research-article

An Energy-efficient Distributed TDMA Scheduling Algorithm for ZigBee-like Cluster-tree WSNs

Authors:

Zdenek HanzalekAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 16, Issue 1

Article No.: 3, Pages 1 - 41

https://doi.org/10.1145/3360722

Published: 18 October 2019 Publication History

Abstract

The design of Medium Access Control (MAC) protocol for Wireless Sensor Networks (WSNs) with both limited energy consumption and data delivery time is crucial for industrial and control applications. Since Time Division Multiple Access (TDMA) MAC eliminates the collision occurrence and seeks the minimization of the number of time-slots assigned to each node, the energy consumption of the nodes is reduced. Furthermore, with the proper allocation of the time-slots to the nodes, the transmission delay can be significantly reduced.

In this article, we propose TDMA scheduling algorithm for Cluster-tree topology WSNs that meets the timeliness and the energy demands. The algorithm adopts an elegant approach that expresses the timing constraints of the data transmissions as an integer multiple of the length of the schedule period. Moreover, since the distributed algorithm is well-suited to the scarce resources of the WSNs, we focus on the distributed methods that allow each cluster to come up with its allocated time-slots. The algorithm is based on graph theory, such as distributed shortest path, distributed topological ordering, and distributed graph coloring algorithms. The efficiency of the algorithm, regarding the elapsed time to construct the schedule and the energy consumption, is evaluated over benchmark instances up to several thousands of nodes.

References

[1]

A. Ahmad and Z. Hanzálek. 2017. Distributed real time TDMA scheduling algorithm for tree topology WSNs. In Proceedings of the International Federation of Automatic Control.

[2]

A. Ahmad and Z. Hanzlek. 2018. An energy efficient schedule for IEEE 802.15.4/ZigBee cluster tree WSN with multiple collision domains and period crossing constraint. IEEE Trans. Industr. Info. 14, 1 (Jan. 2018), 12--23.

[3]

ZigBee Alliance. 2012. ZigBee Specification (Document 053474r20); ZigBee Alliance, San Ramon, CA.

[4]

A. Bhatia and R. C. Hansdah. 2015. A distributed TDMA slot scheduling algorithm for spatially correlated contention in WSNs. Mobile Info. Syst. 2015, Article 234143 (2015), 16 pages. https://doi.org/10.1155/2015/234143

[5]

O. Bonaventure. 2011. Computer Networking : Principles, Protocols and Practice. The Saylor Foundation.

[6]

S. C. Ergen and P. Varaiya. 2006. PEDAMACS: Power efficient and delay aware medium access protocol for sensor networks. IEEE Trans. Mobile Comput. 5, 7 (July 2006), 920--930.

Digital Library

[7]

S. C. Ergen and P. Varaiya. 2010. TDMA scheduling algorithms for wireless sensor networks. Wireless Netw. 16, 4 (May 2010), 985--997.

Digital Library

[8]

L. Erico, M. Carlos, M. Ricardo, P. Paulo, and V. Francisco. 2017. Superframe duration allocation schemes to improve the throughput of cluster-tree wireless sensor networks. Sensors 17, 2 (2017). Retrieved from http://www.mdpi.com/1424-8220/17/2/249.

[9]

G. Franchino, G. Buttazzo, and M. Marinoni. 2016. Bandwidth optimization and energy management in real-time wireless networks. ACM Trans. Embed. Comput. Syst. 15, 3 (March 2016).

Digital Library

[10]

C. Hanen and Z. Hanzálek. 2013. Grouping tasks to save energy in a cyclic scheduling problem: A complexity study. In Proceedings of the 6th Multidisciplinary International Conference on Scheduling: Theory and Applications.

[11]

Z. Hanzálek and P. Jurčík. 2010. Energy efficient scheduling for cluster-tree wireless sensor networks with time-bounded data flows: Application to IEEE 802.15.4/ZigBee. IEEE Trans. Industr. Info. 6, 3 (Aug. 2010).

[12]

P. Huang, L. Xiao, S. Soltani, M. W. Mutka, and N. Xi. 2013. The evolution of MAC protocols in wireless sensor networks: A survey. IEEE Commun. Surveys Tutor. 15, 1 (2013), 101--120.

[13]

S. Kumar and H. Kim. 2019. Energy efficient scheduling in wireless sensor networks for periodic data gathering. IEEE Access 7 (2019), 11410--11426.

[14]

C. Lenzen and R. Wattenhofer. 2011. Distributed algorithms for sensor networks. Philos. Trans. Roy. Soc. London A: Math. Phys. Eng. Sci. 370, 1958 (2011), 11--26.

[15]

J. Long, M. Dong, K. Ota, and A. Liu. 2015. A green TDMA scheduling algorithm for prolonging lifetime in wireless sensor networks. IEEE Syst. J. 11, 2 (2017), 868–877.

[16]

K. Moriyama and Y. Zhang. 2015. An efficient distributed TDMA MAC protocol for large-scale and high-data-rate wireless sensor networks. In Proceedings of the IEEE 29th International Conference on Advanced Information Networking and Applications (AINA’15).

