Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
A smooth method for the finite minimax problem
Mathematical Programming: Series A and B
An overview of derivative estimation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Time synchronization in ad hoc networks
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
A taxonomy of wireless micro-sensor network models
ACM SIGMOBILE Mobile Computing and Communications Review
Fine-grained network time synchronization using reference broadcasts
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Slotted Aloha as a game with partial information
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed optimal self-organization in ad hoc wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Joint energy management and resource allocation in rechargeable sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
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This work is motivated by the need for an ad hoc sensor network to autonomously optimise its performance for given task objectives and constraints. Arguing that communication is the main bottleneck for distributed computation in a sensor network we formulate two approaches for optimisation of computing rates. The first is a team problem for maximising the minimum communication throughput of sensors and the second is a game problem in which cost for each sensor is a measure of its communication time with its neighbours. We investigate adaptive algorithms using which sensors can tune to the optimal channel attempt rates in a distributed fashion. For the team problem, the adaptive scheme is a stochastic gradient algorithm derived from the augmented Lagrangian formulation of the optimisation problem. The game formulation not only leads to an explicit characterisation of the Nash equilibrium but also to a simple iterative scheme by which sensors can learn the equilibrium attempt probabilities using only the estimates of transmission and reception times from their local measurements. Our approach is promising and should be seen as a step towards developing optimally self-organising architectures for sensor networks.