Browse > Article
http://dx.doi.org/10.4218/etrij.2021-0084

An energy-efficient technique for mobile-wireless-sensor-network-based IoT  

Singla, Jatin (Electronics and Communication Engineering, Punjab Engineering College (Deemed to be University))
Mahajan, Rita (Electronics and Communication Engineering, Punjab Engineering College (Deemed to be University))
Bagai, Deepak (Electronics and Communication Engineering, Punjab Engineering College (Deemed to be University))
Publication Information
ETRI Journal / v.44, no.3, 2022 , pp. 389-399 More about this Journal
Abstract
Wireless sensor networks (WSNs) are one of the basic building blocks of Internet of Things (IoT) systems. However, the wireless sensing nodes in WSNs suffer from energy constraint issues because the replacement/recharging of the batteries of the nodes tends to be difficult. Furthermore, a number of realistic IoT scenarios, such as habitat and battlefield monitoring, contain mobile sensing elements, which makes the energy issues more critical. This research paper focuses on realistic WSN scenarios that involve mobile sensing elements with the aim of mitigating the attendant energy constraint issues using the concept of radio-frequency (RF) energy extraction. The proposed technique incorporates a cluster head election workflow for WSNs that includes mobile sensing elements capable of RF energy harvesting. The extensive simulation analysis demonstrated the higher efficacy of the proposed technique compared with the existing techniques in terms of residual energy, number of functional nodes, and network lifetime, with approximately 50% of the nodes found to be functional at the 4000th, 5000th, and 6000th rounds for the proposed technique with initial energies of 0.25, 0.5 and 1 J, respectively.
Keywords
CH selection; energy efficiency; IoT; MWSN; network lifetime; residual energy; RF energy harvesting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, Context aware computing for the internet of things: A survey, IEEE Commun. Surv. Tutorials 16 (2014), no. 1, 414-454.   DOI
2 A. Ali, Y. Ming, T. Si, S. Iram, and S. Chakraborty, Enhancement of RWSN lifetime via firework clustering algorithm validated by ANN, Inform. 9 (2018), no. 3, 1-13.   DOI
3 C. Bettstetter, H. Hartenstein, and X. Perez-Costa, Stochastic properties of the random waypoint mobility model, Wirel. Netw. 10 (2004), no. 5, 555-567.   DOI
4 P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V. C. Victor, and Y. L. Guan, Wireless energy harvesting for the Internet of Things, IEEE Commun. Mag. 53 (2015), no. 6, 102-108.   DOI
5 T. D. Ponnimbaduge Perera, D. N. K. Jayakody, S. K. Sharma, S. Chatzinotas, and J. Li, Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges, IEEE Commun. Surv. Tutorials 20 (2018), no. 1, 264-302.   DOI
6 X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, Wireless networks with rf energy harvesting: A contemporary survey, IEEE Commun. Surv. Tutorials 17 (2015), no. 2, 757-789.   DOI
7 V. Ramasamy, Mobile wireless sensor networks: An overview, In Wireless Sensor Networks: Insights and Innovations, P. J. Sallis (ed.), IntechOpen, India, 2017, pp. 3-19.
8 W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, in Proc. Annu. Hawaii Int. Conf. Syst. Sci. (Maui, HI, USA), 2000, pp. 1-10.
9 L. Nguyen and H. T. Nguyen, Mobility based network lifetime in wireless sensor networks: A review, Comput. Netw. 174 (2020).
10 J. Singh, R. Kumar, and A. K. Mishra, Clustering algorithms for wireless sensor networks: A review, in Proc. Int. Conf. Comput. Sustain. Global Development (New Delhi, India), 2015, pp. 637-642.
11 J. A. Stankovic, Research directions for the internet of things, IEEE Internet Things J. 1 (2014), no. 1, 3-9.   DOI
12 L. Qing, Q. Zhu, and M. Wang, Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks, Comput. Commun. 29 (2006), no. 12, 2230-2237.   DOI
13 J. S. Leu, T. H. Chiang, M. C. Yu, and K. W. Su, Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes, IEEE Commun. Lett. 19 (2015), no. 2, 259-262.   DOI
14 Y. K. Chiang, N. C. Wang, and C. H. Hsieh, A cycle-based data aggregation scheme for grid-based wireless sensor networks, Sensors 14 (2014), no. 5, 8447-8464.   DOI
15 J. Zhang and R. Yan, Centralized energy-efficient clustering routing protocol for mobile nodes in wireless sensor networks, IEEE Commun. Lett. 23 (2019), no. 7, 1215-1218.   DOI
16 M. Mishra, C. R. Panigrahi, B. Pati, and J. L. Sarkar, ECHS: An energy aware cluster head selection algorithm in wireless sensor networks, in Proc. Int. Conf. Man Mach. Interfacing (Bhubaneswar, India), 2015, pp. 1-4.
17 C. Xu, M. Zheng, W. Liang, H. Yu, and Y. C. Liang, End-to-end throughput maximization for underlay multi-hop cognitive radio networks with RF energy harvesting, IEEE Trans. Wirel. Commun. 16 (2017), no. 6, 3561-3572.   DOI
18 H. Farman, H. Javed, J. Ahmad, B. Jan, and M. Zeeshan, Grid-based hybrid network deployment approach for energy efficient wireless sensor networks, J. Sensors 2016 (2016).
