Abstract
We propose a self-organization scheme for cost-effective and load-balanced routing in multi-hop networks. To avoid overloading nodes that provide favourable routing conditions, we assign each node with a cost function that penalizes high loads. Thus, finding routes to sink nodes is formulated as an optimization problem in which the global objective function strikes a balance between route costs and node loads. We apply belief propagation (its min-sum version) to solve the network optimization problem and obtain a distributed algorithm whereby the nodes collectively discover globally optimal routes by performing low-complexity computations and exchanging messages with their neighbours. We prove that the proposed method converges to the global optimum after a finite number of local exchanges of messages. Finally, we demonstrate numerically our framework’s efficacy in balancing the node loads and study the trade-off between load reduction and total cost minimization.
Highlights
Large-scale wireless networks employing multi-hop transmissions are an integral component of the internet of things [1]
We propose a self-organization scheme for cost-effective and load-balanced routing in multi-hop networks
We consider balanced routing as the minimization of a network objective function, which includes the overall cost of the routes and an additional term that penalizes the node-loads
Summary
Large-scale wireless networks employing multi-hop transmissions are an integral component of the internet of things [1]. We propose an algorithmic strategy for distributed multi-hop networking whereby the nodes coordinate and organize themselves so as to route the information to the destinations in an efficient and balanced way. To this end, we consider balanced routing as the minimization of a network objective function, which includes the overall cost of the routes (given by generic link costs) and an additional term that penalizes the node-loads. To solve the optimization problem, we use the min-sum version of the belief propagation (BP) method [10] In this way, we obtain a distributed algorithm which finds globally optimal routes in a decentralized manner with low-complexity local computations and message exchanges between neighbouring nodes. We show that the proposed method converges to the global optimum in a finite number of iterations
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