Error resilient distributed estimation in wireless sensor networks

Proposed is an adaptive scalar quantization scheme. The main advantage is to improve coding performance by suppressing the sum of absolute quantization errors (SAQE) under half the maximal SAQE, assuming that the errors have uniform distribution. This fact was proven and demonstrated, integrated into H.264.

This paper proposes a distributed incremental quantization and estimation scheme by which each sensor can make a maximum likelihood estimation (MLE) of the unknown parameter based on its local observations and the quantized messages transmitted by the previous sensor. We derive the upper bound of the estimation mean squared error (MSE) of the proposed scheme, and propose the bit allocation algorithms, which minimize the total required bandwidth while ensuring a given MSE performance. Simulation results demonstrate that the proposed scheme requires much less bandwidth, but achieves around 40% MSE reduction compared with the distributed adaptive quantization and estimation scheme.

In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cramér–Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings.

In order to improve the accuracy and the convergence rate of distributed consensus under quantized communication, in the paper, based on adaptive quantization scheme, we propose the fast second-order distributed consensus algorithm. We analyze the convergence performance. The fast second-order distributed consensus with adaptive quantization achieves a consensus in a mean square sense, and the consensus is equal to the average of the initial states. Simultaneously, Simulations are done about the fast second-order distributed consensus based on adaptive quantization. Results show that the fast second-order distributed consensus algorithm based on adaptive quantization can reach an average consensus, and its convergence rate is higher than that of the first-order adaptive quantized distributed consensus algorithm, moreover, the mean square errors are smaller within the finite steps.

We consider the problem of distributed parameter estimation in wireless sensor networks (WSNs), where due to bandwidth/power constraints, each sensor quantizes its local observation into one bit of information that is sent to a fusion center (FC) to form a global estimate. Conventional fixed quantization (FQ) approaches, which utilize a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To overcome this difficulty, we propose a distributed adaptive quantization (AQ) approach, where, under the condition that sensors successively broadcast their quantized data, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors. Specifically, our strategy here is to let each sensor choose its quantization threshold as the maximum likelihood (ML) estimate of the unknown parameter based on the quantized data sent from other sensors. The Cramér-Rao bound (CRB) analysis shows that our proposed one-bit AQ approach asymptotically attains an estimation variance that is only π/2 times that of the clairvoyant sample-mean estimator using unquantized observations.

Range estimation is essential in many sensor network localization algorithms. Although wireless sensor systems usually have available received signal strength indication (RSSI) readings, this information has not been effectively used in the existing localization algorithms. In this paper, we present a sensor network localization algorithm based on a sorted RSSI quantization scheme that can improve the range estimation accuracy when distance information is not available. The range level used in the quantization process can be decided by each node using an adaptive quantization scheme. The new algorithm can be implemented in a distributed way and has significant improvement over existing range-free algorithms. The performance advantage for various sensor networks is shown with experimental results from our extensive simulation with a more realistic radio model.

We propose to extend some of the ideas of adaptive lossless compression to design an adaptive quantization algorithm. Noting that the performance of an arithmetic coder is as good as its estimation of the statistics of the input, we split our quantizer into two building blocks: model estimation and quantizer design. The main idea is that, as long as the model estimation manages to track down the changes in source statistics, a standard quantizer design technique which assumes that the source follows the estimated model can be used. As an example of this type of design we study an adaptive scalar quantization scheme where we impose the restriction that no side information can be sent, i.e. encoder and decoder must perform their adaptation based on the quantized information.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We propose in this paper a variable-coefficient fixed-length (VCFL) coding scheme for wavelet-based image transmission over noisy channels. When an image is transmitted through noisy channel with high throughput, both image compression and error-resistant coding scheme need to be considered. In this approach, an image is first decomposed into subbands by wavelet transform and quantized using an adaptive quantization scheme. The adaptive quantization is adaptive to both the frequency characteristics and the spatial constraints based on Gibbs random field. The traditional variable length entropy coding schemes, such as Huffman coding or arithmetic coding, and the fixed length coding such as LZW are usually very sensitive to channel noise for image transmission applications. Even with the insertion of synchronization symbols, they still cannot be directly employed without additional error correction/detection coding. To overcome the difficulty of image transmission over noisy channels, we propose to code the quantized subband coefficients with the VCFL scheme. This coding scheme attempts to keep the balance between redundancy removal, synchronization detection and error resilience. Part of the codebook is field based on the observation of the coefficient spatial distribution patterns in each subbands to alleviate the transmission of the codebook. The remaining code positions within the fixed length codebook can be utilized to combat channel errors by carefully arranging the code positions such that the codes with biggest transition cost will have the biggest Hamming distance. These positions can laos be filled with other frequently appeared coefficient composition sequences to achieve higher compression ratio. Experimental results of image transmission over noisy channels are reported to show the promising potential of the proposed coding scheme.