19 T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, Residual energy based cluster-head selection in WSNs for IoT application, IEEE Internet Things J. 6 (2019), no. 3, 5132-5139.   DOI
20 H. Farman, B. Jan, H. Javed, N. Ahmad, J. Iqbal, M. Arshad, and S. Ali, Multi-criteria based zone head selection in Internet of Things based wireless sensor networks, Futur. Gener. Comput. Syst. 87 (2018), 364-371.   DOI
21 D. S. Kim and Y. J. Chung, Self-organization routing protocol supporting mobile nodes for wireless sensor network, in Proc. First Int. Multi- Symp. Comput. Comput. Sci. (Hangzhou, China), 2006, pp. 622-626.
22 P. Khandnor and T. Aseri, Threshold distance-based cluster routing protocols for static and mobile wireless sensor networks, Turkish J. Electrical Eng. Comput. Sci. 25 (2017), no. 2, 1448-1459.   DOI
23 J. Corn and J. W. Bruce, Clustering algorithm for improved network lifetime of mobile wireless sensor networks, in Proc. Int. Conf. Comput. Netw. Commun. (Silicon Valley, CA, USA), 2017, pp. 1063-1067.
24 M. Ahmad, T. Li, Z. Khan, F. Khurshid, and M. Ahmad, A novel connectivity-based LEACH-MEEC routing protocol for mobile wireless sensor network, Sensors 18 (2018), no. 12.
25 A. Ahmed, Cluster head selection algorithm for mobile wireless sensor networks, in Proc. Int. Conf. Open Source Syst. Technol. (Lahore, Pakistan), 2013, pp. 120-125.
26 S. Aslam, W. Ejaz, and M. Ibnkahla, Energy and spectral efficient cognitive radio sensor networks for internet of things, IEEE Internet Things J. 5 (2018), no. 4, 3220-3233.   DOI
27 T. N. K. Hoan and I. Koo, Multi-slot spectrum sensing schedule and transmitted energy allocation in harvested eenergy powered cognitive radio networks under secrecy constraints, IEEE Sens. J. 17 (2017), no. 7, 2231-2240.   DOI
28 P. Nintanavongsa, A survey on RF energy harvesting: Circuits and protocols, Energy Procedia 56 (2014), 414-422.   DOI
29 T. Q. Wu and H. C. Yang, On the Performance of Overlaid Wireless Sensor Transmission with RF Energy Harvesting, IEEE J. Sel. Areas Commun. 33 (2015), no. 8, 1693-1705.   DOI
30 S. K. Divakaran, D. D. Krishna, and Nasimuddin, RF energy harvesting systems: An overview and design issues, Int. J. RF Microw. Comput. Eng. 29 (2019), no. 1, 1-15.
31 H. Zhang, Y. X. Guo, Z. Zhong, and W. Wu, Cooperative integration of RF energy harvesting and dedicated WPT for wireless sensor networks, IEEE Microw. Wirel. Components Lett. 29 (2019), no. 4, 291-293.   DOI
32 P. Sasikumar and S. Khara, K-means clustering in wireless sensor networks, in Proc. Int. Conf. Comput. Intell. Commun. Netw. (Mathura, India), 2012, pp. 140-144.
33 R. Gantassi, B. Ben Gouissem, and J. Ben Othmen, Routing protocol LEACH-K using K-means algorithm in wireless sensor network, in Proc. Workshops Int. Conf. Adv. Inform. Netw. Applicat. (Caserta, Italy), 2020, pp. 299-309.
34 B. Munir and V. Dyo, On the impact of mobility on battery-less RF energy harvesting system performance, Sensors 18 (2018), no. 11.   DOI
35 L. G. Tran, H. K. Cha, and W. T. Park, RF power harvesting: a review on designing methodologies and applications, Micro Nano Syst. Lett 5 (2017), no. 1.
36 C. Jiang, D. Yuan, and Y. Zhao, Towards clustering algorithms in wireless sensor networks-a survey, in Proc. IEEE Wirel. Commun. Netw. Conf. (Budapest, Hungary), 2009. https://doi.org/10.1109/WCNC.2009.4917996   DOI
37 J. E. Ibarra-Esquer, F. F. Gonzalez-Navarro, B. L. Flores-Rios, L. Burtseva, and M. A. Astorga-Vargas, Tracking the evolution of the internet of things concept across different application domains, Sensors 17 (2017), no. 6, 1-24.   DOI
38 S. Md Zin, N. Badrul Anuar, M. Laiha Mat, and A. S. Khan Pathan, Routing protocol design for secure WSN: Review and open research issues, J. Netw. Comput. Appl. 41 (2014), no. 1, 517-530.   DOI
39 G. P. Joshi, S. Y. Nam, and S. W. Kim, Cognitive radio wireless sensor networks: Applications, challenges and research trends, Sensors 13 (2013), no. 9. https://doi.org/10.3390/s130911196   DOI
40 R. Soua and P. Minet, A survey on energy efficient techniques in wireless sensor networks, in Proc. Joint IFIP Wireless Mobile Netw. Conf. (Toulouse, France), 2011, pp. 1-9.
41 J. Singla, R. Mahajan, and D. Bagai, A survey on energy harvesting cognitive radio networks, in Proc. Edition Int. Conf. Wirel. Netw. Embed. Syst. (Rajpura, India), 2018, pp. 6-10.
42 L. Atzori, A. Iera, and G. Morabito, The internet of things: A survey, Comput. Netw. 54 (2010), no. 15, 2787-2805.   DOI