Distributed Adaptive Sampling, Forwarding, and Routing Algorithms for Wireless Visual Sensor Networks
Distributed Adaptive Sampling, Forwarding, and Routing Algorithms for Wireless Visual Sensor Networks
The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this paper focuses on the adaptive sampling, forwarding, and routing actions of each node in order to maximise the information value of the data collected. These actions are inter-related in this setting because each node's energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting, and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose, and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared to the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured.
Decentralised mechanism, distributed constraint optimization, information metric, inter-related adaptive sampling and routing
63-70
Kho, Johnsen
4f64afa9-b327-4930-8adf-a09b2dd51085
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
10 May 2009
Kho, Johnsen
4f64afa9-b327-4930-8adf-a09b2dd51085
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Kho, Johnsen, Tran-Thanh, Long, Rogers, Alex and Jennings, Nick
(2009)
Distributed Adaptive Sampling, Forwarding, and Routing Algorithms for Wireless Visual Sensor Networks.
Third International Workshop on Agent Technology for Sensor Networks, a workshop of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-09), Budapest, Hungary.
10 - 15 May 2009.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this paper focuses on the adaptive sampling, forwarding, and routing actions of each node in order to maximise the information value of the data collected. These actions are inter-related in this setting because each node's energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting, and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose, and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared to the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured.
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Published date: 10 May 2009
Additional Information:
Event Dates: May 10-15, 2009
Venue - Dates:
Third International Workshop on Agent Technology for Sensor Networks, a workshop of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-09), Budapest, Hungary, 2009-05-10 - 2009-05-15
Keywords:
Decentralised mechanism, distributed constraint optimization, information metric, inter-related adaptive sampling and routing
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 267196
URI: http://eprints.soton.ac.uk/id/eprint/267196
PURE UUID: b5f8711a-b967-4123-9872-bb80bd78aacc
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Date deposited: 19 Mar 2009 18:21
Last modified: 14 Mar 2024 08:45
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Contributors
Author:
Johnsen Kho
Author:
Long Tran-Thanh
Author:
Alex Rogers
Author:
Nick Jennings
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