Relay node placement based on balancing power consumption in wireless sensor networks

Lu, Kezhong; Liu, Gang; Mao, Rui; Feng, Yan

doi:10.1049/iet-wss.2010.0004

Cited by 24 publications

(13 citation statements)

References 19 publications

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“…Relay nodes are deployed between the sensing area and the base station. Many studies suggest how to deploy relay nodes and determine routes, in order to prolong the network lifetime [Xing et al 2004;Hua and Yum 2008;Kuruvila et al 2006;Cheng et al 2003;Liu et al 2007Liu et al , 2011Vidhyapriya and Vanathi 2007;Ghneimat et al 2011].…”

Section: Transmission Methodsmentioning

confidence: 99%

“…In Lu et al [2011], the authors study the problem of deploying a minimum number of relay nodes in the sensing field in order to balance power consumption among all sensing nodes and relay nodes. In Cheng et al [2003], the authors present two routing algorithms to prolong the network lifetime by minimizing the maximum transmission power of individual nodes and assigning edge weights based on remaining energy of sending nodes.…”

Section: Transmission Methodsmentioning

confidence: 99%

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Energy-efficient data dissemination using beamforming in wireless sensor networks

Feng

Jung

et al. 2013

ACM Trans. Sen. Netw.

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Energy conservation is essential in Wireless Sensor Networks (WSNs) because of limited energy in nodes' batteries. Collaborative beamforming uses multiple transmitters to form antenna arrays; the electromagnetic waves from these antenna arrays can create constructive interferences at the receiver and increase the transmission distance. Each transmitter can use lower power and save energy, since the energy consumption is spread over multiple transmitters. Each beamforming transmission requires multiple collaborative transmitters. Repetitively using the same transmitters will deplete their energy and create coverage holes in the sensing area. To prevent holes, energy should be balanced by using different transmitters. This article investigates the factors that can affect the energy consumption and network lifetime when using beamforming. We present an algorithm for selecting the transmitters in order to prolong the network lifetime. Compared with an existing beamforming transmitters scheduling algorithm, our algorithm doubles the network lifetime. When compared with direct transmission or multihop transmissions towards a receiver far away from the sensing area, our approach can increase the network's lifetime substantially.

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Section: Transmission Methodsmentioning

confidence: 99%

Section: Transmission Methodsmentioning

confidence: 99%

Energy-efficient data dissemination using beamforming in wireless sensor networks

Feng

Jung

et al. 2013

ACM Trans. Sen. Netw.

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“…Wang [1] considered the sensing field as an arbitrary-shaped region possibly with obstacles in the sensor placement, proposed the placement algorithm without care of the relationship between the communication range and sensing range, the key idea is to partition the sensing field into smaller sub-regions and to deploy sensors in these sub-regions. LU [2] and other scholars concerned with uneven power consumption of the sensors nearby the sink, and deduced a relay node density function according to which relay nodes are placed in the sensing field. Misra [3] and other scholars studied constrained versions of the relay node placement problem, where relay nodes can only be placed at a subset of candidate locations.…”

Section: Related Workmentioning

confidence: 99%

A Distributed and Shortest-Path-Based Algorithm for Maximum Cover Sets Problem in Wireless Sensor Networks

Zeng

Yao

Chen

et al. 2011

2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications

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In wireless sensor networks, there exist many redundant sensor nodes, and activating only the necessary number of sensor nodes at any particular moment can save energy, while ensuring all targets covered and network connectivity. In this paper, we first introduce a distributed scheme for sink nodes to find K paths to each sensor nodes. Secondly, a shortestpath-based algorithm is presented for the maximum set covers problem in wireless sensor networks. The algorithm partitions all nodes into possibly maximum disjointed sets, and the nodes in each set have all targets covered while ensuring the network connectivity. In the proposed algorithm, when constructing a cover set, the key idea is to select a node joining into the set if it has the shortest path to the nodes which is already in the set. At last, simulation is done, and the result shows that the proposed algorithm outperforms others.

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“…Each node contains its own Battery, Processor, Time Synchronizer, Sensor, Storage and Transceiver. The sensor nodes are usually operated by Dr. A. Suruliandi, Associate Professor, Department of Computer Science and Engineering, Manonmanium Sundaranar University, Abhishekapatti, suruliandi@yahoo.com lightweight batteries that are difficult to replace or recharge, energy becomes one of the most crucial resources in WSNs [14]. Interconnection is facilitated via the transceiver.…”

Section: Inrroductronmentioning

confidence: 99%

Performance evaluation of the energy efficient localization techniques in WSN

Ranjithamani

Suruliandi

2012

2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12)

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Wireless sensor network (WSN) requires information about th e geograph ical location of th e sensor nodes as the sensor nodes organize themselves in a large-s cale adhoc network. The ess entials for WSN are the energy efficiency and nodes location identification. Minimizing the energy consumption to improve energy efficient and fixing the accurate location of a mobile nodes are two different criteria for WSN. In this scenario it is ess ential to identify the protocols which perform better. This paper provides an overview of th e energy efficient localization for es timating the positional accuracy with less power consumption. Th e performance of Particle swarm optimization (PSO), Relay node placement sch eme (RNPS) and Mobility prediction model (MPM) is discussed and evaluated. All th e th ree models are analyzed and the simulated results are provided. Performance metrics such as energy efficiency, energy consumption, avg. end to end network delay, packet delivery ratio, and throughput are evaluated. Performance analysis states that the mobility prediction model gives a better performance th an the other two methods.

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Relay node placement based on balancing power consumption in wireless sensor networks

Cited by 24 publications

References 19 publications

Energy-efficient data dissemination using beamforming in wireless sensor networks

Energy-efficient data dissemination using beamforming in wireless sensor networks

A Distributed and Shortest-Path-Based Algorithm for Maximum Cover Sets Problem in Wireless Sensor Networks

Performance evaluation of the energy efficient localization techniques in WSN

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