Distributed source coding using symbol-based and non-binary turbo codes – applications to wireless sensor networks

It is well known that binary error-correcting codes with iterative decoders can achieve near Shannon-limit performance on the additive white Gaussian noise (AWGN) channel, but their performance on more realistic wireless channels can become degraded due to the presence of burst errors or impulsive noise due to interference. A better performing coding scheme is the class of non-binary codes, which are known to be more effective in correcting burst errors, but interestingly there is no research reported in the literature investigating non-binary codes on impulsive noise channels. In this paper, we investigate the performance of non-binary turbo codes defined in a finite field GF(4) on symmetric alpha-stable impulsive noise channels and compare with comparable binary turbo codes. A Cauchy receiver is also employed to mitigate the effects of the channel to assist the turbo decoding. Our simulation results show that although the non-binary turbo code performs similarly to the binary turbo code on the AWGN channel, it achieves a significant coding gain over the binary turbo code as impulsiveness increases.

Non binary turbo codes are gaining a lot of attention since the recent adoption of duo binary circular turbo codes by the Digital Video Broadcasting (DVB) community. In this paper, a Joint Source Channel Decoding (JSCD) scheme is proposed for circular non-binary turbo codes. With a view to exploit a-priori probabilities from source statistics, a symbol level trellis is incorporated into the circular trellis of the non-binary turbo decoder. Reversible Variable Length Codes (RVLC) are used as the input source in all simulations. Results show that with JSCD, circular duo binary turbo codes provide an average gain of 0.4 dB at Bit Error Rate (BER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> as compared to conventional decoding of circular duo binary turbo codes. With circular triple binary turbo codes, JSCD provides a striking average gain of 3.5 dB at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> compared to conventional decoding of circular triple binary turbo codes.

In this paper, we study two problems of providing reliable data transmission and developing aggregation techniques for correlated data in wireless sensor networks. A system with forward error correction (FEC) can provide an objective reliability while using less transmission power than a system without FEC. Because of the additional parity bits and encoding/decoding energy consumptions, we study the effect of FEC on energy efficiency. We propose to use LDPC codes for FEC. We show that wireless sensor networks using LDPC codes are almost 45% more energy efficient than those that use BCH codes, which were shown to be 15% more energy efficient than the best performing convolutional codes. Then we study the problem of providing aggregation for two and three correlated nodes in wireless sensor networks. We propose to use LDPC codes in wireless sensor networks for source and channel coding to obtain a two-fold energy savings. For two correlated nodes, we study both distributed source coding and joint source-channel coding. While distributed source coding using LDPC codes was studied before, joint source-channel coding using LDPC codes is introduced for the first time. The difference between our work in distributed source coding using LDPC codes and the previous work lies in the LDPC code design procedure. The simulation results show that our proposed design criteria improves the performance of the source coding. The convergence of the non-uniform LDPC code of our design technique is almost 60% closer to the Slepian-Wolf limit. For three correlated nodes, we study distributed source coding using LDPC codes. We simplified the problem of design procedure to randomly punctured LDPC codes. This is a new approach for designing LDPC codes and the simulation results for code of length 1000 shows that the convergence of the LDPC code is achieved at compression rate 0.3174 which is only 0.08 away from the Slepian-Wolf limit.

For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Na&#239;ve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

The classical turbo decoding algorithm is based on the use of binary waveform disturbed by a Gaussian distributed noise. Although a significant amount of coding gain can be achieved, binary turbo codes do not have good bandwidth efficiency unless they are heavily punctured. Turbo trellis coded modulation (TTCM) can provide good turbo code performance without bandwidth expansion. However, it requires unique code design. The simplest way to attain both power and bandwidth efficiency is to adopt high-order modulation schemes with a commonly studied binary turbo code. This approach requires a conversion of the demodulator output associated with the nonbinary channel symbol into bit metrics to input to the binary turbo decoder. This paper investigates various conversion functions for M-ary PSK and M-ary QAM constellations. The turbocoded bit-error-rate performances are compared using these metrics. It is shown that the likelihood metric, which is the optimum for the uncoded case, does not provide the optimum performance in the coded case. This paper also indicates that this turbo-coded waveform can perform as well as TTCM

In this paper propose an several concepts for optimal algorithm of aggregation of data in wireless sensor network. Given analysis of problems for information aggregation in wireless sensor network. Proposed optimise of structure the node and given basic concepts for reducing energy consumption. Also proposed network partitioning into piconet with next clustering. In final proposed are two modifications for ant algorithms to optimize data collection in wireless sensor network.