We examine distributed estimation of the average power of a random signal in wireless sensor networks (WSNs). Due to stringent bandwidth/power constraints, each sensor quantizes its observation into one bit of information and sends the quantized data to a fusion center, where the signal power is estimated. We firstly introduce two fixed quantization (FQ) schemes, with the first using a single threshold and the second employing a pair of symmetric thresholds. The maximum likelihood (ML) estimators associated with the two FQ schemes are developed, and their corresponding Cramer-Rao bounds (CRBs) are analyzed. We show that the FQ approach, especially the second one, can achieve an estimation performance close to that of a clairvoyant estimator using unquantized data if the optimum quantization threshold is available; however, the optimum threshold is dependent on the unknown signal power, and as the threshold deviates from its optimum value, the performance degrades rapidly. To cope with this difficulty, we propose a distributed adaptive quantization (AQ) approach by which the threshold is dynamically adjusted from one sensor to another in a way such that the threshold converges to the optimum threshold. Our analysis shows that the proposed AQ approach is asymptotically optimum, yielding an asymptotic CRB equivalent to that of the FQ approach with optimum threshold.

This study presents a transform-domain distributed video coding (DVC) system with a rate–distortion (R–D)-based Adaptive QuanTisation (AQT) scheme. In the proposed system, the transform-domain Wyner–Ziv frame is divided into partitions and is adaptively quantised based on estimated local R–D characteristics for each partition. The R–D estimation is performed based on a correlation model between the original source information and the side information and can be applied at the decoder without adding complexity to the encoder. Coding results and comparisons with existing DVC schemes and with H.264/AVC interframe and intraframe coding are presented to illustrate the performance of the proposed system.

The quantizer plays an important role not only in traditional video compression standards but also in the Wyner-Ziv coding, which can be thought to consist of a quantizer followed by a Slepian-Wolf encoder. In this context, this paper proposes a frame-level adaptive nested scalar quantization scheme for distributed video coding (DVC). At the encoder the absolute frame difference between current Wyner-Ziv frame and previous key frame is utilized to denote the statistical dependency, and then the adaptive quantizer stpesize is decided according to threshold and sent to decoder. The Wyner-Ziv frame is decoded and reconstructed based on the received parameter and the side information. It is shown that at a possible cost of higher error rate of channel code, the proposed adaptive quantization scheme is a potential way to lower the quantization error and on the whole improve the rate distortion performance, especially for video with low motion.

Vector quantization is a new coding technique which has enjoyed much success in its brief history. Its most attractive features are a high compression ratio and a simple decoder. Thus, it shows great promise in applications using a single-encoder with a multiple decoder such as videotext and archiving. However, some problems have arisen, most notably edge degradation, the so-called "block effect". A novel method is described in this paper which alleviated this problem without much increase in computational effort. An index has been devised, the activity index, based upon measurements on the input image data; it is used to classify image areas into two groups, active and nonactive. For nonactive areas, large block size is used, while for active areas the block size is small. Two codebooks are generated corresponding to each of the two groups of blocks formed. Using this adaptive vector quantization scheme the results obtained show that the edge features are well preserved upon reconstruction at the decoder.

Hidden Markov Model is an important approach applied to activity recognition. In the first-order Hidden Markov Model, there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model, which debases the precision of information extraction comparatively. In second-order Hidden Markov Model, the relevance between the current state and its previous two states is considered. Also, the relevance between the current observation and its previous state is considered. So second-order Hidden Markov Model has stronger performance of recognition of incorrect information. In my paper, second-order Hidden Markov Model is applied to activity recognition. The experiments show that our approach has higher precision than those approaches based on first-order Hidden Markov Model and based on Conditional Random Fields.

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations.

In this paper, we propose a fully distributed algorithm for joint clock skew and offset estimation in wireless sensor networks. With the proposed algorithm, each node can estimate its clock skew and offset by communicating only with its neighbors. Such algorithm does not require any centralized information processing or coordination. Simulation results show that estimation mean-square-error at each node converge to the centralized Cramér-Rao bound with only a few number of message exchanges.