Approximation Algorithms for Data Broadcast in Wireless Networks

Abstract

Abstract-Broadcasting is a fundamental operation in wireless networks and plays an important role in the communication protocol design. In multihop wireless networks, however, interference at a node due to simultaneous transmissions from its neighbors makes it non-trivial to design a minimum-latency broadcast algorithm, which is known to be NP-complete. We present a simple 12-approximation algorithm for the one-to-all broadcast problem that improves all previously known guarantees for this problem. We then consider the all-to-all broadcast problem where each node sends its own message to all other nodes. For the all-to-all broadcast problem, we present two algorithms with approximation ratios of 20 and 34, improving the best result available in the literature. Finally, we report experimental evaluation of our algorithms. Our studies indicate that our algorithms perform much better in practice than the worst-case guarantees provided in the theoretical analysis and achieve up to 37% performance improvement over existing schemes.

An Iterative Power Gradient Searching Algorithm towards the Slot and Power Joint Scheduling Problem in MANET Broadcast

“…In [20], Gandhi et al proved the NP-hardness of the MLBS problem by giving an approximation ratio in terms of latency of 400. A recent report has shown [8] the reduction of the approximation ratio in terms of latency to 12. Besides the MLBS problem in ad hoc networks, the MLBS problem in duty-cycled multihop wireless networks (MLBSDC) has also been studied widely since 2012.…”

Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks

“…Researchers have dedicated significant efforts to studying the MLBS problem, particularly in traditional wireless IoT networks where the nodes are active at all times. [6][7][8][9][10][11][12] Gandhi et al proved its NP-hardness and presented an approximation algorithm that guarantees a ratio of no smaller than 400 in Reference 6. The approximation ratio was later improved to 16 by Huang et al, 7 and further reduced to 12 in Reference 8.…”

“…Gandhi et al proved its NP‐hardness and presented an approximation algorithm that guarantees a ratio of no smaller than 400 in Reference 6. The approximation ratio was later improved to 16 by Huang et al, 7 and further reduced to 12 in Reference 8. Different from the above works, Chen et al 9 employed the assumption that the interference radius of a node is greater than its transmission radius and presented an approximate algorithm.…”

“…In traditional wireless IoT networks where the nodes continue to be active, the broadcast latency refers to the amount of time slots necessary for establishing a collision‐free schedule for broadcasting. In such networks, the problem of minimum latency broadcast scheduling (MLBS) has been proven to be NP‐hard and has been investigated by Gandhi et al, 6 Huang et al, 7 Gandhi et al, 8 Chen et al, 9 Mahjourian et al, 10 Huang et al, 11 and Kam et al 12 Many efficient methods are proposed considering the design of the broadcast tree and the collisions between neighboring transmissions.…”