Exact Algorithms for Maximizing Lifetime of WSNs Using Integer Linear Programming

Ma, Xinshu; Zhu, Xiaojun; Chen, Bing

doi:10.1109/wcnc.2017.7925669

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…Finding routing paths of messages to maximize lifetime is a critical problem in wireless sensor networks (e.g., [1][2][3][5][6][7]). Unfortunately, it is NP-hard in most scenarios when nodes can or cannot perform data aggregation.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, it is NP-hard in most scenarios when nodes can or cannot perform data aggregation. Researchers resort to polynomial-time approximation algorithms by sacrificing accuracy (e.g., [8]), or exponential-time exact algorithms by sacrificing running time (e.g., [1,3]). While both algorithms have important applications, we focus on exact algorithms in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…A limitation is that the technique does not work when the graph is already biconnected. Reference [3] proposes to incorporate graph decomposition with integer linear programming. The basic idea is to decompose the graph into biconnected subgraphs and formulate the problem on each subgraph as an integer linear programming problem, which is solved by an integer linear programming solver.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, all existing exact algorithms run in exponential time [1][2][3]. A straightforward approach is to perform exhaustive search over the solution space (e.g., [2]).…”

Section: Introductionmentioning

confidence: 99%

“…A straightforward approach is to perform exhaustive search over the solution space (e.g., [2]). This process can be improved by dynamically eliminating suboptimal solutions in the search process [1], or integrating fast integer linear programming solvers [3]. However, these algorithms implicitly use only one CPU core and do not use the full potential of current multicore CPUs.…”

Section: Introductionmentioning

confidence: 99%

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Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores

Cao

Zhu

2018

Wireless Communications and Mobile Computing

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Maximizing the lifetime of wireless sensor networks is NP-hard, and existing exact algorithms run in exponential time. These algorithms implicitly use only one CPU core. In this work, we propose to use multiple CPU cores to speed up the computation. The key is to decompose the problem into independent subproblems and then solve them on different cores simultaneously. We propose three decomposition approaches. Two of them are based on the notion that a tree does not contain cycles, and the third is based on the notion that, in any tree, a node has at most one parent. Simulations on an 8-core desktop computer show that our approach can speed up existing algorithms significantly.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In fact, all existing exact algorithms run in exponential time [1][2][3]. A straightforward approach is to perform exhaustive search over the solution space (e.g., [2]).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores

Cao

Zhu

2018

Wireless Communications and Mobile Computing

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Revisiting non-tree routing for maximum lifetime data gathering in wireless sensor networks

Zhu

2021

J Supercomput

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A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

Pandit

Zhang

Chattopadhyay

et al. 2018

Knowledge-Based Systems

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The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions.

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Exact Algorithms for Maximizing Lifetime of WSNs Using Integer Linear Programming

Cited by 7 publications

References 11 publications

Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores

Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores

Revisiting non-tree routing for maximum lifetime data gathering in wireless sensor networks

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

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