Parallel Algorithm for NP-Hard Problem of Channel Resource Allocation Optimization in Ad Hoc and Sensor Networks

This paper proposes a technique to estimate the minimal quantity of orthogonal channel resources required for ad hoc and sensor network connectivity. Simultaneously, the resource allocation to each specific line is conducted by grouping lines into concurrent transmission sets. Our proposed technique...

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Bibliographic Details
Published in:Future internet Vol. 17; no. 8; p. 362
Main Authors: Ivanov, Valeriy, Tereshonok, Maxim
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
ad hoc network
Ad hoc networks
Advertising executives
Algorithms
Analysis
artificial intelligence
channel resource allocation
Combinatorial analysis
Complexity
concurrent resource usage
Data transmission
Genetic algorithms
global heuristic optimization
Localization
Mathematical optimization
Network topologies
Neural networks
Optimization
Polynomials
Resource allocation
Sensors
Transmission
Germany
ISSN:1999-5903, 1999-5903
Online Access:Get full text
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Summary:This paper proposes a technique to estimate the minimal quantity of orthogonal channel resources required for ad hoc and sensor network connectivity. Simultaneously, the resource allocation to each specific line is conducted by grouping lines into concurrent transmission sets. Our proposed technique uses the physical-based interference model assumption, where each node interferes with every other node, turning ad hoc and sensor network performance optimization problems into the NP-hard ones. In contrast to most of the other works with the physical-based interference model assumption, we mitigate the combinatorial explosion of concurrently transmitting line sets considering the global interference instead of localizing the interference with line or space partitioning. We have performed the mitigation, firstly, using pairwise mutually acceptable line sets for each line. Then, based on the limitations of pairwise sets, we construct the tree of mutually acceptable interfering line sets. Then, from the created tree, we find the minimal set cover of concurrently transmitting line sets. The tree construction has the exponential worst-case time and space complexity if all lines in the network can transmit together. By randomly pruning the tree and using the genetic algorithm to find the pruned tree which gives the same minimal set cover as the full tree, we have reduced the worst space and time complexities to the polynomial ones. We have devised our technique with parallelism in mind to speed up the resource allocation optimization even more. Based on an extensive simulation study with random network topologies of sizes up to 250 nodes and the average number of lines up to 2000, we estimated the time and space complexity for different tree pruning and optimization techniques and found the effective ones.
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi17080362