Energy-Efficient Timing Assignment of Tasks to Actors in WSANs

Okhovvat, M. R.; Kheirabadi, M. T.; Nodehi, A.; Okhovvat, M.

doi:10.22044/jadm.2022.11783.2325

Document Type : Original/Review Paper

Authors

¹ Department of Computer Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran.

² Health Management and Social Development Research Center, Golestan University of Medical Sciences, Gorgan, Iran.

https://doi.org/10.22044/jadm.2022.11783.2325

Abstract

Minimizing make-span and maximizing remaining energy are usually of chief importance in the applications of wireless sensor actor networks (WSANs). Current task assignment approaches are typically concerned with one of the timing or energy constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, an optimized and type aware task assignment approach called TATA is proposed that considers the energy consumption as well as the make-span. TATA is an optimized task assignment approach and aware of the distribution necessities of WSANs with hybrid architecture. TATA comprises of two protocols, namely a Make-span Calculation Protocol (MaSC) and an Energy Consumption Calculation Protocol (ECal). Through considering both time and energy, TATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors. A series of extensive simulation results on typical scenarios show shorter make-span and larger remaining energy in comparison to when stochastic task assignment (STA), opportunistic load balancing (OLB), and task assignment algorithm based on quasi-Newton interior point (TA-QNIP) approaches is applied.

Keywords

20.1001.1.23225211.2022.10.3.1.3

References

[1] S.K. Mothku and R.R. Rout, “Markov decision process and network coding for reliable data transmission in wireless sensor and actor networks,” Pervasive and Mobile Computing, Vol. 56, pp. 29-44, 2019.

[2] M. Okhovvat and M.R. Kangavari, “TSLBS: A Time-Sensitive and Load Balanced Scheduling Approach to Wireless Sensor Actor Networks,” Computer System Science and Engineering, Vol. 34, No.1, pp. 13-21, 2019.

[3] A. Nayak and I. Stojmenovic, “Wireless Sensor and Actuator Networks: Algorithms and Protocols for Scal

able Coordination and Data Communication,” Hoboken, New Jersey: Wiley Press, 2010.

[4] R. Shams, P. Otero, M. Aamir and F. H. Khan, “Joint Algorithm for Multi-Hop Localization and Time Synchronization in Underwater Sensors Networks Using Single Anchor,” IEEE Access, Vol. 9, pp. 27945-27958, 2021.

[5] A. Khosravi, A. Alfi and A. Roshandel, “Delay-dependent stability for transparent bilateral teleoperation system: an LMI approach,” Journal of AI and Data Mining, Vol. 1, No. 2, pp. 75-87, 2013.

[6] S.M. Dima, C. Antonopoulos and S. Koubias, “Resource Aware Sensor-to-Actor Assignment Framework for WSANs Based on Voronoi Cells Theory,” Journal of Sensors, Vol. 2017, pp. 1-16, 2017.

[7] M. Sharifi and M. Okhovvat, “Scate: A Scalable Time and Energy Aware Actor Task Assignment Algorithm in Wireless Sensor and Actor Networks,” ETRI Journal, Vol. 34, No. 3, pp. 330-340, 2012.

[8] M. Arnesano, B. Bueno, A. Pracucci, S. Magnagni, O. Casadei and G. M. Revel, “Sensors and control solutions for Smart-IoT façade modules,” in IEEE International Symposium on Measurements & Networking (M&N), pp. 1-6, 2019.

[9] U. Fischer-Hirchert, P. Kussmann, S. Hoppstock and M. Haupt, “Real-time Intelligent Tele-Care Assistance Systems,” Broadband Coverage in Germany; 12th ITG-Symposium, pp. 1-4, 2018.

[10] C. Konstantopoulos, G. Pantziou, I.E. Venetis and D. Gavalas, “Efficient event handling in Wireless Sensor and Actor Networks: An on-line computation approach,” Journal of Network and Computer Applications, Vol. 75, pp. 181-199, 2016.

[11] V. P. Modekurthy, A. Saifullah and S. Madria, “DistributedHART: A Distributed Real-Time Scheduling System for WirelessHART Networks,” in IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 216-227, 2019.

[12] H. Byun and J. So, "Node Scheduling Control Inspired by Epidemic Theory for Data Dissemination in Wireless Sensor-Actuator Networks with Delay Constraints," IEEE Transactions on Wireless Communications, Vol. 15, No. 3, pp. 1794-1807, 2016.

[13] M.R. Okhovvat, M.T. Kheirabadi, A. Nodehi and M. Okhovvat, “Task Assignment Approach for Minimizing Make-Span in Wireless Sensor Actor Networks,” Computer Systems Science and Technology, Vol. 39, No. 2, pp.165-178, 2021.

[14] X. Kong, Y. Gao, T. Wang, J. Liu and W. Xu, “Multi-robot Task Assignment Strategy based on Particle Swarm Optimization and Greedy Algorithm,” in IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 1643-1646, 2019.

[15] Y. Huang, Y. Zhang and H. Xiao, “Multi-robot system task assignment mechanism for smart factory,” in Conference (ITAIC), pp. 587-591, 2019.

[16] L. Wang, M. Liu and M. Q. Meng, "A Hierarchical Auction-Based Mechanism for Real-Time Resource Assignment in Cloud Robotic Systems," IEEE Transactions on Cybernetics, Vol. 47, No. 2, pp. 473-484, 2017.

[17] M. Younis, K. Akkaya and A. Kunjithapatham, “Optimization of Task Assignment in a Cluster-based Sensor Network,” In Proceeding of 8th IEEE International Symposium on Computers and Communication, pp. 329-334, 2003.

[18] G. Bolch, S. Greiner, H. De Meer and KS. Trivedi, “Queuing Networks and Markov Chains,” Wiley, 2nd Edition, USA, 2006.

[19] J. Kim, A. Dudin, S. Dudin and C. Kim, “Analysis of a semi-open queuing
network with Markovian arrival process,” Performance Evaluation, Vol. 120, pp. 1-19, 2018.

[20] P. L. Burke, “The Output of a Queuing System,” Operation Research, Vol. 4, No. 6, pp. 699-704, 1956.

[21] J. Xue, Z. Wang, Y. Zhang and L. Wang, "Task Assignment Optimization Scheme Based on Queuing Theory for Mobile Edge Computing in 5G Heterogeneous Networks," Mobile Information Systems, Vol. 2020, pp. 1-12, 2020.

[22] M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen and R. F. Freund, Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems, Journal of Parallel and Distributed Computing, Vol. 59, pp. 107-121, 1999.

[23] S. C. Sarin, B. Nagarajan and L. Liao, “Stochastic Scheduling: Expectation-Variance Analysis of a Schedule,” Cambridge: Cambridge University Press, 2010.

Energy-Efficient Timing Assignment of Tasks to Actors in WSANs

References

References

Volume 10, Issue 3July 2022Pages 303-310

Volume 10, Issue 3
July 2022
Pages 303-310