More Web Proxy on the site http://driver.im/

research-article

An Energy-efficient UAV-based Data Aggregation Protocol in Wireless Sensor Networks

Authors:

Azzedine BoukercheAuthors Info & Claims

DIVANet'18: Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Pages 34 - 40

https://doi.org/10.1145/3272036.3272047

Published: 25 October 2018 Publication History

Abstract

Energy efficiency is an important issue in Wireless Sensor Networks (WSNs). The energy is mainly consumed by data sensing, data transmission and movement of sensors. The energy consumed by data transmission is much larger than data sensing. A potential solution for energy saving is applying the external devices to collect data to prolong the network lifetime. In this paper, we adopt an unmanned aerial vehicle (UAV) serving as the data mule and propose a novel energy-efficient UAV-based data aggregation protocol in WSNs to reduce the energy consumption of sensors. By considering a clustered WSN, our approach computes an optimal path for data mule through all cluster heads (CHs) while achieving a relatively high system-wide energy efficiency. Moreover, we introduce a genetic algorithm to derive a near-optimal solution for a large-scale WSN while reducing the computing time. We compare our protocol with state-of-the-art approaches, and the simulation results demonstrate that the proposed algorithm improves the network performance on energy efficiency.

References

[1]

Zakir H. Ahmed. 2010. Genetic Algorithm for the Traveling Salesman Problem using Sequential Constructive Crossover Operator., Vol. 3 (2010), 96--105.

[2]

M. Alnuaimi, K. Shuaib, K. Alnuaimi, and M. Abdel-Hafez. 2015. Data Gathering in Delay Tolerant Wireless Sensor Networks Using a Ferry. Sensors, Vol. 15, 10 (2015), 25809--25830.

[3]

G. Anastasi, M. Conti, M. Di Francesco, and. Passarella. 2009. Energy Conservation in Wireless Sensor Networks: A Survey. Ad Hoc Netw., Vol. 7, 3 (2009), 537--568.

Digital Library

[4]

Constantinos Marios Angelopoulos, Julia Buwaya, Orestis Evangelatos, and José Rolim. 2015. Traversal strategies for wireless power transfer in mobile ad-hoc networks. In Proc. MSWiM . 31--40.

Digital Library

[5]

J. Aranda, H. Carrillo, and D. Mendez. 2017. Enhanced multimodal switching mechanisms for node scheduling and data gathering in wireless sensor networks. In Proc. COLCOM. 1--6.

[6]

Rodolfo W.L. Coutinho, Azzedine Boukerche, Luiz F.M. Vieira, and Antonio A.F. Loureiro. 2014. Transmission power control-based opportunistic routing for wireless sensor networks. In Proc. MSWiM. 219--226.

Digital Library

[7]

R. Dasgupta and S. Yoon. 2017. Energy-Efficient Deadline-Aware Data-Gathering Scheme Using Multiple Mobile Data Collectors. Sensors, Vol. 17, 4 (2017), 742.

[8]

S. R. Gandham, M. Dawande, R. Prakash, and S. Venkatesan. 2003. Energy efficient schemes for wireless sensor networks with multiple mobile base stations. In Proc. IEEE GLOBECOM, Vol. 1. 377--381.

[9]

Pramod D. Ganjewar, S. Barani, Sanjeev J. Wagh, and Santosh S. Sonavane. 2018. Survey on Data Reduction Techniques for Energy Conservation for Prolonging Life of Wireless Sensor Network. IEEE Wireless Commun., Vol. 10, 2 (2018).

[10]

Chirihane Gherbi, Zibouda Aliouat, and Mohamed Benmohammed. 2016. An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy, Vol. 114 (2016), 647 -- 662.

[11]

W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. 2000. Energy-efficient communication protocol for wireless microsensor networks. In Proc. HICSS . 1--10.

Digital Library

[12]

R. Hinterding. 1995. Gaussian mutation and self-adaption for numeric genetic algorithms. In Proc. IEEE CEC, Vol. 1. 384--389.

[13]

DT. Ho, E. I. Gr Otli, P. B. Sujit, and TA. Johansen. 2015. Optimization of Wireless Sensor Network and UAV Data Acquisition. J. Intell. Robot. Syst., Vol. 78, 1 (2015), 159--179.

Digital Library

[14]

L. Kong, J. Pan, V. Snávs el, P. Tsai, and T. Sung. 2018. An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommun. Syst., Vol. 67, 3 (2018), 451--463.

[15]

C. Konstantopoulos, G. Pantziou, D. Gavalas, A. Mpitziopoulos, and B. Mamalis. 2012. A Rendezvous-Based Approach Enabling Energy-Efficient Sensory Data Collection with Mobile Sinks. IEEE Trans. Parallel Distrib. Syst., Vol. 23, 5 (2012), 809--817.