[17]

L. Palopoli, R. Passerone, and T. Rizano. 2011. Scalable offline optimization of industrial wireless sensor networks. IEEE Trans. Industr. Info. 7, 2 (May 2011), 328--339.

[18]

N. Pereira R. Severino and E. Tovar. 2014. Dynamic cluster scheduling for cluster-tree WSNs. Springerplus 3, 493.

[19]

Y. Wu, K. S. Liu, J. A. Stankovic, T. He, and S. Lin. 2016. Efficient multichannel communications in wireless sensor networks. ACM Trans. Sen. Netw. 12, 1, Article 3 (March 2016), 23 pages.

Digital Library

[20]

W. Ye, J. Heidemann, and D. Estrin. 2002. In Proceedings Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, Vol. 3. 1567–1576.

[21]

L. Yeh and M. Pan. 2014. Beacon scheduling for broadcast and convergecast in ZigBee wireless sensor networks. Comput. Commun. 38 (2014), 1–12.

Digital Library

[22]

B. Yu, J. Li, and Y. Li. 2009. Distributed data aggregation scheduling in wireless sensor networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’09). 2159--2167.

[23]

T. Zheng, M. Gidlund, and J. Kerberg. 2014. Medium access protocol design for time-critical applications in wireless sensor networks. In Proceedings of the 10th IEEE Workshop on Factory Communication Systems (WFCS’14).

Cited By

Zhao CTang BHuang YFu H(2023)Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration MonitoringIEEE Internet of Things Journal10.1109/JIOT.2022.322719310:7(6444-6455)Online publication date: 1-Apr-2023
https://doi.org/10.1109/JIOT.2022.3227193
Sharma PBhardwaj KSingh M(2023)ZigBee based Appliances Controlling System: Design and Implementation2023 8th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES57224.2023.10192701(66-73)Online publication date: 1-Jun-2023
https://doi.org/10.1109/ICCES57224.2023.10192701
Malik AKushwah R(2023)Energy-efficient scheduling in IoT using Wi-Fi and ZigBee cross-technologyThe Journal of Supercomputing10.1007/s11227-023-05093-779:10(10977-11006)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11227-023-05093-7
Show More Cited By

Index Terms

Recommendations

A traffic aware, energy-efficient MAC protocol for Wireless Sensor Networks

In this paper, we propose TA-MAC, a traffic load adaptive, energy-efficient Medium Access Control (MAC) protocol for Wireless Sensor Networks (WSNs). TA-MAC modifies the contention window mechanism of S-MAC, a mostly well-known MAC scheme for sensor ...
Energy efficient constant cluster node scheduling protocol for wireless sensor networks

The Wireless sensor network (WSN) consisting of a large number of sensors are effective for gathering data in a variety of environments. Since the sensors operate on battery of limited power, it is a great challenging aim to design an energy efficient ...
Tree TDMA MAC Algorithm Using Time and Frequency Slot Allocations in Tree-Based WSNs

In this paper, we propose a tree-based time division multiple access (Tree TDMA) media access control (MAC) algorithm based on the IEEE 802.15.4 PHY standard. The method involves the simultaneous use of two algorithms, a time slot allocation algorithm (...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 16, Issue 1

February 2020

351 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3368392

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2019 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 18 October 2019

Accepted: 01 August 2019

Revised: 01 March 2019

Received: 01 January 2018

Published in TOSN Volume 16, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

ECSEL Joint Undertaking
European Commission and the Ministry of Education of the Czech Republic under the project Arrowhead Tools
European Regional Development Fund under the project AI 8 Reasoning

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
345
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)1

Reflects downloads up to 14 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhao CTang BHuang YFu H(2023)Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration MonitoringIEEE Internet of Things Journal10.1109/JIOT.2022.322719310:7(6444-6455)Online publication date: 1-Apr-2023
https://doi.org/10.1109/JIOT.2022.3227193
Sharma PBhardwaj KSingh M(2023)ZigBee based Appliances Controlling System: Design and Implementation2023 8th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES57224.2023.10192701(66-73)Online publication date: 1-Jun-2023
https://doi.org/10.1109/ICCES57224.2023.10192701
Malik AKushwah R(2023)Energy-efficient scheduling in IoT using Wi-Fi and ZigBee cross-technologyThe Journal of Supercomputing10.1007/s11227-023-05093-779:10(10977-11006)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11227-023-05093-7
Yu HChin KSoh S(2022)Charging RF-Energy Harvesting Devices in IoT Networks With Imperfect CSIIEEE Internet of Things Journal10.1109/JIOT.2022.31610239:18(17808-17820)Online publication date: 15-Sep-2022
https://doi.org/10.1109/JIOT.2022.3161023
Kirner RPuschner P(2021)A Quantitative Analysis of Interfaces to Time-Triggered Communication BusesIEEE/ACM Transactions on Networking10.1109/TNET.2021.307346029:4(1786-1797)Online publication date: 28-Apr-2021
https://dl.acm.org/doi/10.1109/TNET.2021.3073460

View Options

Get Access

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents