Short Word-Length Entering Compressive Sensing Domain: Improved Energy-Efficiency in Wireless Sensor Networks (original) (raw)

IJERT- Wireless Sensor Networks - Energy Perspective with Compressive Sensing

International Journal of Engineering Research and Technology (IJERT), 2018

https://www.ijert.org/wireless-sensor-networks-energy-perspective-with-compressive-sensing https://www.ijert.org/research/wireless-sensor-networks-energy-perspective-with-compressive-sensing-IJERTCONV6IS09005.pdf Wireless sensor networks are getting more importance with the technological progression of all realms of life. The critical issue of energy expenditure and management of wireless sensor networks (WSNs) has been discussed in this paper. Together with the normal procedures for saving the energy by using renewable energy sources, the study talks over the use of compressive sensing (CS) framework in WSNs to increase the energy efficiency. The energy efficient performance of different CS algorithms are discussed with necessary estimations. It is clear that for a sufficiently sparse sensor signal, a substantial amount of energy can be hold back by using CS methods.

Efficient Compressive Sensing based Technique for Routing in Wireless Sensor Networks

INFOCOMP Journal of Computer Science, 2015

Energy consumption and prolonging network lifetime are a primary challenge in many studies on Wireless Sensor Networks (WSN). Thus, since radio communication and routing protocol transmission are in general the main cause of power consumption, different techniques proposed in lit- erature to improve energy efficiency have mainly focused on l imiting transmission/reception of data. To this aim, we propose an adaptive and efficient technique base d on compressive sensing for improving the performance of routing in wireless sensor network. The performance of our technique is evaluated by applying it to PEGASIS (power efficient gathering in senso r information systems), which is one of the most popular protocols for routing in wireless sensor network. A comparison of PEGASIS and PE- GASIS with Huffman coding shows the advantage of the proposed technique in terms of reducing the energy consumption and network lifetime.

Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing

International Journal of Distributed Sensor Networks, 2015

The estimation of the localization of targets in wireless sensor network is addressed within the Bayesian compressive sensing (BCS) framework. BCS can estimate not only target locations but also noise variance of the environment. Furthermore, we provide adaptive iteration BCS localization (AIBCSL) algorithm, which is based on BCS and will choose measurement sensors according to the environment adaptively with only an initial value, while other frameworks require prior knowledge such as target numbers to choose measurements. AIBCSL suppose that environment noise variance is identical in interested area in a short period of time and change measurement numbers until terminal condition is reached. To suppress noise, we optimize estimation result by energy threshold strategy (ETS), which takes that transmit energy of noise focused on single grid is much lower than signal into consideration. And multisnapshot BCS (MT-BCS) will be explained and lead to a good result in low SNR level situat...

Applications Of Compressive Sensing Technique In Wireless Sensor Network

In order to reduce number of data transmission and to balance the load all over the network, the technique used is called Compressive Sensing. After using the pure compressive sensing technique the total number of transmission is too vast. So to reduce this hybrid compressive sensing method was proposed. The main intention is to decreases energy consumption and to increase the life cycle of the whole network.

Compressive Data Gathering in Wireless Sensor Networks

2016

The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs). Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compre...