Open access peer-reviewed chapter

Energy Management in Wireless Sensor Network

Written By

Tareq Alhmiedat

Submitted: 12 January 2022 Reviewed: 22 March 2022 Published: 12 June 2022

DOI: 10.5772/intechopen.104618

From the Edited Volume

Emerging Trends in Wireless Sensor Networks

Edited by Venkata Krishna Parimala

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Abstract

Usually, wireless sensor networks (WSNs) are installed in large areas to monitor various physical conditions of the environment and forward the collected sensed data to a base station (central node), for instance: gas monitoring, intrusion detection, tracking objects, etc. However, sensor nodes are usually deployed unattended and battery-powered with no external power source. Therefore, WSNs face the challenge of limited energy source available onboard, where packet transmission and sensing functions are the most power consumption factors in WSN. Therefore, to overcome the energy depletion in sensor nodes, it is important to study the energy management issue in WSN. In this chapter, the significance of energy management issue is discussed first, and then the possible energy management strategies for WSN are presented and illustrated.

Keywords

  • energy management
  • wireless sensor networks (WSNs)
  • power consumption
  • energy management strategies
  • data aggregation
  • clustering

1. Introduction

A wireless sensor network (WSN) is made up of a set of sensor devices (nodes), which are usually powered by batteries to operate and interconnected through radio links to assure data transmission, processing, and reception. In general, WSNs have a significant potential in different applications in the areas of medical sciences, telecommunications, agriculture, environmental sciences, military services, and surveillance. The increasing demand on the deployment of autonomous sensor nodes and extending the sensor network lifetime can therefore be considered among the main objectives through examining interesting methods and research studies, which optimize the WSN energy consumption, and proposing methods to improve it. These methods can include several action levels that can range from the deployment stage to the information processing and manipulation stage [1].

In general, WSN is a combination of distributed self-governing sensor nodes, which monitor environmental and physical certain conditions, for instance: monitoring humidity, temperature, pressure, etc., and transfer such data through multihop network to the base station. WSN is considered as attractive solutions for many applications in fields [2, 3, 4, 5, 6, 7, 8, 9]. Figure 1 depicts an environmental sensor nodes deployed in a forest area, where sensor nodes may transmit the sensed data through multihop communication to the sink node (base station). The energy capability for the sensor nodes allows them to work autonomously and can communicate with other nodes through radio waves through the establishment of the routing mechanisms [10].

Figure 1.

Energy management.

Recently, an intensive research has studied and addressed the energy consumption issue in WSNs, as the sensor networks have been employed in various types of applications, where it is difficult in certain cases to replace or recharge the attached battery source. In addition, sensor nodes are expected to work from months to a few years. Therefore, it is significant to develop an energy efficient hardware and software components to allow the WSNs to operate for the maximum period of time. This chapter focuses on the power management issue in WSNs and discusses several power management schemes that aim to minimize the power consumption for sensor nodes.

The rest of this chapter is organized as follows: Section 2 discusses the energy management issue in WSNs, whereas Section 3 presents the energy management strategies that can be adopted to minimize the power consumption for sensor nodes in the WSN. And, finally, Section 4 concludes the work presented in this chapter.

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2. Energy management in WSNs

This section discusses the main energy management considerations when designing or developing an algorithm for WSNs. In general, a sensor network consists of a sensor nodes linked to each other using wireless communication protocol. Usually, a sensor network involves various types of nodes with different capabilities (memory size, on-board battery capacity, and processor speed). For instance, Figure 2 shows a sensor network with three different types of nodes (coordinator, router, and end-device) that exist in the ZigBee communication protocol. Usually, a single coordinator is required to start and coordinate the WSN, whereas a number of active routers are required to forward sensed data in the WSN through multihop communication, and a large number of end-device nodes are expected in the WSN, where end-device node may go to sleep mode.

Figure 2.

Mesh WSN with three different sensor nodes.

According to the different energy consumption levels in the WSN based on the type of sensor node that employed in the area of interest, it is important to study the field of energy management to minimize the power consumption for sensor network. As presented in Figure 3, the energy management is based on two main considerations: energy consumption and energy provision. The former focuses on the operations and devices that deplete the energy through performing transmission, reception, and data processing, whereas the later intends to discover different methods for supplying the sensor node with the required energy source in order to allow the WSN to operate as long as possible.

Figure 3.

Energy management in WSN.