Digital Library

[16]

C. Konstantopoulos, G. Pantziou, N. Vathis, V. Nakos, and D. Gavalas. 2014. Efficient Mobile Sink-based Data Gathering in Wireless Sensor Networks with Guaranteed Delay. In Proc. MobiWac. 47--54.

Digital Library

[17]

J. S. Leu, T. H. Chiang, M. C. Yu, and K. W. Su. 2015. Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor Network With Isolated Nodes. IEEE Commun. Lett., Vol. 19, 2 (2015), 259--262.

[18]

J. Li and P. Mohapatra. 2005. An analytical model for the energy hole problem in many-to-one sensor networks. In Proc. IEEE VTC, Vol. 4. 2721--2725.

[19]

Kin Sum Liu, Jie Gao, Shan Lin, Hua Huang, and Brent Schiller. 2016. Joint Sensor Duty Cycle Scheduling with Coverage Guarantee. In Proc. MobiWac . 11--20.

Digital Library

[20]

Reem E. Mohemed, Ahmed I. Saleh, Maher Abdelrazzak, and Ahmed S. Samra. 2017. Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Comput. Netw., Vol. 114 (2017), 51 -- 66.

[21]

Sarwar Morshed and Geert Heijenk. 2014. TR-MAC: An Energy-efficient MAC Protocol Exploiting Transmitted Reference Modulation for Wireless Sensor Networks. In Proc. MSWiM. 21--29.

Digital Library

[22]

M. Mozaffari, W. Saad, M. Bennis, and M. Debbah. 2016. Mobile Internet of Things: Can UAVs Provide an Energy-Efficient Mobile Architecture?. In Proc. IEEE GLOBAECOM. 1--6.

[23]

B. Prince and S. Gupta. 2016. Load balanced and energy efficient data collection scheme using data mule for WSNs. In Proc. IEEE RTEICT. 1651--1655.

[24]

P. Rawat, K. D. Singh, H. Chaouchi, and J. M. Bonnin. 2014. Wireless sensor networks: a survey on recent developments and potential synergies. J. Supercomput., Vol. 68, 1 (2014), 1--48.

Digital Library

[25]

M. Sartipi and R. Fletcher. 2011. Energy-Efficient Data Acquisition in Wireless Sensor Networks Using Compressed Sensing. In Proc. DCC. 223--232.

Digital Library

[26]

R. Silva, J. S. Silva, and F. Boavida. 2014. Mobility in wireless sensor networks -- Survey and proposal. Comput. Commun., Vol. 52, Supplement C (2014), 1 -- 20.

[27]

S. K. Singh, P. Kumar, and J. P. Singh. 2016. An energy efficient Odd-Even round number based data collection using mules in WSNs. In Proc. WCSP. 1255--1259.

[28]

Éfren L. Souza, Eduardo F. Nakamura, and Richard W. Pazzi. 2016. Target Tracking for Sensor Networks: A Survey. ACM CSUR, Vol. 49, 2 (2016), 30:1--30:31.

Digital Library

[29]

P. Sun and A. Boukerche. 2016. Integrated connectivity and coverage techniques for wireless sensor networks. In Proc. MobiWac . 75--82.

Digital Library

[30]

P. Sun and N. Samaan. 2015. Random Node Failures and Wireless Networks Connectivity: Theoretical Analysis. IEEE Wirel. Commun. Lett., Vol. 4, 5 (2015), 461--464.

[31]

SZ DJI Technology Co., Ltd. 2018. Phantom 4 PRO specs. {Online}. Available: https://www.dji.com/phantom-4-pro/info#specs

[32]

Z. M. Wang, S. Basagni, E. Melachrinoudis, and C. Petrioli. 2005. Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime. In Proc. HICSS . 287.1--287.9.

Digital Library

[33]

D. Wu, J. He, H. Wang, C. Wang, and R. Wang. 2015. A hierarchical packet forwarding mechanism for energy harvesting wireless sensor networks. IEEE Commun. Mag., Vol. 53, 8 (2015), 92--98.

[34]

S. Y. Wu and J. S. Liu. 2014. Evolutionary path planning of a data mule in wireless sensor network by using shortcuts. In Proc. IEEE CEC. 2708--2715.

[35]

Y. Yoon and Y. H. Kim. 2013. An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks. IEEE Trans. Cybern., Vol. 43, 5 (2013), 1473--1483.

