MAP : A Balanced Energy Consumption Routing Protocol for Wireless Sensor Networks

Azim, Mohamed Mostafa A.

doi:10.3745/jips.2010.6.3.295

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…In order to widen the analysis, we also compare the performance of the LA, CM, and CT routing processes with state of the art alternatives that especially target the optimization of energy and the hop count in flat sensor networks. In the evaluations shown in this paper, we considered the Maximum Available Power (MAP) [6], and the Minimum Hop Routing (MHR) [7] schemes. These fall under the same category of the routing scheme described in this paper, i.e., under the family of source-initiated routing protocols.…”

Section: Performance Evaluationmentioning

confidence: 99%

Improving learning automata-based routing in Wireless Sensor Networks

Ahvar

Yannuzzi

Serral-Gracià

et al. 2012

2012 IEEE Global Communications Conference (GLOBECOM)

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Recent research in the field of Wireless Sensor Networks (WSNs) has demonstrated the advantages of using learning automata theory to steer the routing decisions made by the sensors in the network. These advantages include aspects such as energy saving, energy balancing, increased lifetime, the selection of relatively short paths, as well as combinations of these and other goals. In this paper, we propose a very simple yet effective technique, which can be easily combined with a learning automaton to dramatically improve the performance of the routing process obtained with the latter. As a proof-of-concept, we focus on a typical learning automata-based routing process, which aims at finding a good trade off between the energy consumed and the number of hops along the paths chosen. In order to assess the performance of this routing process, we apply it on a WSN scenario where a station S gathers data from the sensors. In this typical WSN setting, we show that our combined technique can significantly improve the decisions made with the automata; and more importantly, even though the proof-of-concept particularizes somehow the automata and their behavior, the technique described in this paper is general in scope, and therefore can be applied under different routing methods and settings using learning automata.

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Section: Performance Evaluationmentioning

confidence: 99%

Improving learning automata-based routing in Wireless Sensor Networks

Ahvar

Yannuzzi

Serral-Gracià

et al. 2012

2012 IEEE Global Communications Conference (GLOBECOM)

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“…Therefore, the intermediate nodes use the unintended energy for the packet forwarding of the source node, which induces high energy consumption of the nodes and thus accelerates network partitioning. Therefore, the energy efficiency of the nodes is the key factor that affects the network performance in distributed networks for IoT [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Probabilistic Routing Algorithm for Internet of Things

Park¹,

Cho²,

Lee

2014

Journal of Applied Mathematics

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In the future network with Internet of Things (IoT), each of the things communicates with the others and acquires information by itself. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose energy-efficient probabilistic routing (EEPR) algorithm, which controls the transmission of the routing request packets stochastically in order to increase the network lifetime and decrease the packet loss under the flooding algorithm. The proposed EEPR algorithm adopts energy-efficient probabilistic control by simultaneously using the residual energy of each node and ETX metric in the context of the typical AODV protocol. In the simulations, we verify that the proposed algorithm has longer network lifetime and consumes the residual energy of each node more evenly when compared with the typical AODV protocol.

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“…Major components of wireless sensor networks include sensor nodes that measure or detect some objects through sensors and transmit the information to wireless networks, sink nodes that collect measured values from numerous sensor nodes and transmit the measured values to middleware, and relay nodes that relay information between sensor nodes and sink nodes. Those pieces of information detected by sensor nodes are delivered to sink nodes through networks and then again delivered by sink nodes to middleware or servers [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Visualization of dynamic fault tolerance rerouting for data traffic in wireless sensor network

Jeong

Park

2012

Int J Communication

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In wireless sensor network environments, a phenomenon of data concentration may occur in one or certain sensor nodes in the process of transmitting sensing data from a source sensor node to a sink node. In this case, the overhead that occurs in the sensor node affects the performance of the entire sensor network. In addition, in the sensor network, excessive sensing data traffic or data loss may occur depending on the variability of the topology of the sensor network. In this paper, visualization for dynamic rerouting is designed and implemented, which visibly provides packet movement routes between sensor nodes and transmitted packet traffic transmission capacity to Geography Markup Language-based maps having GPS coordinate information. A mechanism for the visualization for dynamic rerouting to detect sensing data overheads and sensor node faults occurring in sensor networks and dynamically rerouting of data is proposed. In addition, information on rerouting route paths from source sensors to sink nodes is visually provided.

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MAP : A Balanced Energy Consumption Routing Protocol for Wireless Sensor Networks

Cited by 13 publications

References 9 publications

Improving learning automata-based routing in Wireless Sensor Networks

Improving learning automata-based routing in Wireless Sensor Networks

Energy-Efficient Probabilistic Routing Algorithm for Internet of Things

Visualization of dynamic fault tolerance rerouting for data traffic in wireless sensor network

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