Abstract
We study the load balancing problem in a dense wireless multihop network, where a typical path consists of a large number of hops, that is, the spatial scales of a typical distance between source and destination and mean distance between the neighboring nodes are strongly separated. In this limit, we present a general framework for analyzing the traffic load resulting from a given set of paths and traffic demands. We formulate the load balancing problem as a minmax problem and give two lower bounds for the achievable minimal maximum traffic load. The framework is illustrated by considering the load balancing problem of uniformly distributed traffic demands in a unit disk. For this special case, we derive efficient expressions for computing the resulting traffic load for a given set of paths. By using these expressions, we are able to optimize a parameterized set of paths yielding a particularly flat traffic load distribution which decreases the maximum traffic load in the network by 40% in comparison with the shortest-path routing.
Highlights
In a wireless multihop network, a typical path consists of several hops and the intermediate nodes along a path act as relays
We have presented a general framework for analyzing traffic load and routing in a large dense multihop network
The approach relies on strong separation of spatial scales between the microscopic level, corresponding to the node and its immediate neighbors, and the macroscopic level, corresponding to the path from the source to the destination
Summary
In a wireless multihop network, a typical path consists of several hops and the intermediate nodes along a path act as relays. EURASIP Journal on Wireless Communications and Networking of packets is bounded by the given maximum, and the load balancing task is to determine the paths in such a way that the maximum flux is minimized. We derive a simple computationally efficient expression for evaluating the traffic load for a general family of paths, making full use of the symmetry of the problem. By using these expressions, we optimize a parameterized set of paths which yields about 40% reduction of the maximum traffic load. Even though the results presented in this work are valid only in the limit of a dense network (i.e., a large number of nodes and a small transmission range), they give insight to the problem and can serve as useful approximations for more realistic scenarios
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