Load Balancing Routing for Wireless Sensor Network in 2D Mesh

Chen, Yipiao; Yang, Yi; Deng, Yubo; Li, Lian

doi:10.1109/chinagrid.2012.16

Cited by 1 publication

(4 citation statements)

References 23 publications

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“…The advantage of the application is that it was tested in a real environment at the Technical University of Madrid using three vehicles equipped with nodes MTM-CM3100 which are based on the TelosB platform. Marginal mobility Waiting for destination Following the destination V2V [119] Data rate and coverage Modified CTP VCRB [120] Traffic threshold Virtual cracky rectangular block Integer linear programming [121] Nodes' position Weight heuristic algorithm…”

Section: Meshesmentioning

confidence: 99%

“…The problem of load balancing in mesh topologies is studied in Ref. [120]. The authors find that the nodes located near the middle of the network bear a heavier workload which results in congestion, packet loss, and shorter node lifetime.…”

Section: Meshesmentioning

confidence: 99%

“…Each node sends a data packet to its parent every X seconds, where X is a random number between [60,120] seconds. The nodes send data packets using aperiodic intervals of time to prevent the nodes from synchronizing and transmitting at the same time, which causes traffic congestion.…”

Section: Data Collectionmentioning

confidence: 99%

“…Besides, the transmission range is 50 meters, and the interference range is 100 meters. During the data collection, each node sends a data packet every X seconds, where X is a random number between [60,120] seconds; the packet payload is 8 bytes. We run 20 emulations with different seeds and average the results.…”

Section: Performance Evaluation Settingsmentioning

confidence: 99%

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A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

Salas¹

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Proponemos un marco conceptual para acoplar técnicas de auto-organización y técnicas de autocuración. A este marco se le llama de auto-curación y es capaz de hacer frente a enlaces inalámbricos inestables y nodos defectuosos. Dividimos el marco en dos componentes principales: la auto-organización y auto-curación. En el componente de auto-organización, nosotros construimos una topología de árbol que determine las rutas hacia el sumidero. En el componente de auto-curación, la topología del árbol se adapta a ambos tipos de fallas siguiendo tres pasos: recopilación de información, detección de fallas, y la recuperación de fallos. En el paso de recopilación de información, los nodos determinan el estado actual de la red mediante la recopilación de información de la capa MAC. En el paso de detección de fallas, los nodos analizan la información recopilada y detectan nodos/enlaces defectuosos. En el paso de recuperación de fallos, los nodos recuperan la topología del árbol mediante la sustitución de componentes defectuosos con redundantes (es decir, componentes de respaldo). Este marco permite una red con resiliencia que se recupera sin agotar los recursos de la red.

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Section: Meshesmentioning

confidence: 99%

Section: Meshesmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Performance Evaluation Settingsmentioning

confidence: 99%

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