Position Based Unequal Error Protection for Image Transmission with Energy Constraint over Multirate XPD MIMO Sensor Networks

Wireless Sensor Network (WSN) is used in various applications. Sensors acquire samples of physical data and send them to a central node in different topologies to process the data and makes decisions. A main performance factor for WSN is the battery life that depends on energy consumption on the sensor. To reduce the energy consumption, an energy efficient transmission technique is required. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. This leads to enhance the transmission and hence reduce energy consumed by the sensor. In MIMO systems multiple signals are combined at the transmitter and transmitted using multiple antennas. This provides each receiver the whole combined signal and hence, array processing techniques helps in getting better performance. To further enhance the transmission of data, a Low Density Parity Check (LDPC) Coded MIMO wireless sensor network is proposed. The system implements space diversity through Multiple Antennas and temporal diversity through LDPC code and uses a clustering procedure to optimize the forming of the MIMO system. Results showed that, if the number of sensors is greater than the number of receiving antennas, time or frequency multiplexing is possible to keep good performance for the devised system. And by controlling the encoder we can create a temporal and spatial code among the transmitted signals enhancing the BER results in longer battery life at sensor nodes.

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

In view of increasing use of video transmission over wireless channels, a lot of research efforts have been made to trade-off the error resiliency and transmission bandwidth requirements. This in turn has led to development of various unequal error protection (UEP) schemes. In this paper, the performance of optimized 2-layered and 3-layered UEP schemes using hierarchical 64 QAM (64-HQAM) for wavelet-transformed images coded with set partitioning in hierarchical trees (SPIHT) algorithm and transmitted over additive white Gaussian noise (AWGN) channel is investigated. A look-up table (LUT) based approach to select the optimal values of modulation parameters of 64-HQAM for different channel conditions is suggested so as to reduce the computational complexity. Simulation results show that 3-layered optimized 64-HQAM-based UEP outperforms corresponding non-optimized UEP. Also, 3-layered optimized UEP scheme using 64-HQAM performs slightly better as compared to optimized 2-layered UEP scheme using 64-HQAM.

CRSN (Cognitive Radio Sensor Networks) have recently gained huge interest as an area of research. Naturally, due to energy limitations in WSN (Wireless Sensor Networks), grouping sensor nodes into clusters has been widely adopted by the research community to overcome the issue of limited energy budget and generally achieve high energy efficiency as well as prolonging network lifetime in large-scale WSN environments. Combining clustering of WSN with MIMO (Multiple Input Multiple Output) is a promising technique used in literature of this research area leading to what is called cooperative MIMO clustered WSN. Compared with existing work, our contributions are: i) Extension of CR (Cognitive Radio) technique to cooperative MIMO clustered WSN in a spectrum sharing sense as well as demonstrating that SISO setting isn't always the best setting for transmission even at small distances. The shift to other settings is more favorable to guarantee not disturbing the primary user while transmitting on the same channel; and ii) Proposing a selection criterion for cooperative MIMO CRSN that chooses the minimum energy setting (SISO/SIMO/MISO/MIMO) to guarantee a proper communication for the secondary CR system under some interference constraint to the primary user as well as satisfying a specific BER requirement for CR secondary system.

We present a dynamic unequal error protection (UEP) framework for the embedded image bit streams over noisy channels. A general source model for M-rate UEP schemes is derived and an optimization algorithm for 2-rate dynamic UEP schemes is developed. To facilitate the design and implementation of UEP schemes, we also derived a necessary condition and an upper bound for UEP gains. Simulation results demonstrate about 0.3 dB improvements over equal error protection (EEP) schemes at the price of negligible overheads while the progressiveness of the original bit streams is still kept.

In this paper we propose an image coding scheme based on Block Turbo Codes (BTC) for protecting SPIHT coded images transmitted over wireless channels. An Unequal Error Protection (UEP) scheme using different product codes is also presented. The optimization of the UEP scheme under a transmission budget constraint is investigated. Performance of the proposed system are first evaluated over Gilbert-Elliott (GE) channels. They are then compared to the well-known system of Sherwood and Zeger, through numerical simulations over a single path Rayleigh fading channel. Then, we present an optimized mobile image transmission framework involving a multiuser asynchronous DS-CDMA transmission system over Rayleigh multipath channels. Simulation results for the 512x512 Lena image, transmitted over the ITU Vehicular A channel, show that a significant gain in PSNR is obtained by using the SIC/RAKE multiuser detector in comparison with a conventional detection by the RAKE receiver. Furthermore, performance are also improved when introducing the UEP scheme.

Resource allocation for multimedia selective encryption and energy efficient transmission has not been fully investigated in literature for wireless sensor networks (WSNs). In this article, we propose a new cross-layer approach to optimize selectively encrypted image transmission quality in WSNs with strict energy constraint. A new selective image encryption approach favorable for unequal error protection (UEP) is proposed, which reduces encryption overhead considerably by controlling the structure of image bitstreams. Also, a novel cross-layer UEP scheme based on cipher-plain-text diversity is studied. In this UEP scheme, resources are unequally and optimally allocated in the encrypted bitstream structure, including data position information and magnitude value information. Simulation studies demonstrate that the proposed approach can simultaneously achieve improved image quality and assured energy efficiency with secure transmissions over WSNs.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

In this paper, we address the ordering of transmit antennas according to reliability for unequal error protection (UEP) in spatially multiplexed (SM) multiple-input multiple-output (MIMO) systems with joint detection at the receiver. When zero-forcing (ZF) detection is adopted, the reliabilities of transmit antennas are explicitly expressed as postequalization signal-to-noise ratios (SNRs). Thus, multiantenna UEP can be implemented by assigning data of high priority to transmit antennas with a high postequalization SNR. Unfortunately, the overall error performance is generally unsatisfactory. Various joint signal detection techniques that achieve maximum likelihood (ML) or near-ML performance have been developed as alternatives to ZF, but it is not obvious how to discriminate the reliabilities of the transmit antennas or the jointly detected symbols. Recently, an ordering technique for antenna reliabilities was proposed that exploits the structure of the previous near-optimal QR-decomposition-based least-reliable-layer joint detection. In this paper, we divide the log-likelihood ratio of each symbol into collaborative and individual components, assuming joint ML detection. We derive a statistical connection between the magnitude order of each component of the multiple symbols (or the corresponding transmit antennas) and the post-ZF-equalization SNRs. Based on the statistical connections, we propose the use of post-ZF-equalization SNR as a criterion for antenna reliability ordering of SM MIMO with various near-optimal, as well as optimal, joint detections. The differentiated error performance of transmit antennas is also shown to be mainly attributed to the difference of individual components, and the tendency becomes stronger as the constellation size increases. Simulations demonstrate that the proposed ordering technique better meets the UEP requirement with lower computational complexity than the conventional ordering. Assuming $N_t \times N_t$ MIMO systems, the previous ordering and the proposed ordering require $\mathcal{O}(N_t^3)+\sum_{N=2}^{N_t-1}\mathcal{O}(NN_t^2+N^3)$ and $\mathcal{O}(N_t^3)$ , respectively.

In wireless sensor network (WSN), one of the most critical issues is to minimize the energy consumption. The use of cooperative multiple-input multiple-output (MIMO) communication can save a lot of transmission energy. However, how to organize the WSN based on MIMO remains a critical challenge. In this paper, a novel clustering-CH-exchange-method (CCEM) is proposed to apply cooperative MIMO in WSN. CCEM firstly selects some cluster heads (CHs) and partitions the network into clusters. Then the CH in each cluster finds cooperative nodes to form MIMO antennas. The CH exchange operation runs if CH can't find enough cooperative nodes. The energy consumption of the network is developed. Simulation results show that CCEM provides high success probability of forming complete MIMO overlay network and significant energy efficiencies.

Efficient utilization of bandwidth is key to providing mobile multimedia services such as wireless imaging. As such, efficient allocation of the available parity budget for error protection of the associated codestreams is essential for ensuring quality of service. Computing the optimal parity allocation to the codestream packets may not be possible in real-time due to the huge number of ways the parity can be assigned to the codestream packets. This is particularly true for systems having limited resources, such as mobile handheld devices. As a result, it is important to provide an error-protection scheme that gives good error-protection performance while imposing low computational and memory demands on the system. We therefore analyze how different parameters such as signal-to-noise ratio and source distortion-rate function affect the optimal unequal error protection (UEP) scheme for embedded codestreams. We also propose to approximate the optimal UEP using suitable mathematical models in order to reduce the complexity that otherwise would occur with computing the optimal UEP. We investigate several models for their fitness in approximating the optimal UEP using different performance metrics. The simulation results show that most of these models provide an excellent trade-off between performance and complexity.

