Efficient computation of optimal assignments for distributed tasks
Journal of Parallel and Distributed Computing
Task Allocation Algorithms for Maximizing Reliability of Distributed Computing Systems
IEEE Transactions on Computers
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
IEEE Transactions on Parallel and Distributed Systems
Optimization of Task Allocation in a Cluster-Based Sensor Network
ISCC '03 Proceedings of the Eighth IEEE International Symposium on Computers and Communications
Energy-balanced task allocation for collaborative processing in wireless sensor networks
Mobile Networks and Applications
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
A survey of application distribution in wireless sensor networks
EURASIP Journal on Wireless Communications and Networking
IEEE Communications Magazine
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Sensor networks are increasingly being used for applications which require fast processing of data, such as multimedia processing and collaboration among sensors to relay observed data to a base station (BS). Distributed computing can be used on a sensor network to reduce the completion time of a task (an application) and distribute the energy consumption equitably across all sensors, so that certain sensors do not die out faster than the others. The distribution of task modules to sensors should consider not only the time and energy savings, but must also improve reliability of the entire task execution. We formulate the above as an optimization problem, and use the A^* algorithm with improvements to determine an optimal static allocation of modules among a set of sensors. We also suggest a faster algorithm, called the greedy A^* algorithm, if a sub-optimal solution is sufficient. Both algorithms have been simulated, and the results have been compared in terms of energy savings, decrease in completion time of the task, and the deviation of the sub-optimal solution from the optimal one. The sub-optimal solution required 8%-35% less computation, at the cost of 2.5%-15% deviation from the optimal solution in terms of average energy spent per sensor node. Both the A^* and greedy A^* algorithms have been shown to distribute energy consumption more uniformly across sensors than centralized execution. The greedy A^* algorithm is found to be scalable, as the number of evaluations in determining the allocation increases linearly with the number of sensors.