Distributed source coding (DSC), formulated thirty years ago, is lately witnessing a great research effort. This effort is encouraged by emerging new applications that could greatly benefit from the properties inherent to DSC, like low complexity encoders and embedded error resilience. Among the challenging topics related to DSC there is the generation of the side information, an estimation made by the decoder of the information being decoded. This paper quickly summarizes the theoretical bases of DSC and then focuses on the essential task of generating the side information, by developing the model-based method drafted by the authors on a previous publication. It is shown that the application of model-based side information generation techniques on top of current state of the art DSC-based video codecs provides an improvement of 0.5 to 0.75 dB over a wide range of bit-rates. Comparisons against H.264 are also provided.

With the limited resources, it is difficult for wireless sensor network to achieve a long time, continuous, high-speed acquisition of multimedia information and a real-time, reliable transmission of high-volume sampling data. This article proposed the novel data acquisition strategy based on compressed sensing theory, which can perfectly achieve the long time, real-time, reliable transmission of high-volume multimedia data in wireless sensor network. Reasonable experiments were designed to verify the effectiveness of the algorithms, and the experiment results show that: the proposed multimedia signal acquisition strategy is reasonable, practicable, and more suitable for the wireless multimedia sensor networks.

In this paper, new interleaver design criteria for turbo codes are proposed, targeting the reduction of the correlation between component decoders. To go beyond the already known correlation girth maximization, we propose several additional criteria that limit the impact of short correlation cycles and increase code diversity. Two application examples are elaborated, targeting an 8-state binary turbo code and a non-binary turbo code defined over GF(64). The proposed design criteria are shown to improve the error correcting performance of the code, especially in the error floor region.

In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The Wyner-Ziv Theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve similar efficiency. We propose a transformdomain Wyner-Ziv coding scheme for motion video that uses intraframe encoding, but interframe decoding. In this system, the transform coefficients of a Wyner-Ziv frame are encoded independently using a scalar quantizer and turbo coder. The decoder uses previously reconstructed frames to generate side information to conditionally decode the Wyner-Ziv frames. Simulation results show significant gains above DCT-based intraframe coding and improvements over the pixel-domain Wyner-Ziv video coder.

In practical applications, because of some reasons, such as node failure or link failure, data loss is a normal occurrence in wireless sensor networks. For many applications on which data has a great impact, it is important to recover the lost data. Because of the sensory data has spatial correlation and temporal correlation, it is difficult to recover the lost data. Consider this, in this paper, we propose a novel algorithm based on the Markov Random Field to reduce the data error ratio during the data recovery process. In our proposed algorithm, we use the curved face recovery to utilize the spatial correlation in the sensory data, then we explore the temporal correlation between data in wireless sensor networks so that it can improve the accuracy of data recovered. A large amount of experiments show that, compared to other relevant works, the data recovery algorithm we proposed can reduce the error rate of the fitting data, especially data lost seriously in the wireless sensor network.

Wireless Sensor Networks (WSNs) are being used globally in many areas. As the purpose of the WSN is raising at the similar rate the safety intimidation are also rising. Nodes in a WSN are vulnerable to different attacks mainly due to their unguarded communication and nature of exploitation. Therefore, the most important in such networks is to providing security. A vigorous security solution for the networks must also make possible node confirmation. But occasionally it would be an energy utilizing process. So, we require an energy efficient and safe mechanism to protected the entire network. Thus this paper, Energy Aware efficient Cryptographic Approach (EACA) for securing data in WSN is proposed. In this scheme, the dynamic mode and sleep mode of sensor to save the energy consumption in WSN. The sender side and receiver side algorithm provide the data security. Our simulation results demonstrate that improved the residual energy and throughput in WSN.

In distributed video source coding side-information at the decoder is generated as a temporal prediction based on previous frames. This creates a virtual dependency channel between the source video at the encoder and the side information at the decoder. In recent years, distributed source coders were introduced with sophisticated error correction codes, like turbo codes and LDPC codes. Although these codes performs well on noisy network communication channels, it is far from obvious that these codes can handle the non-stationary noise in the dependency channel as encountered in distributed video coders. In this paper we study the consequences of inaccurate modeling of the dependency channel on turbo and LDPC coding and show that the performance depends greatly on the choice of the probabilistic model for the dependency channel. The results show that LDPC codes are less sensitive to inaccuracies in the dependency channel models.