$k$ -traveling salesperson problem with neighborhood ($k$-TSPN) and the $k$-rooted path cover problem with neighborhood ( $k$-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average." /> $k$ -traveling salesperson problem with neighborhood ($k$-TSPN) and the $k$-rooted path cover problem with neighborhood ( $k$-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average." /> $k$ -traveling salesperson problem with neighborhood ($k$-TSPN) and the $k$-rooted path cover problem with neighborhood ( $k$-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average." /> Minimum Latency Multiple Data MULETrajectory Planning in Wireless Sensor Networks
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Issue No.04 - April (2014 vol.13)
pp: 838-851
Donghyun Kim , Dept. of Math. & Comput. Sci., North Carolina Central Univ., Durham, NC, USA
R. N. Uma , Dept. of Math. & Comput. Sci., North Carolina Central Univ., Durham, NC, USA
Baraki H. Abay , Dept. of Math. & Comput. Sci., North Carolina Central Univ., Durham, NC, USA
Weili Wu , Dept. of Comput. Sci., Univ. of Texas at Dallas, Richardson, TX, USA
Wei Wang , Dept. of Math., Xi'an Jiaotong Univ., Xi'an, China
Alade O. Tokuta , Dept. of Math. & Comput. Sci., North Carolina Central Univ., Durham, NC, USA
ABSTRACT
This paper investigates the problem of computing the optimal trajectories of multiple data MULEs (e.g., robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks. By relying on a slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood ( k-TSPN) and the k-rooted path cover problem with neighborhood ( k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average.
INDEX TERMS
wireless sensor networks, approximation theory, telecommunication network routing, travelling salesman problems,heuristic algorithms, minimum latency multiple data MULE trajectory planning, wireless sensor networks, optimal trajectories, data collection latency, k-traveling salesperson problem, k-rooted path cover problem, NP-hard, constant factor approximation algorithms,Approximation algorithms, Approximation methods, Trajectory, Wireless sensor networks, Robot sensing systems, Polynomials, Wireless communication,approximation algorithm, Wireless sensor network, data mules, traveling salesperson problem with neighborhood
CITATION
Donghyun Kim, R. N. Uma, Baraki H. Abay, Weili Wu, Wei Wang, Alade O. Tokuta, "Minimum Latency Multiple Data MULE Trajectory Planning in Wireless Sensor Networks", IEEE Transactions on Mobile Computing, vol.13, no. 4, pp. 838-851, April 2014, doi:10.1109/TMC.2013.69
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