This paper investigates the performance of IEEE 802.11ac physical layer (PHY) when the following multiple input multiple output (MIMO) detectors are implemented: Sphere Decoding (SD); QRM-Maximum Likelihood (QRM-MLD); List Minimum Mean Squared Error Successive Interference Cancellation (List MMSE-SIC); List Zero-Forcing SIC (List ZF-SIC); Lattice Reduction MMSE-SIC (LR MMSE-SIC) and List LR MMSE-SIC. The conclusions are founded on simulation results, validated using first order techniques, over TGac channel models. The IEEE 802.11ac simulator implements hard-decision Viterbi decoding and realistic synchronization and channel estimation algorithms. The List MMSE-SIC scheme presents power gains around 2 dB in relation to the List ZF-SIC MIMO detector. The QRM-MLD MIMO detector has a highly superior performance regarding to the List MMSE-SIC MIMO detector since this latter scheme cannot overcome the propagation errors that degrade the performance of MMSE-SIC MIMO detectors. However, for moderate list lengths of 4 and 8, the QRM-MLD MIMO detector presents severe power losses in relation to the optimal SD MIMO detector. The LR MMSE-SIC MIMO detector allows power losses of only 1–2 dB in reference to the optimal SD MIMO receiver. There are no noticeable power gains when List detection techniques in MMSE-SIC MIMO detectors are implemented since the diversity gains due to implementation of LR basis change overcome the error propagation in SIC MIMO detectors with original channel basis.

Congestion in Wireless Sensor Network (WSN) occurs when traffic load allocated to any sensor node is beyond its capacity. It is necessary to control the congestion traffic to support traditional quality of service (such as packet loss ratios, packet delay, wasting energy and throughput) especially, multimedia application of Wireless Multimedia Sensor Network (WMSN). Congestion control in WSN has been shown to be very effective with respect to extending lifespan of system. Hence to avoid the congestion in WSN, we proposed an efficient protocol called Priority-based Congestion Control Protocol (PCCP) which prevents an upstream congestion in WSN. The PCCP creates priority table based an importance of each node, and then sends this information to all the nodes within the network. Secondly, the major function of PCCP is to measure congestion level in the network using ratio of packet inter-arrival time along over packet service time. PCCP uses to control upstream congestion along with congestion degree and packet table. PCCP is hop-by-hop upstream congestion control protocol for WSN. PCCP provides work for packet-based computation to optimize congestion control. It can work under both single path & multipath routing. PCCP ensures guarantee of packet loss as well as delay, resulting each node can avoid unfairness and achieve flexible throughput. In this paper, we have proposed that PCCP provides better QoS by controlling network recourse management and network traffic to consuming efficient energy.

In this paper, we present and analyze a novel link layer transmission strategy for wireless networks using multiple input multiple output (MIMO) technology at the physical layer. When the channel state information (CSI) is available at the transmitter (CSIT), we present an adaptation algorithm that selects the proper orthogonal sub-channels of the MIMO link to use in order to increase the link layer throughput while maintaining an acceptable frame loss rate. Computer simulations show that the proposed adaptation technique provides a significant performance improvement achieved by designing MIMO-aware link layer algorithms

Wireless sensor network (WSN), a network of minute interconnected sensing element nodes communicating within themselves utilizing radio frequency signals and utilized in immensely enormous quantities to examine and realize the physical world. The communicating sensing element nodes are power-driven by batteries, which have a circumscribed lifetime. Hence managing the energy is a primary requisite for the design of WSN. In this paper, an efficient wireless sensor network utilizing a cooperative Multiple Input Multiple Output (MIMO) predicated on Space Time Trellis Code (STTC) is proposed. This STTC based cooperative MIMO technique is incorporated into Exclusive OR — Low Energy Adaptive Clustering Hierarchy (XOR-LEACH) protocol. For selection of cluster head, the last cluster head and a arbitrary binary vector are XORed together. The proposed system obtains amended performance and is compared with the existing LEACH protocol in terms of network lifespan and residual energy. This system performance in terms of Bit Error Rate (BER) has been analyzed for various detection techniques.