Balancing Energy Consumption for Clustered Wireless Sensor Networks Utilizing Compressed Sensing

Clustering in wireless sensor networks is an efficient way to structure and organize the network. It aims at identifying a subset of nodes within the network and binding it to a leader (i.e., cluster head). The leader becomes in charge of specific additional tasks like gathering data from all nodes in its cluster and sending them using a longer range communication to a sink. As a consequence, a cluster head exhausts its battery more quickly than regular nodes. In this paper, we present four variants of BLAC, a novel battery level aware clustering family of schemes. BLAC considers the battery level combined with another metric to elect the cluster-head. The cluster-head role is taken alternately by each node to balance energy consumption. Due to the local nature of the algorithms, keeping the network stable is easier. BLAC aims at maximizing the time with all nodes alive to satisfy the application requirements. Simulation results show that BLAC improves the full network lifetime three times more than the traditional clustering schemes by balancing energy consumption over nodes and still deliveres high data ratio.

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Balancing Energy Consumption in Wireless Sensor Networks Using Fuzzy Artificial Bee Colony Routing Protocol

INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY ◽

10.24297/ijmit.v7i2.3354 ◽

2013 ◽

Vol 7 (2) ◽

pp. 1018-1032

Author(s):

Imad S. Alshawi

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Network Lifetime ◽

Routing Protocol ◽

Energy Efficient ◽

Artificial Bee Colony ◽

Wireless Sensor ◽

Bee Colony ◽

Balancing Energy

Energy is an extremely critical resource for battery-powered Wireless Sensor Networks (WSNs), thus making energy-efficient protocol design a key challenging problem. Most of the existing routing protocols always forward packets along the minimum energy paths to merely minimize energy consumption, which causes an Uneven Energy Consumption (UEC) problem and eventually results in a network partition. Due to the limited energy resources of sensor nodes, selecting an appropriate routing protocol can be significantly improve overall performance especially energy awareness in WSNs. Therefore, this paper proposes an energy-efficient routing protocol called Fuzzy Artificial Bee Colony Routing Protocol (FABCRP) which is capable of finding the optimal routing path form the source to the destination by favoring some of routing criteria and balancing among them to prolong the network lifetime. To demonstrate the effectiveness of FABCRP in terms of balancing energy consumption and maximization of network lifetime, we compare it with Fuzzy approach, ABC algorithm and Fuzzy_A-star approach using the same criteria in two different topographical areas. Simulation results show that the network lifetime achieved by FABCRP could be increased by nearly 35%, 30%, and 15% more than that obtained by Fuzzy, ABC and Fuzzy_A-star respectively.

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Mixed Communication Method on the Grid-Based for Wireless Sensor Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.686.417 ◽

2014 ◽

Vol 686 ◽

pp. 417-422

Author(s):

Zhu Yan ◽

Xi Ne You ◽

Ran Yan

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Network Lifetime ◽

Wireless Sensor ◽

Cluster Radius ◽

Communication Method ◽

Balancing Energy ◽

Grid Based ◽

Optimal Cluster

When senors transmit their data to the sink via multi-hop communication, the sensors closer to the sink are burdened with heavy relay traffic and tend to die early. On the contrary, if all sensors transmit datas to the sink via single-hop communication, the sensors further from the sink will die much more quickly than those closer to the sink. In this paper, we first develop an analytical model to derive the optimal cluster radius. Then we propose a mixed communication method on grid-based where the sensors can transmit data to the sink in either single-hop or multi-hop. Finally, we conduct extensive experiments and show that our method outperforms LEACH and HEED in terms of network lifetime by balancing energy consumption.

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