Broadcast with Heterogeneous Node Capability

Abstract

Abstract-In this paper, we investigate the power-efficient broadcast routing problem over heterogeneous wireless ad hoc or sensor networks where network nodes have heterogeneous capability. The network links between pairs of nodes can no longer be modeled as symmetric or bidirectional. We show that, while most previous power-efficient algorithms work in this setting with minor modifications, they are not designed to exploit such asymmetric constraints. We present a suitable algorithm which takes into account of the constraints and yet most powerefficient among all known algorithms.

A Two-Phase Constructive Heuristic for Minimum Energy Broadcasting in Wireless Ad Hoc Networks

“…Sweep [9], Iterative Maximum-Branch Minimization (IMBM) [26], Embedded Wireless Multicast Advantage (EWMA) [4], Broadcast Incremental-Decremental Power (BIDP) [27], and Shrinking Overlapped Range (SOR) [28] are some typical examples of tree based algorithms where a tree is updated to a new tree at each improvement step by removing some of the links of the previous one and adding new links. The power assignment based algorithms like r-shrink [29] and Largest Expanding Sweep Search (LESS) [30] make moves based on a new power assignment for each node in the network.…”

Iterated Local Optimization for Minimum Energy Broadcast

“…Performance-wise, above heuristics (IMBM, rShrink, Post-Sweep) are not impressive, resulting in local optima only comparable to tree construction heuristics (e.g., EWMA) in their solution quality. In our prior work [15], we presented a highly effective heuristic algorithm (comparable to the LK search for TSP [20]) called the Largest Expanding Sweep Search (LESS), based on local search principle. In this paper, we extend this concept and present an iterated local optimization based heuristics relying on two neighborhood structures, which are designed to be more general and to maximize the correlation with the objective function (the meaning of which will be clarified later), still maintaining computational efficiency.…”

“…After the expanding sweep search neighborhood N H ess is clearly defined, the LESS algorithm [15] is nothing more than LocalSearch(T , c, NH ess ) with the steepest descent search strategy. That is, given an initial feasible solution, the best neighbor among N H ess is chosen at each iteration, until there is no more gain in cost.…”

“…On the other hand, our LESS algorithm [15] progressively expands the ranges of nodes. While range expansion and range reduction are like the flip side of the coin (one can be achieved at the cost of the other), the implication in local search is radically different.…”

“…2, we could use our LESS algorithm [15]. However, we can speed up convergence significantly by selecting the first k largest gain at each iteration as long as the ranges are disjoint, for which we call the variableorder disjoint (VOD) steepest descent search strategy.…”