Encoded Sensing for Energy Efficient Wireless Sensor Networks

Nikolov, Milen; Haas, Zygmunt J.

doi:10.1109/jsen.2017.2756865

Cited by 23 publications

(9 citation statements)

References 33 publications

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“…Recently, Compressive Sensing (CS) [1] has been researched extensively in various areas, such as image processing [2], [3], wireless communication [4]- [6], mm-wave communications [7], [8], optical communication [9], and wireless sensor networks [10]. However, with development of the Internet of Things and 5G wireless communications, data secure transmission has become increasingly important.…”

Section: Introductionmentioning

confidence: 99%

Secure Image Transmission in Wireless OFDM Systems Using Secure Block Compression-Encryption and Symbol Scrambling

Wang

2019

IEEE Access

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In this paper, a joint compression-encryption and symbol scrambling scheme is proposed for orthogonal frequency-division multiplexing (OFDM) systems for image date. First, in the compressionencryption phase, the original image data is fist divided into sub-blocks and then an secure image block compressive sensing (BCS) is applied to resulting image data, in which a chaotic sparse basis matrix and a chaotic measurement matrix based on the discrete cosine transform (DCT) are used. Secondly, the resulting secure CS data is transform into binary phase shift keying (BPSK) symbols and then a chaotic scrambling operation is applied to the obtained BPSK symbols. The simulation results show that the proposed secure BCS scheme can achieve better peak signal-to-nose ratio (PSNR) performance than that of the conventional chaotic measurement method. The simulation results also show that a good trade-off performance of signals between PSNR and computational complexity can be achieved when the proper sub block size of image is adopted. In addition, the performances of the BPSK OFDM signal, which is generated from the secure BCS of the image, are evaluated over AWGN and multipath fading channels. The simulation results show that the proposed scheme not only achieve effective secure block compressive sensing and achieve secure transmission in physical layer. The security of the transmitted image data is enhanced by the joint compression-encryption and symbol scrambling. INDEX TERMS Compressive sensing, security, chaos theory, OFDM.

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Section: Introductionmentioning

confidence: 99%

Secure Image Transmission in Wireless OFDM Systems Using Secure Block Compression-Encryption and Symbol Scrambling

Wang

2019

IEEE Access

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“…Researchers have produced plenty of research on routing in WSNs; some of the research is analyzed here. Nikolov, M. et al (2018) [11] presented encoded detection for communitarian encoding and the transmission of sensor information that lessens correspondence. Encoded detection spared 80% of the vitality of an ideal hypothetical transmission circulated shaft shaping design.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Bali

Gill²,

Choudhary³

et al. 2022

Energies

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In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

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“…Besides, in [23], the authors study the potential of analog mappings in a fading multiple-access environment and optimize the precision levels of the quantizers. Another example can be found in [31], where the authors rely on encoded sensing to partition the network into groups of sensors that jointly encode and transmit their information to the sink. Nevertheless, most of these works do not take into account the power limitations of the devices involved (which is crucial in current mMTC deployments).…”

Section: A Prior Workmentioning

confidence: 99%

Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC

Liesegang

Muñoz

Pascual‐Iserte

2021

IEEE Trans. Commun.

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This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy.

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Encoded Sensing for Energy Efficient Wireless Sensor Networks

Cited by 23 publications

References 33 publications

Secure Image Transmission in Wireless OFDM Systems Using Secure Block Compression-Encryption and Symbol Scrambling

Secure Image Transmission in Wireless OFDM Systems Using Secure Block Compression-Encryption and Symbol Scrambling

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC

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