Routing in scale-free networks based on expanding betweenness centrality

Guan, Zhi‐Hong; Chen, Long; Qian, Ting

doi:10.1016/j.physa.2010.10.002

Cited by 40 publications

(17 citation statements)

References 43 publications

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“…where is number of paths between nodes j and h passing through S i and is total number of paths between j and h. This definition was applied in [24,25] to propose an energy-aware dynamic routing strategy using local betweenness centrality.…”

Section: Optimal Ehs Placeme Tmentioning

confidence: 99%

Reliability based optimization in hybrid wireless sensor networks

Zonouz

Xing

Vokkarane

et al. 2015

2015 Annual Reliability and Maintainability Symposium (RAMS)

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Key Words: Hybrid wireless sensor networks, battery powered and energy harvesting sensor nodes, node placement SUMMARY & CO CLUSIO SSensor nodes in wireless sensor networks (WSNs) are usually battery-powered sensor nodes (BPSNs) and can rarely meet design goals such as network lifetime, energy and reliability. Energy-harvesting sensor nodes (EHSNs) are an alternative type of sensor nodes, which are capable of converting different types of energy to electrical energy. They have longer lifetime but higher cost than BPSNs. Combining BPSNs and EHSNs in a heterogeneous WSN has the potential to deal with the conflicting design goals. In addition, consider certain scenarios where replacing batteries of BPSNs is impossible, EHSNs can be deployed instead to satisfy the quality of service (QoS) of applications running on the hybrid WSN. In this work, we study a hybrid WSN including both types of sensor nodes to deal with the network reliability and cost requirements, particularly, the limited lifetime of BPSNs and high cost of EHSNs. The problem is to find the optimal locations for a specific number of EHSNs in the hybrid WSN with the objective to provide the best combination of average path reliability, implementation path cost, and average energy consumption based on the QoS requirements.Both the centrality concept and the multivariable nonlinear programming method are explored to find the optimal positions for EHSNs in the hybrid WSN. I TRODUCTIOWireless sensor networks (WSNs) bring significant advantages over traditional communications in today's applications such as environmental monitoring, structure monitoring, homeland security, health care and etc. Many important factors like energy consumption, implementation cost, and reliability can affect the design of WSNs [1][2][3][4].Sensor nodes in WSNs are usually battery-powered sensor nodes (BPSNs) and can rarely meet design goals such as network lifetime, energy and reliability. For example, BPSNs typically optimize their energy usage via decreasing their sampling frequency or duty-cycle so as to maximize their lifetime, which however can decrease the sensing reliability of the WSN.Extensive research has been performed to maximize the lifetime of BPSNs [5,6]. Examples include energy-aware MAC protocols [7-9] and system architectures [10-12], as well as energy-conservation by scheduling sleep intervals for some nodes [13]. Most of these techniques try to optimize energy usage to maximize the lifetime of a sensor node. However, with BPSNs, it is difficult to optimize all the performance parameters at the same time. For example, implementation cost will be increased using higher battery capacity, increasing transmission range causes higher energy requirement, decreasing transmission range implies more hops in the communication path to the sink node resulting in energy usage at more intermediate sensor nodes [5]. Energy-harvesting sensor nodes (EHSNs) are an alternative type of sensor nodes, which are capable of converting different types of energy (e.g., from environment ...

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Section: Optimal Ehs Placeme Tmentioning

confidence: 99%

Reliability based optimization in hybrid wireless sensor networks

Zonouz

Xing

Vokkarane

et al. 2015

2015 Annual Reliability and Maintainability Symposium (RAMS)

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“…Recent studies [22], [23], [24], [25] have reported that betweenness centrality plays an important role in the traffic on a network. For a given network, the betweenness centrality (BC) of a node i is defined as…”

Section: A Betweenness Centralitymentioning

confidence: 99%

“…where σ sd is the number of shortest paths going from s to d; and σ sd (i) is the number of shortest paths going from s to d and passing through node i. Till time t, the average number of packets passing through a given node i is then [22], [24], [25] RtB i N (N − 1)…”

Section: A Betweenness Centralitymentioning

confidence: 99%

Energy-balance routing for wireless sensor networks with scale-free characteristic

Fang

Chen

et al. 2012

Australasian Telecommunication Networks and Applications Conference (ATNAC) 2012

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In this paper, an energy-balance routing strategy is proposed for prolonging the network lifetime for wireless sensor networks with scale-free characteristic. In order to avoid the nodes with high traffic using up their energy too early and prolong the network lifetime, the new routing strategy adopts the shortest path routing algorithm on weighted wireless sensor networks, where the weight values are calculated based on betweenness centrality of the nodes. Since the nodes with large betweenness centrality consumed energy more quickly, the network lifetime can be prolonged by distributing energy consumption from nodes with large betweenness centrality to nodes with small betweenness centrality by increasing weight value on every link that connects the node with the highest betweenness centrality to other nodes. Simulation results show that the proposed routing strategy is better than the shortest path routing as regards extending the network lifetime and balancing the energy consumption.

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“…Transport capacity considerably relies on the routing strategies, network resource and underlying topologies. Thus, two main types of methods to improve network transport capacity are optimizing routing strategies [15][16][17][18][19][20][21][22] and establishing suitable underlying network infrastructures [23][24][25][26] for real communication systems.…”

Section: Introductionmentioning

confidence: 99%

Effective hybrid link-adding strategy to enhance network transport efficiency for scale-free networks

Bai

Liu

Zhang

2017

Int. J. Mod. Phys. C

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In this paper, we propose a new strategy (HLS) to enhance the transport capacity of scale-free networks by adding links to the existing networks, based on the betweenness of nodes, the shortest path length and the betweenness of links. Since only slight amounts of nodes in scalefree networks have high betweenness centrality, local link-adding strategy is adopted as a part of HLS for target nodes, which can signi¯cantly reduce the load of target nodes. Moreover, in order to improve the robustness of our strategy under some extreme cases, second sorting procedure is introduced in HLS. Simulation results show that HLS outperforms the IE strategy in terms of transport capacity and delivering ability of scale-free networks. After the adding links process of HLS, the congestion can be alleviated e±ciently, which is meaningful to the realistic networks.

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Routing in scale-free networks based on expanding betweenness centrality

Cited by 40 publications

References 43 publications

Reliability based optimization in hybrid wireless sensor networks

Reliability based optimization in hybrid wireless sensor networks

Energy-balance routing for wireless sensor networks with scale-free characteristic

Effective hybrid link-adding strategy to enhance network transport efficiency for scale-free networks

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