Energy aware self-organizing density management in wireless sensor networks

Merrer, Erwan Le; Gramoli, Vincent; Kermarrec, Anne-Marie; Viana, Aline Carneiro; Bertier, Marin

doi:10.1145/1161252.1161259

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…The design of TITAN is based on the trade-offs between waking up power-saving nodes on shorter routes and using longer routes that contain active nodes. While the aforementioned approaches target at forwarding and routing services, SAND [Merrer et al 2006] coordinates the node sleep and wakeup cycles for sensing and/or routing states, which is more in tune with the requirements of wireless sensor networks.…”

Section: Topology Managementmentioning

confidence: 99%

A survey of adaptive services to cope with dynamics in wireless self-organizing networks

2012

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In this paper, we consider different types of wireless networks that benefit from-and, in certain cases, require-self-organization. Taking mobile ad hoc, wireless sensor, wireless mesh, and delay-tolerant networks as examples of Wireless Self-Organizing Networks (WSONs), we identify that common challenges these networks face are mainly due to lack of centralized management, device heterogeneity, unreliable wireless communication, mobility, resource constraints, or the need to support different traffic types. In this context, we survey several adaptive services proposed to handle these challenges. In particular, we group the adaptive services as core services and network-level services. By categorizing different types of services that handle adaptation and the types of adaptations, we intend to provide useful design guidelines for achieving self-organizing behavior in network protocols. Finally, we discuss open research problems to encourage the design of novel protocols for WSONs.

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Section: Topology Managementmentioning

confidence: 99%

A survey of adaptive services to cope with dynamics in wireless self-organizing networks

2012

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“…This method requires, however, a good knowledge of the overall network. In a similar way, authors in [18] propose to switch the nodes' energy state into sleep, forwarding, or sensing-only. The proposed method relies on a distributed probing approach and on the redundancy resolution of sensors for getting energy optimizations.…”

Section: Management Of Node Activity By State Switchingmentioning

confidence: 99%

“…Recently, interesting researches have been facing this requirement by focusing on the extension of the entire network lifetime. In a global point of view, these researches: -Switch nodes' energy level between sleep and awake states [5,8,15,16,18,21,22]. -By keeping nodes in the active state, perform power control [2,7,13] or energy-aware routing [1,3,4,11,12,14].…”

Section: Introductionmentioning

confidence: 99%

Looking for network functionalities’ extension by avoiding energy-compromised hotspots in wireless sensor networks

2008

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The vast literature on the wireless sensor research community contains many valuable proposals for managing energy consumption, the most important factor that determines sensors' lifetime. Interesting researches have been facing this requirement by focusing on the extension of the entire network lifetime: either by switching between node states (active, sleep) or by using energy-efficient routing. We argue that a better extension of the network lifetime can be obtained if an efficient combination of management mechanisms can be performed at the energy of each single sensor and at the load distribution over the network. Considering these two accuracy levels (i.e., node and network), this paper presents a new approach that uses cost functions to choose energy-efficient routes. In particular, by making different energy considerations at a node level, our approach distributes routing load, avoiding, thus, energy-compromised hotspots that may cause network disconnections. The proposed cost functions have completely decentralized and adaptive behavior and take into consideration the end-to-end energy consumption, the remaining energy of nodes, and the number of transmissions a node can make before its energy depletion. Our simulation results show that, though slightly This research was financed by the ANR-OCARI project.

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“…This methods requires a good knowledge of the overall network. In a similar way, authors in [11] propose to switch nodes energy state into sleep, forwarding, or sensing-only. The proposed method relies on a distributed probing approach and on the redundancy resolution of sensors for getting energy optimizations.…”

Section: Management Of Nodes Activity By State Switchingmentioning

confidence: 99%

“…• switch nodes' energy level between sleep and awake states [4,2,6,15,11] or • by keeping nodes in the active state, perform power control [9, 3, I] or energyaware routing [10,13,8,5,14,16].…”

Section: Introductionmentioning

confidence: 99%

Avoiding energy-compromised hotspots in resource-limited wireless networks

2008

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The vast literature on the wireless sensor research community contains many valuable proposals far managing energy consumption, the most important factor that determines sensors lifetime. Interesting researches have been facing this requirement by focusing on the extension of the entire netwark lifetime: either by switching between node states (active, sleep), or by using energy efficient routing. We argue that a better extension of the network lifetime can be obtained if an efficient combination of management mechanisms can be performed at the energy of each single sensor and at the load distribution over the network. Considering these two accuracy levels (i.e., node and network), this paper presents a new approach that uses cost functions to choose energy efficient routes. In particular, by making different energy considerations at anode level, our approach distributes routing load, avoiding thus, energy-compromised hotspots that may cause network disconnections. The proposed cost functions have completely decentralized and adaptive behavior and take into consideration: the end-to-end energy consumption, the remaining energy of nodes, and the number of transmissions anode can make before its energy depletion. Our simulation results show that, though slightly increasing path lengths from sensor to sink nodes, the proposed scheme (I) improves significantly the network lifetime for different neighborhood densities degrees, while (2) preserves network connectivity for a longer period of time.

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Energy aware self-organizing density management in wireless sensor networks

Cited by 7 publications

References 13 publications

A survey of adaptive services to cope with dynamics in wireless self-organizing networks

A survey of adaptive services to cope with dynamics in wireless self-organizing networks

Looking for network functionalities’ extension by avoiding energy-compromised hotspots in wireless sensor networks

Avoiding energy-compromised hotspots in resource-limited wireless networks

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