Topology control with coverage and lifetime optimization of wireless sensor networks with unequal energy distribution

Xenakis, Apostolos; Foukalas, Fotis; Stamoulis, George; Katsavounidis, Ioannis

doi:10.1016/j.compeleceng.2017.05.017

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…In formula (12), the parameter e s is 60 3 10 29 J/bit, in formula ( 13), the parameter e r is 135 3 10 29 J/bit, in formula ( 14), the parameter j mp is 0.0013 3 10 212 J/ bit/m 4 is a multipath transmission parameter, and the parameter j fs is 10 3 10 212 J/bit/m 2 is a common space transmission parameter. The control packet length is 200 bits, the parameter E elect is 45 3 10 29 J/ bit, the parameter l represents the data length of the sent data packet, and the parameter l is 4000 bits.…”

Section: Algorithm Simulation Comparison and Results Analysismentioning

confidence: 99%

“…Za´rraga-Rodrı´guez et al 11 presented a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies. Xenakis et al 12 used an unequal energy distribution algorithm, and provided the best effort coverage by arranging the nodes using topology control principles, formulated an optimization problem, and defined an objective function that incorporates both the network coverage and lifetime. Khedr et al 13 proposed a coverage-aware and efficient planar face topology structure (CAFT) for WSNs, and the proposed design is effective in reducing the energy consumption and thereby improves the WSN lifetime.…”

Section: Related Workmentioning

confidence: 99%

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A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Wang

You

Yue

et al. 2021

International Journal of Distributed Sensor Networks

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Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

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Section: Algorithm Simulation Comparison and Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Wang

You

Yue

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…A joint optimization problem, considering the network topology and unequal energy charge scheme for sensor nodes, has been formulated, and solved in order to extend the network lifetime and network coverage [18]. Authors proposed an energy-efficient traffic prioritization for medium access control protocol inside wireless body area networks.…”

Section: Related Workmentioning

confidence: 99%

Toward green computing in wireless sensor networks: prediction-oriented distributed clustering for non-uniform node distribution

Sikandar¹,

Agrawal²,

Tyagi³

et al. 2020

J Wireless Com Network

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Recently, researchers and practitioners in wireless sensor networks (WSNs) are focusing on energy-oriented communication and computing considering next-generation smaller and tiny wireless devices. The tiny sensor-enabled devices will be used for the purpose of sensing, computing, and wireless communication. The hundreds/thousands of WSNs sensors are used to monitor specific activities and report events via wireless communication. The tiny sensor-enabled devices are powered by smaller batteries to work independently in distributed environments resulting in limited maximum lifetime of the network constituted by these devices. Considering the non-uniform distribution of sensor-enabled devices in the next-generation mobility centric WSNs environments, energy consumption is imbalanced among the different sensors in the overall network environments. Toward this end, in this paper, a cluster-oriented routing protocol termed as prediction-oriented distributed clustering (PODC) mechanism is proposed for WSNs focusing on non-uniform sensor distribution in the network. A network model is presented, while categorizing PODC mechanism in two activities including setting cluster of nodes and the activity in the steady state. Further cluster set up activity is described while categorizing in four subcategories. The proposed protocol is compared with individual sensor energy awareness and distributed networking mode of clustering (EADC) and scheduled sensor activity-based individual sensor energy awareness and distributed networking mode of clustering (SA-ADC). The metrics including the overall lifetime of the network and nodes individual energy consumption in realistic next-generation WSNs environments are considered in the experimental evaluation. The results attest the reduced energy consumption centric benefits of the proposed framework PODC as compared to the literature. Therefore, the framework will be more applicable for the smart product development in the next-generation WSNs environments.

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“…The redundancy of sensor nodes in an area, firstly leads to waste the energy of some sensor nodes which causes to reduce the network lifetime and secondly, leads to overlap that area with a high probability while some areas may remain out of coverage [11][12][13][14]. Hence, in these networks in order to reduce the amount of overlap of sensor nodes and optimal coverage of the network and also reducing the energy consumption and prolonging the network lifetime, identifying the redundant nodes seems to be an essential problem [15][16][17][18]. Three main categories of coverage related problems can be identified into three classes: (i) target coverage, (ii) barrier coverage, and (iii) area coverage.…”

Section: -Introductionmentioning

confidence: 99%

Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata

Ghaffari¹,

Mousavi²

2021

JIST

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Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption.

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Topology control with coverage and lifetime optimization of wireless sensor networks with unequal energy distribution

Cited by 17 publications

References 10 publications

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Toward green computing in wireless sensor networks: prediction-oriented distributed clustering for non-uniform node distribution

Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata

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