ACO-Based Sweep Coverage Scheme in Wireless Sensor Networks

Huang, Peng; Lin, Feng; Liu, Chang; Gao, Jian; Zhou, Jiliu

doi:10.1155/2015/484902

Cited by 12 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The efficient GA algorithm in the stated problem domain has been selected from [4]. Since a coverage problem is one of the most critical issues in wireless sensor networks domain, this issue has been formulated into an optimization problem and was solved by an ant colony optimization for sweep coverage (ACOSC) problem [29]; this algorithm is customized based on the circumstance of the current paper. In the last, a hybrid discrete PSO (HDPSO) algorithm has been selected and customized for the stated problem [30].…”

Section: 1experimental Settingsmentioning

confidence: 99%

Sleep-wakeup Scheduling Algorithm for Lifespan Maximization of Directional Sensor Networks: An Advanced Discrete Cuckoo Search Optimization Algorithm

Shirvani

2021

Preprint

View full text Add to dashboard Cite

Directional sensor networks are ad hoc networks which are utilized in different applications to monitor and coverage all of the specific targets in the observing fields permanently. These kinds of networks include several configurable directional sensors in which they can be adjusted in one the possible directions along with one of its adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization is still a challenge. This paper formulates the expansion of directional sensor network lifespan to a discrete optimization problem. Several proposals were presented in literature to solve the stated problem, but majority of them are threatened to get stuck in local optimum and led low efficiency. To solve this combinatorial problem, an advanced discrete cuckoo search algorithm is designed and is called several times until the remaining battery associated to alive sensors do not let observe all targets. In each time, algorithm returns an efficient cover along with its activation time. A cover is a sub set of available sensors capable of monitoring all targets in the observing field. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Designing miscellaneous discrete walking around procedures makes to reach a good balance between exploration and exploitation in search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in variety circumstances proves the superiority of the proposed algorithm is about 19.33%, 14.83%, 13.50%, and 5.33% in term of average lifespan improvement against H-MNLAR, ACOSC, GA, and HDPSO algorithms respectively.

show abstract

Section: 1experimental Settingsmentioning

confidence: 99%

Sleep-wakeup Scheduling Algorithm for Lifespan Maximization of Directional Sensor Networks: An Advanced Discrete Cuckoo Search Optimization Algorithm

Shirvani

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Simulation results show that VRPSC achieves better performance than CSWEEP but lack comparison with other algorithms. Huang et al [23] tested the feasibility of ant colony optimization (ACO) [24] for sweep coverage and proposed the ACO-based sweep coverage (ACOSC) algorithm to solve the sweep coverage problem while considering data delivery. ACOSC uses a two-point-crossover operation in the local search.…”

Section: State Of the Artmentioning

confidence: 99%

“…This section presents the simulation results for evaluating the proposed algorithm and compares the algorithm with VRPSC [22] and ACOSC [23]. The performance of these solutions is evaluated in different scenarios.…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

Improved ACO-Based Sweep Coverage Scheme Considering Data Delivery

Huang¹,

Lin²,

Xu³

et al. 2017

Int. j. simul. model.

View full text Add to dashboard Cite

The newly introduced sweep coverage scheme uses mobile sensors to implement network coverage in wireless sensor networks (WSNs) and has attracted much attention from researchers. However, data buffer and moving speed of the mobile sensor are limited in sweep coverage. Thus, scheduling the minimum number of mobile sensors to efficiently implement dynamical network coverage while considering data delivery is still a challenging problem. To provide steady and efficient data gathering from sensors, an improved ant colony optimization-based sweep coverage (IACOSC) scheme supporting data delivery was proposed. In IACOSC, the artificial ants were used to create the initial coverage routes for points of interest. Then, a novel metric called route coverage efficiency was used to evaluate the routes. Finally, a local search algorithm based on route deletion and node insertion was employed to optimize these routes. Algorithm analysis shows that the time complexity of IACOSC is O(n 3 ). Simulation results show that, compared with existing sweep coverage approaches considering data delivery, IACOSC significantly reduces the computational complexity and decreases the computation time by 50 % while reducing the mobile sensors by 16.73 % in the same network scenarios. The results obtained in this study can be applied to optimal deployment of WSN using the sweep coverage scheme.

show abstract

“…So, mobile sensors can move around to cover more objects than in the continuous coverage situation to reduce monitoring cost. For its cost effectiveness, sweep coverage has attracted increasing attention [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Liang

Shen

2021

Sensors

View full text Add to dashboard Cite

As an important application of wireless sensor networks (WSNs), deployment of mobile sensors to periodically monitor (sweep cover) a set of points of interest (PoIs) arises in various applications, such as environmental monitoring and data collection. For a set of PoIs in an Eulerian graph, the point sweep coverage problem of deploying the fewest sensors to periodically cover a set of PoIs is known to be Non-deterministic Polynomial Hard (NP-hard), even if all sensors have the same velocity. In this paper, we consider the problem of finding the set of PoIs on a line periodically covered by a given set of mobile sensors that has the maximum sum of weight. The problem is first proven NP-hard when sensors are with different velocities in this paper. Optimal and approximate solutions are also presented for sensors with the same and different velocities, respectively. For M sensors and N PoIs, the optimal algorithm for the case when sensors are with the same velocity runs in O(MN) time; our polynomial-time approximation algorithm for the case when sensors have a constant number of velocities achieves approximation ratio 12; for the general case of arbitrary velocities, 12α and 12(1−1/e) approximation algorithms are presented, respectively, where integer α≥2 is the tradeoff factor between time complexity and approximation ratio.

show abstract

ACO-Based Sweep Coverage Scheme in Wireless Sensor Networks

Cited by 12 publications

References 11 publications

Sleep-wakeup Scheduling Algorithm for Lifespan Maximization of Directional Sensor Networks: An Advanced Discrete Cuckoo Search Optimization Algorithm

Sleep-wakeup Scheduling Algorithm for Lifespan Maximization of Directional Sensor Networks: An Advanced Discrete Cuckoo Search Optimization Algorithm

Improved ACO-Based Sweep Coverage Scheme Considering Data Delivery

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Contact Info

Product

Resources

About