

Conclusion

In this paper, we studied the problem of remote source estimation in WSN with application of a random-binning based DSC scheme. Compared to a basic transmission scheme, in the DSC scheme the correlated sensor readings are jointly encoded in a distributed way and sent to the sink for decoding and further processing. We modeled the signal to distortion ratio (SDR) performance of both the basic transmission scheme and the DSC scheme, as a function of observation noise, quantization, network

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