Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks

Sheng, Zhengguo; Mahapatra, Chinmaya; Leung, Victor C. M.; Chen, Min; Sahu, Pratap Kumar

doi:10.1109/tcc.2015.2458272

Cited by 151 publications

(126 citation statements)

References 41 publications

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“…In terms of the energy model, we take the node residual energy into account, because the energy of the sensing nodes is inevitably consumed when sending and receiving data . Assume that residual energy of the node v is E v ( τ ), where v ∈ V and τ denotes τ th time slot.…”

Section: System Modelmentioning

confidence: 99%

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

Chen

et al. 2019

Struct Control Health Monit

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Summary Structural health monitoring (SHM) is a kind of data‐intensive applications for wireless sensors network (WSN), which usually requires a high network capacity. However, the bandwidth of traditional single‐radio single‐channel (SR‐SC) WSN is quite limited. In order to meet the requirement of structural monitoring, we investigate the multi‐radio multi‐channel multi‐power (MR‐MC‐MP) communication to improve the data collection performance of SHM‐oriented WSNs in terms of network capacity and power consumption. First, the data collection problem in MR‐MC‐MP WSNs is modeled as an optimization problem under the constraint of available time slots, radios, channels, and power levels. And then, combining the fast convergence of the particle swarm optimization (PSO) algorithm and high exploration performance of flower pollination optimization (FPA) algorithm, we propose a novel binary hybrid meta‐heuristic algorithm named BFPA‐PSO to solve the problem. In order to verify the advantage of the proposed BFPA‐PSO, some other meta‐heuristic algorithms are tested for the problem as well. Finally, several simulation experiments are carried out to test and compare the performance of different algorithms. Experiment results demonstrate that the proposed BFPA‐PSO algorithm has superior performance in terms of network capacity and energy consumption.

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Section: System Modelmentioning

confidence: 99%

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

Chen

et al. 2019

Struct Control Health Monit

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“…As a result, computing resources are optimally used [37]. Cloud-assisted monitoring of complex event is used to select the service access point scheme for QoS support under energy-efficient constraint.…”

Section: Load Balancing Techniquesmentioning

confidence: 99%

Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

Das

Darji

et al. 2018

Journal of Sensors

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The sensor cloud is a combination of cloud computing with a wireless sensor network (WSN) which provides an easy to scale and efficient computing infrastructure for real-time application. A sensor cloud should be energy efficient as the life of the battery in the sensor is limited and there is a huge consumption of energy in the data centre in running the servers to provide storage. In this paper, we have classified energy-efficient techniques for sensor cloud into different categories and analyzed each technology by using various parameters. Usage percentage of each parameter for every technology is calculated and for all technologies on average is also calculated. From our analysis, we found that most of the energy-efficient techniques ignore quality of service (QoS) parameters, scalability, and network lifetime. Multiparameter optimization including other QoS parameters along with energy may be the future direction of research. Our study will be helpful for researchers to get information regarding current methods used for an energy-efficient sensor cloud and also to build advanced systems in the future.

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“…[1][2][3] Compared to traditional senor networks (wired sensor networks), WSNs have many advantages in terms of deployment and maintenance. WSNs have permeated every corner of life and have become a main type of infrastructure in smart cities, industries, agriculture, and living.…”

Section: Introductionmentioning

confidence: 99%

An optimization method to improve the performance of unmanned aerial vehicle wireless sensor networks

Cao

Yang

Yue

et al. 2017

International Journal of Distributed Sensor Networks

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Wireless sensor networks have made great progress in recent years in every aspect of our life. To extend their range of application and provide a further effective option for remote surveillance, unmanned aerial vehicles have been gradually introduced into sensor networks, due to their advantages of flexibility, mobility, and ease of realization. Despite the success of various applications and studies in this new field, unmanned aerial vehicle-wireless sensor network still faces many open challenges, such as the unmanned aerial vehicle capable system framework, land-wireless sensor network management, and unmanned aerial vehicle mission planning strategies. In the article, we propose a cooperative framework for unmanned aerial vehicle-wireless sensor network, which is composed of sensor nodes, fixed-group leaders, and a unmanned aerial vehicle-Sink, in which a three-layer hierarchical network is formed. A land-wireless sensor network k-means driven grouping approach is then presented, which considers the communication performance, the position, and other factors. Additionally, a simulated annealing algorithm is employed to detect the optimal flight trajectory according the ground wireless sensor network architecture. Finally, the proposed approach is compared to other related approaches, and the results have shown better performance of our proposal in terms of energy consumption, flying time, and other relevant evaluation criteria.

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Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks

Abstract: Energy efficient cooperative computing in mobile wireless sensor networks

Cited by 151 publications

References 41 publications

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

An optimization method to improve the performance of unmanned aerial vehicle wireless sensor networks

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