Siguang Chen

Compressive network coding for error control in wireless sensor networks

Since the observed signals of nearby sensors are known to be correlated, this paper firstly investigates the connection between network coding and compression concept of compressed sensing and then makes an in-depth combination between these two powerful concepts for error control in wireless sensor networks. Thus, a joint scheme is developed to achieve the maximum gain by exploiting the temporal and spatial correlations simultaneously. This scheme overcomes drawbacks of network coding theory by injecting the corresponding distributed compressed sensing concept into network coding, i.e., the scheme possesses good compression gain and graceful degradation of precision in the reconstruction process. Meanwhile, it can tolerate finite erasures and errors as well as reconstruct the original information as precise as possible when the rank of error matrix (induced by erasures and errors) doesn’t exceed the upper boundary. Finally, the reliability analysis and numeric results show that the compressive network coding scheme (i.e., the joint scheme) outperforms the conventional network coding scheme in robustness and performance.

Compressed error and erasure correcting codes via rank-metric codes in random network coding

The error control of random network coding has recently received a lot of attention because its solution can increase robustness and reliability of data transmission. To achieve this, additional overhead is needed for error correction. In this paper, we design a compressed error and erasure correcting scheme to decrease the additional overhead of error correction. This scheme reduces the computation overhead dramatically by employing an efficient algorithm to detect and delete linearly dependent received packets in the destination node. It also simplifies the hardware operations when the scheme reduces the received matrix Y to form Ek(Y) instead of E(Y) in the decoding process. If at most r original packets get combined in k packets of one batch, the payload of one packet can increase from M − k to M − O(rlog qk) for the application of compressed code, where M is the packet length. In particular, the decoding complexity of compressed code is O(rm) operations in an extension field , which does not enhance the overall decoding complexity of the system. Finally, we also compare our schemes performance with existing works. The numerical results and analyses illustrate the security and performance of our scheme. Copyright

Combining network coding and compressed sensing for error correction in wireless sensor networks

As the spatial and temporal correlations of sensor readings are common in wireless sensor networks, motivated by these features and the drawbacks of network coding NC, we introduce compressed sensing CS into NC scheme and construct a cooperating coding mechanism, which performs over different data fields with a compatible transformation measure for the combination of NC and CS. This cooperating coding scheme can reduce the amount of redundant information transmission significantly, because the temporal and spatial correlations are explored fully. Meanwhile, the erasures and errors are considered simultaneously in relay transmission process; a NC decoding for error control is proposed to correct the erasures and errors. Although the decoding error of NC is existent, this error can be further reduced by the reconstruction process of CS; as a result, the relative recovery error is small enough in the sink. Finally, the reliability and performance analyses confirm that the proposed cooperating coding scheme obtains considerable compression gain as compared with conventional coding scheme of NC and transmits information reliably with high recovery precision. Copyright

LKM: a LDA-based k -means clustering algorithm for data analysis of intrusion detection in mobile sensor networks

Mobile sensor networks (MSNs), consisting of mobile nodes, are sensitive to network attacks. Intrusion detection system (IDS) is a kind of active network security technology to protect network from attacks. In the data gathering phase of IDS, due to the high-dimension data collected in multidimension space, great pressure has been put on the subsequent data analysis and response phase. Therefore, traditional methods for intrusion detection can no longer be applicable in MSNs. To improve the performance of data analysis, we apply K-means algorithm to high-dimension data clustering analysis. Thus, an improved K-means clustering algorithm based on linear discriminant analysis (LDA) is proposed, called LKM algorithm. In this algorithm, we firstly apply the dimension reduction of LDA to divide the high-dimension data set into 2-dimension data set; then we use K-means algorithm for clustering analysis of the dimension-reduced data. Simulation results show that LKM algorithm shortens the sample feature extraction time and improves the accuracy of K-means clustering algorithm, both of which prove that LKM algorithm enhances the performance of high-dimension data analysis and the abnormal detection rate of IDS in MSNs.

Accelerated Distributed Optimization Design for Reconstruction of Big Sensory Data

According to the practical requirements of high recovery precision and low latency in wireless big sensory data networks, this paper proposes an accelerated distributed rate control method for minimizing the recovery error of big sensory data. This method can guarantee the error minimization of reconstructed data and converge to the optimal value fast with a lower latency. In order to achieve these effects, an accelerated distributed solving algorithm is constructed by designing accelerated subgradient method for dual decomposition. This solving algorithm achieves convergence rate

Uplink Capacity of Two-Hop Relay TDD-CDMA Cellular Networks with Time-Slot Scheduling

{O(1/{t^{2}})}

Downlink Subcarriers Required Analysis for Two-Hop OFDMA Cellular System

in practical implementation, which significantly improves the convergence rate of regular solving algorithms. Meanwhile, the convergence analysis testifies the convergence property of the proposed distributed solving algorithm, and this algorithm is applicable to other convex optimization problems. Finally, the performance evaluation shows that the proposed accelerated method can converge to the unique optimal value successfully and the convergence speed is faster than the regular optimization method, and this proposed method can be extended to networks of different sizes without sacrificing the accelerated effect.

A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks

In code division multiple access network, the interference has more effect on the capacity than the other cellular networks. To increase the uplink capacity of traditional time division duplex-code division multiple access (TDD-CDMA) cellular networks, the method of two-hop relay is adopted. In this paper, we first present a two-hop relay TDD-CDMA cellular system model, and then design two kinds of time slot scheduling schemes and power control models. Based on the system model, the total interference power to target cell can be analyzed, and then the closed form expressions of the uplink capacity are obtained by mathematical calculating. Finally the impact of different system parameters on networks’ capacity is discussed.

Concurrent transmission mechanism to mitigate pan-exposed-node problems in wireless sensor networks:

In Orthogonal frequency division multiple access (OFDMA) cellular system, the user at the different location of the cell requires different number of subcarriers to satisfy the user’s data rate requirement. In this paper, based on the Interference to Signal Ratio (ISR), we analyze the average number of downlink subcarriers required at inner and outer region for the two-hop OFDMA cellular system. Firstly we propose a two-hop OFDMA cellular system model. Then based on this model, we calculate the Cumulative Distribution Function of ISR at inner and outer region respectively, and obtain the theoretical analysis results for the average number of subcarriers required at two regions of the reference cell. Finally, with the help of the numerical calculation and Monte-Carlo simulations, we analyze the impact of the various parameters on the average number of subcarriers required of the two-hop cellular system. And the results are compared with the average number of subcarriers required of the one-hop cellular system.

Secret and reliable coding mechanism for noncoherent multisource network coding

How to reduce the number of transmissions or prolong the lifetime of wireless sensor networks significantly has become a great challenge. Based on the spatio-temporal correlations of sensory data, in this paper, we propose a hierarchical adaptive spatio-temporal data compression (HASDC) scheme to address this issue. The proposed compression scheme explores the temporal correlation of original sensory data by employing the discrete cosine transform and adaptive threshold compression algorithm (ATCA). And then, the cluster head node explores the spatial correlation among the compressed temporal readings by utilizing discrete wavelet transform (DWT) and ATCA. The HASDC scheme obtains better recovery quality and compression ratio by combining data sorting, ATCA and spatio-temporal compression concept. At the same time, according to the correlation of sensory data and the adaptive threshold value, the HASDC scheme can adjust the compression ratio adaptively, thus it’s applicable to different physical scenarios. Finally, the simulation results confirm that the transformed coefficients are more concentrated than the ones without introducing DWT, and the proposed scheme outperforms other spatio-temporal schemes in terms of compression and recovery performances.