Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2015

01-06-2015 | Original Article

An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks

Authors: Wei-Lun Chang, Deze Zeng, Rung-Ching Chen, Song Guo

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2015

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In sparse wireless sensor networks, a mobile robot is usually exploited to collect the sensing data. Each sensor has a limited transmission range and the mobile robot must get into the coverage of each sensor node to obtain the sensing data. To minimize the energy consumption on the traveling of the mobile robot, it is significant to plan a data collection path with the minimum length to complete the data collection task. In this paper, we observe that this problem can be formulated as traveling salesman problem with neighborhoods, which is known to be NP-hard. To address this problem, we apply the concept of artificial bee colony (ABC) and design an ABC-based path planning algorithm. Simulation results validate the correctness and high efficiency of our proposal.
previous article Synchronization of a class of memristive neural networks with time delays via sampled-data control
next article Identification of boundary shape using a hybrid approach

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
2.
Chiu KM, Liu JS (2011) Robot routing using clustering-based parallel genetic algorithm with migration. In: IEEE workshop on merging fields of computational intelligence and sensor technology (CompSens) (2011), pp 42–49
3.
Comarela G, Gonçalves K, Pappa GL, Almeida J, Almeida V (2011) Robot routing in sparse wireless sensor networks with continuous ant colony optimization. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation (GECCO ’11). ACM, New York, pp 599–606
4.
De Berg M, Gudmundsson J, Katz MJ, Levcopoulos C, Overmars MH, van der Stappen AF (2005) Tsp with neighborhoods of varying size. J Algorithms 57(1):22–36CrossRefMATHMathSciNet
5.
Dorigo M, Gambardella L (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRef
6.
Dorigo M, Stützle T (2004) Ant colony optimization. Bradford Company, Scituate
7.
Elbassioni K, Fishkin AV, Mustafa NH, Sitters R (2005) Approximation algorithms for euclidean group tsp. In: Proceedings of the 32nd international colloquim on automata, languages and programming (ICALP). Springer, Lisbon, Portugal, pp 1115–1126
8.
Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697CrossRefMATH
9.
Gentilini I, Margot F, Shimada K (2013) The travelling salesman problem with neighbourhoods: Minlp solution. Optim Methods Softw 28(2):364–378CrossRefMATHMathSciNet
10.
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 8(1):687–697CrossRef
11.
Karaboga D, Okdem S, Ozturk C (2010) Cluster based wireless sensor network routings using artificial bee colony algorithm. In: Proceedings of the 2010 international conference on autonomous and intelligent systems (AIS ’10). pp 1–5
12.
Li L, Cheng Y, Tan L, Niu B (2011) A discrete artificial bee colony algorithm for tsp problem. In: Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications (ICIC’11). Springer-Verlag, Berlin, pp 566–573.doi:10.​1007/​978-3-642-24553-4_​75
13.
14.
Little J, Murty K, Sweeney D, Karel C (1963) An algorithm for the traveling salesman problem. Oper Res 11(6):972–989CrossRefMATH
15.
16.
Safra S, Schwartz O (2006) On the complexity of approximating tsp with neighborhoods and related problems. Comput Complex 14(4):281–307CrossRefMathSciNet
17.
Tekdas O, Isler V, Lim JH, Terzis A (2009) Using mobile robots to harvest data from sensor fields. Wirel Commun 16(1):22–28CrossRef
18.
Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330CrossRef
19.
Yuan B, Orlowska M, Sadiq S (2007) On the optimal robot routing problem in wireless sensor networks. IEEE Trans Knowl Data Eng 19(9):1252–1261CrossRef
Metadata
Title
An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks
Authors
Wei-Lun Chang
Deze Zeng
Rung-Ching Chen
Song Guo
Publication date
01-06-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0195-z

Other articles of this Issue 3/2015

International Journal of Machine Learning and Cybernetics 3/2015 Go to the issue

Original Article

A kind of approximations of generalized rough set model

Original Article

A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement

Original Article

A data-driven study for evaluating fineness of cement by various predictors

Original Article

Identification of boundary shape using a hybrid approach

Original Article

Synchronization of a class of memristive neural networks with time delays via sampled-data control

Original Article

An algorithm selection based platform for image understanding using high-level symbolic feedback and machine learning