Cited By

Jebur T(2024)Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor NetworksJournal of Electronics,Computer Networking and Applied Mathematics10.55529/jecnam.43.34.48(34-48)Online publication date: 1-Apr-2024
https://doi.org/10.55529/jecnam.43.34.48
Pan YZhu CLuo LLiu YCheng Z(2024)FedTrack: A Collaborative Target Tracking Framework Based on Adaptive Federated LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2024.339529273:9(13868-13882)Online publication date: Sep-2024
https://doi.org/10.1109/TVT.2024.3395292
Aldin HGhods MNayebipour FTorshiz M(2024)A comprehensive review of energy harvesting and routing strategies for IoT sensors sustainability and communication technologySensors International10.1016/j.sintl.2023.1002585(100258)Online publication date: 2024
https://doi.org/10.1016/j.sintl.2023.100258
Show More Cited By

Index Terms

An Energy-efficient UAV-based Data Aggregation Protocol in Wireless Sensor Networks
1. Networks
  1. Network algorithms
    1. Data path algorithms
  2. Network performance evaluation

Recommendations

Energy-Efficient Data Gathering Schemes in UAV-Based Wireless Sensor Networks
SMA 2020: The 9th International Conference on Smart Media and Applications

Wireless sensors networks (WSNs) comprise small sensing and computing units with limited power, and often run in non-replaceable energy sources. A large number of researches have been conducted for energy-efficient data gathering in unmanned aerial ...
An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how ...
Energy balanced data gathering approaches in wireless sensor networks using mixed-hop communication

Wireless sensor networks (WSN) consists of small battery powered nodes. Energy efficiency and energy balancing are the most stringent needs of WSN for prolonging its lifetime. Due to many-to-one communication pattern in multi-hop communication, energy ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DIVANet'18: Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

October 2018

93 pages

ISBN:9781450359641

DOI:10.1145/3272036

General Chair:
Mirela M. A. Notare
Faculdade de Tecnologia em Transporte Aereo, Brazil
,
Program Chairs:
Peng Sun
University of Ottawa, Canada
,
Rodolfo W. L. Coutinho
University of Ottawa, Canada

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MSWIM '18

Sponsor:

SIGSIM

MSWIM '18: 21st ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

October 28 - November 2, 2018

QC, Montreal, Canada

Acceptance Rates

Overall Acceptance Rate 70 of 308 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

24
Total Citations
View Citations
225
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Jebur T(2024)Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor NetworksJournal of Electronics,Computer Networking and Applied Mathematics10.55529/jecnam.43.34.48(34-48)Online publication date: 1-Apr-2024
https://doi.org/10.55529/jecnam.43.34.48
Pan YZhu CLuo LLiu YCheng Z(2024)FedTrack: A Collaborative Target Tracking Framework Based on Adaptive Federated LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2024.339529273:9(13868-13882)Online publication date: Sep-2024
https://doi.org/10.1109/TVT.2024.3395292
Aldin HGhods MNayebipour FTorshiz M(2024)A comprehensive review of energy harvesting and routing strategies for IoT sensors sustainability and communication technologySensors International10.1016/j.sintl.2023.1002585(100258)Online publication date: 2024
https://doi.org/10.1016/j.sintl.2023.100258
Verma GChamola VKumar NDas AMishra D(2024)Efficient and secure signcryption-based data aggregation for Internet of Drone-based drone-to-ground station communicationAd Hoc Networks10.1016/j.adhoc.2024.103502159(103502)Online publication date: Jun-2024
https://doi.org/10.1016/j.adhoc.2024.103502
Salam AJavaid QAhmad MWahid IArafat M(2023)Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of ThingsFuture Internet10.3390/fi1508027915:8(279)Online publication date: 20-Aug-2023
https://doi.org/10.3390/fi15080279
Messaoudi KOubbati ORachedi ALakas ABendouma TChaib N(2023)A survey of UAV-based data collection: Challenges, solutions and future perspectivesJournal of Network and Computer Applications10.1016/j.jnca.2023.103670216(103670)Online publication date: Jul-2023
https://doi.org/10.1016/j.jnca.2023.103670
Raivi AMoh S(2023)A comprehensive survey on data aggregation techniques in UAV-enabled Internet of thingsComputer Science Review10.1016/j.cosrev.2023.10059950:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100599
Boukerche AWu QSun P(2022)A Novel Two-Mode QoS-Aware Mobile Charger Scheduling Method for Achieving Sustainable Wireless Sensor NetworksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.30353347:1(14-26)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2020.3035334
Saxena KGupta NGupta JSharma DDev K(2022)Trajectory optimization for the UAV assisted data collection in wireless sensor networksWireless Networks10.1007/s11276-022-02934-w28:4(1785-1796)Online publication date: 22-Mar-2022
https://doi.org/10.1007/s11276-022-02934-w
Khan AGupta SGupta S(2022)Emerging UAV technology for disaster detection, mitigation, response, and preparednessJournal of Field Robotics10.1002/rob.2207539:6(905-955)Online publication date: 14-Apr-2022
https://doi.org/10.1002/rob.22075
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten