Predictive QoS routing to mobile sinks in wireless sensor networks

We present an algorithm for data delivery to mobile sinks in wireless sensor networks. Our algorithm is based on information potentials, which we extend to account for mobility. We show that for local movement along edges in the communication graph, the information potentials can be adapted using a...

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Vydáno v:2009 International Conference on Information Processing in Sensor Networks s. 109 - 120
Hlavní autoři: Kusy, Branislav, Lee, HyungJune, Wicke, Martin, Milosavljevic, Nikola, Guibas, Leonidas
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Washington, DC, USA IEEE Computer Society 13.04.2009
IEEE
Edice:ACM Conferences
Témata:
Applied computing > Physical sciences and engineering > Mathematics and statistics
Data mining
Delay
Distributed computing
Hardware > Communication hardware, interfaces and storage
Iterative algorithms
Maintenance
Mathematics of computing > Discrete mathematics
Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
Mathematics of computing > Discrete mathematics > Graph theory > Paths and connectivity problems
Mobile communication
Mobile Routing
Networks
Networks > Network protocols > Network layer protocols > Routing protocols
Networks > Network types > Mobile networks
Networks > Network types > Wireless access networks
Relays
Routing
Sensor Networks
Training data
Wireless sensor networks
ISBN:1424451086, 9781424451081
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Shrnutí:We present an algorithm for data delivery to mobile sinks in wireless sensor networks. Our algorithm is based on information potentials, which we extend to account for mobility. We show that for local movement along edges in the communication graph, the information potentials can be adapted using a simple iterative distributed computation. However, for non-local movement, the potential field might change significantly, and iterative computation leads to packet loss and packet delivery delays. We address this problem by introducing the mobility graph, which encodes knowledge about likely mobility patterns within the network. The mobility graph can be extracted from training data and is used to predict future relay nodes for the mobile node. Using the mobility graph, we can precompute and efficiently store additional routing states in the network. This enables the algorithm to maintain uninterrupted data streams. We analyze the benefits of computing and maintaining a mobility graph, and show that the information contained therein can be used to improve routing reliability in experiments involving mobile sinks.
ISBN:1424451086
9781424451081
DOI:10.5555/1602165.1602177