A distributed multi-path routing algorithm to balance energy consumption in wireless sensor networks

Laouid, Abdelkader; Dahmani, Abdelnasser; Bounceur, Ahcène; Euler, Reinhardt; Lalem, Farid; Tari, Abdelkamel

doi:10.1016/j.adhoc.2017.06.006

Cited by 59 publications

(25 citation statements)

References 11 publications

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“…In order to maintain the balance of energy consumption among nodes, each node in the cluster should have a relatively equal opportunity to serve as cluster head. That is to say, the T/k cluster members in the cluster have a chance to act as cluster head in the next T/k round communication process [19,20].…”

Section: Cluster Head Selection and Rotating Probability Modelmentioning

confidence: 99%

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

Bin

Sun

2020

Sensors

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The rapid increase of train speed has brought greater challenges to the safety and reliability of railway systems. Therefore, it is necessary to monitor the operation status of trains, infrastructure, and their operating environment in real time. Because the operation environment of railway systems is complex, the construction cost of wired monitoring systems is high, and it is difficult to achieve full coverage in the operation area of harsh environments, so wireless sensor networks are suitable for the status monitoring of railway systems. Energy resources of nodes are the basis of ensuring the lifecycle of wireless sensor networks, but severely restrict the sustainability of wireless sensor networks. A construction method of special wireless sensor networks for railway status monitoring, and an optimal energy resources allocation method of wireless sensor networks for intelligent railway systems are proposed in this paper. Through cluster head selection and rotating probability model, clustering generation and optimization model, and partial coverage model, the energy consumption of nodes can be minimized and balanced. The result of simulation experiment proved that the lifetime of wireless sensor networks can be maximized by the optimal energy resources allocation method based on clustering optimization and partial coverage model, based on polynomial time algorithm.

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Section: Cluster Head Selection and Rotating Probability Modelmentioning

confidence: 99%

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

Bin

Sun

2020

Sensors

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“…Abdelkader et al [1] proposed a multipath routing algorithm based on the residual energy of the network. This setting is based on the number of hops in each node to find the easiest path and place it in the routing table.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Optimized Routing Protocol Based on Binary Particle Swarm Optimization Successive Interference Cancellation for WSN

Verma*,

Gupta

2020

IJITEE

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A large variety of Wireless Sensor Network (WSN) applications encourage researchers to develop and improve protocols and algorithms for rising challenges. During data transmission, energy consumption is main issue because sensor nodes have limited energy capacity. In fact, WSN needs load balancing algorithms that keep use of the limited energy source to route the collected data to the receiving node. While considering adhoc network in sensitive areas, one amongst the necessary aspects to consider is energy as a result of while sending information all communicating nodes exhausts its battery life. For mobile nodes in adhoc situation one and only one source of energy is battery. As compared to single path multipath routing helps to find best path that needs less energy and enhances the network life. In this paper an energy efficient routing protocol is proposed. In routing algorithm, route that have shortest path among multipaths chosen by particle swarm optimization algorithm. Among these shortest paths, that path is chosen that need minimum route selection parameter. The proposed algorithm is based on selection of energy efficient paths from multiple shortest SIC path from source to destination. For selection Binary Particle Swarm Optimization (BPSO) algorithm is applied. The Binary Particle Swarm Optimization (BPSO) algorithm selects energy efficient route among different shortest paths. The k-shortest route is selected on the basis of bandwidth and minimum SIC. The performance of the proposed algorithm is compared with multipath Ad hoc On-Demand Distance Vector (AODV) routing protocol and concluded that BPSO optimized energy efficient path is more efficient with respect to remaining energy of the network. The result is analyzed with variable number of packets send.

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“…For the energy consumption model of the node, we can refer to Joo. 55 In the process of transmission, we mainly have the following three types of data packets, which are the data packet containing the information collected by the node, the ACK packet, and CTS packet responsible for the communication between the secondary nodes. 65,66 Since the ACK packet and the CTS packet itself are small, and the energy consumed by the node when receiving these packets is much less than the energy of the transmitted packets.…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Adaptive duty cycle control–based opportunistic routing scheme to reduce delay in cyber physical systems

Xiang

Liu

et al. 2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks are the foundation of cyber physical systems. In loss and delay sensitive wireless sensor networks, there is much room for improving the performance of opportunistic routing scheme. In this article, an adaptive duty cycle control-based opportunistic routing scheme was put forward to improve the performance of the opportunistic routing scheme. Our main work is as follows: (a) the proposed duty cycle control-based opportunistic routing scheme implements the opportunistic routing in the duty cycle-based wireless sensor networks. Compared with the opportunistic routing scheme in the non-duty cycle mode, the duty cycle control-based opportunistic routing scheme can effectively reduce the energy consumption by 55.64%, although the performance in the delay is degraded. (b) Next, adaptive duty cycle control-based opportunistic routing scheme is proposed to reduce the delay of nodes in duty cycle control-based opportunistic routing. In adaptive duty cycle control-based opportunistic routing scheme, the node's duty cycle is increased in the area where the energy has remaining, so that the network delay can be reduced without reducing the network lifetime. Compared with duty cycle control-based opportunistic routing scheme, the maximum end-toend delay of the node in the network can be reduced by 69.83%.

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A distributed multi-path routing algorithm to balance energy consumption in wireless sensor networks

Cited by 59 publications

References 11 publications

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

Energy Optimized Routing Protocol Based on Binary Particle Swarm Optimization Successive Interference Cancellation for WSN

Adaptive duty cycle control–based opportunistic routing scheme to reduce delay in cyber physical systems

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