ABSTRACT
A decentralized framework for network optimization is presented for wireless sensing nodes. The wireless sensing nodes use a dynamic programming algorithm to choose optimal routes for data transmission from any network node to a specialized ‘gateway’ node that provides access to the wider internet. The dynamic programming algorithm is a variation of the Bellman-Ford algorithm and allows for the wireless sensing nodes to make decisions based on locally available network information, resulting in a decentralized routing algorithm. Routing decisions depend on the cost it takes to communicate from a node to a gateway, either directly or indirectly, using neighboring nodes as relay points. Nodes constantly share information with neighbors and when something effects the cost of a path, such as a node failure or the discovery of a less costly route, all nodes upstream along the existing path are made aware and re-route accordingly. A sample network is used to illustrate and verify the functionality of the proposed algorithm. The network and node decisions are simulated to show the evolution of the network routing decisions, and the simulation consistently shows the network converging to an optimal configuration. The speed of convergence depends on the order in which the nodes are assumed to attempt to establish and optimize their connections.
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