Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree-Based Wireless Sensor Networks

Imon, Sk Kajal Arefin; Khan, Adnan; Francesco, Mario Di; Das, Sajal K.

doi:10.1109/tnet.2014.2331178

Cited by 65 publications

(20 citation statements)

References 26 publications

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“…Here, they considered aggregation in the network at every level of fat-tree and applies the "min-cost-max-flow" approach. Energy efficient algorithm is demonstrated in [15] for distributing the load over the network using randomized changing (switching). They extend the lifetime of data gathering tree in WSN by applying load balanced scheme.…”

Section: Related Workmentioning

confidence: 99%

Maximize the Lifetime of Sensor Network by Load Balancing Using Tree Topology

Patil¹,

Shetty²

2018

IJCT

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In many wireless sensor networks due to the limited energy of sensor nodes energy conservation is one of the most important challenges. To enhance the lifetime of the network emphasis is given to design energy efficient routing algorithms. In WSN, sensor nodes which are nearer to the base station having a task of collecting data for the entire area and send to the base station. This node has an additional load and depletes its energy faster. This paper addresses the problem of lifetime maximization by load balancing. This paper proposes energy efficient load balanced data collection algorithm considering different network parameter (e.g., density, degree). In this method, Data collection tree topology is built at the sink node. Performance of the proposed algorithm is evaluated by considering various parameters like topology, availability of resources and the energy utilization of nodes in different paths of the tree, which may vary and ultimately impacts the overall network lifetime. Sensor nodes are switched from their original path to other based on the load and it reduces communication overhead.

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Section: Related Workmentioning

confidence: 99%

Maximize the Lifetime of Sensor Network by Load Balancing Using Tree Topology

Patil¹,

Shetty²

2018

IJCT

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“…If V has some potential International Journal of Distributed Sensor Networks parents, a random decision is made based on its current switching probability (V ) through the switching decision function. If the outcome of the decision is to switch, a node Par V is chosen as the parent of node V through formula (20), and then the tree is updated by switching the subtree V rooted at node V to Par V . The new loads path ( , V ) and node ( , V ) of each V ∈ are updated (lines (14)- (16)).…”

Section: Details Of the Algorithmmentioning

confidence: 99%

“…This problem that is similar to the maximum-lifetime problem proposed in [18][19][20] is an NP-complete problem. The unique property of the problem is the energy consumption model of the node in data-gathering tree based on hybrid-CS model, which brings about new challenges for constructing data-gathering tree.…”

mentioning

confidence: 99%

Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing

Chen

Yang²,

Chen³

et al. 2016

International Journal of Distributed Sensor Networks

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Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As a technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless sensor networks for saving energy. Compressive sensing can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper, we investigate data gathering in wireless sensor networks using CS and aim at constructing a maximum-lifetime data-gathering tree. The lifetime of the network is defined as the number of data-gathering rounds until the first node depletes its energy. Based on the hybrid-CS data-gathering model, we first construct an arbitrary data-gathering tree and then use the random switching decision and optimal parent node selecting strategy to adjust the load of the bottleneck node and prolong the network lifetime. Simulation results show that the proposed algorithm outperforms several existing approaches in terms of network lifetime.

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“…On the other hand, it is defined as the earliest time at which some nodes in the network cease to cover their target area [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Routing Protocol for Maximum Lifetime in Wireless Sensor Networks

Abidoye¹,

X²

2018

IJITCS

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Abstract-Wireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the researchers and the practitioner communities due to their wide area of applications. These include real time sensing for audio delivery, imaging, video streaming, environmental monitoring, industrial applications and remote monitoring. WSNs are constrained with limited energy due to their physical size. In order to maximize network lifetime, efficient use of limited sensor nodes energy resources is important. Energy efficient routing protocol for maximum lifetime in wireless sensor networks (EERPM) is proposed. Sensor nodes lifetime optimization models are formulated subject to energy consumption constraint, data flow conservation constraint, maximum data rate constraint and link capacity constraint. The models are used to solve mathematical models for the maximum lifetime routing problems. Sensor nodes transmit their data packets based on the link capacity that is inference free among the sets of links. Moreover, algorithms are developed for coverage of sensor nodes and maximization of lifetime for sensor nodes. Simulation results show that EERPM performs better than MLCS, MLCAL and AEEC protocols. It can reduce data gathering latency and achieve load balancing. Finally, the proposed method extends network lifetime compared to the related selected protocols.

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Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree-Based Wireless Sensor Networks

Cited by 65 publications

References 26 publications

Maximize the Lifetime of Sensor Network by Load Balancing Using Tree Topology

Maximize the Lifetime of Sensor Network by Load Balancing Using Tree Topology

Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing

Energy Efficient Routing Protocol for Maximum Lifetime in Wireless Sensor Networks

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