The energy provision is further classified into: battery-driven, transference, and harvesting. The battery-driven classification is based on the deployment of a battery source for powering the sensor node, whereas the battery might be replaceable, fixed, or rechargeable. The transference classification employs such methods for transferring energy from the source to the sensor node (destination), for instance, the employment of microwaves and radio frequency energy. The harvesting-based classification uses for instance energy from solar, wind, thermal, etc.

On the other hand, there are huge efforts have been made to design and implement an efficient energy management schemes to save the limited energy available for each sensor node. The energy consumption can be further classified into: duty-cycling, mobility-based, and data-driven. Through the duty cycle method, the sensor node can alternate between sleep and active modes in order to minimize the power consumed in the active mode. In the mobility-based method, a mobile node is employed to collect the sensed data from stationary sensor nodes, and therefore minimize the power consumed in multi-hop forwarding of data. The data-driven methods are based on prediction and aggregation algorithms to minimize the power consumed in the transmission process.

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3. Energy management schemes in WSNs

Energy management involves saving the onboard energy of sensor nodes in order to allow the sensor node to operate for the maximum lifetime possible. Through studying and analyzing the available literature, it is important to categorize the energy management schemes into four main categories as presented in Figure 4.

Figure 4.

Classification of different management energy methods for WSNs.

3.1 Battery management schemes

Battery management includes exploiting the internal characteristics of batteries to evoke their charge in order to maximize the sensor node lifetime. Therefore, in this section the battery management schemes are considered in two ways of views: node energy management and energy balancing.

Node energy management aims to allow sensor nodes to operate permanently in the WSN. Authors of [11] explored the Dynamic Power Management (DPM) strategy in WSNs that established the sleep and active modes for power management. DPM minimizes the energy consumption for each sensor node with the help of micro-operating embedded system. Moreover, several research works [12, 13, 14, 15, 16, 17] have focused on the DPM in order to reduce the energy consumption for each sensor node, hence maximizing the WSN throughput.

The energy balancing schemes on the other hand achieve a balance between the energy generation and the energy consumption. For longer WSN lifetime, the efficient and balanced power consumption is highly important. For instance, authors of [18] presented a solution for insufficient energy problem in the sensing unit and excessive usage of power in transmission unit for sensor nodes through setting up a decent harmony among them to prolong throughput up to some extent. In addition, several research works have focused on the energy balancing scheme [19, 20, 21, 22].

3.2 Transmission power management schemes

In general, data transmission is considered as the most power consumption module with comparison to sensor node’s other modules. The transmission power management schemes can be categorized into three main categories as follows: Medium Access Control (MAC) layer management, routing policies.

The MAC layer management schemes are adopted the MAC protocol to minimize the power consumption for the sensor nodes. MAC protocol is considered as the bottom segment protocol for network communication in WSNs. Several research works [23, 24, 25, 26, 27] have explored the divergent MAC protocols for enormous applications of WSNs.

The routing protocols on the other hand aim to set up the best link between the source node and the destination node, without compromising some major performance character tics. Routing protocols focus on the power saving, where several routing protocols and systems [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39] have been developed and implemented for forwarding data in WSN to reach the destination or the sink node.

3.3 System management schemes

The system power management schemes are accomplished in the processor unit using power and device management strategies. The substantial dropping in power consumption offers efficient hardware design. Moreover, the power consumption might be further minimized by some other features including turning-off the sensor node over idle situations or operating in power-saving mode. The system management schemes involve processor power management and device management.

Generally, the power consumption of the sensor node’s processor is affected by several parameters including: processor clock speed and the number of command executed per unit time. The processor power management strategy tries to minimize the number of performed calculations and the processor’s power consumption. Several research works [40, 41, 42] have adopted various power management methods, for instance: employing the power saving mode to minimize the power consumption of a certain sensor node in the WSN.

On the other hand, using intelligent mobile sensor nodes, the power management can minimize power usage considerably. The design and development of the sensor node hardware have been proposed for device management schemes, which minimize the energy consumption. Through this management technology, the intelligent device employs an operating system that aims to reduce the power consumption using various power saving modes according to the sensor node’s energy usage. Several device management systems for WSNs have been proposed recently [43, 44, 45, 46, 47, 48] with various functionalities and outcomes.

3.4 Other power management schemes

This subsection discusses other WSN management systems including: load balancing, duty cycling, and mobility-based systems.

Load balancing includes managing power usage of the transmission segment. Several data clustering approaches [49, 50, 51, 52, 53] have been developed to extend the WSN lifetime and enhance the network throughput, where a cluster head is elected in order to collect, aggregate, and then transmit the sensed data to the base station. In general, cluster-based approaches significantly minimize the power consumption for WSNs. Figure 5 shows the concept of dividing the sensor nodes in a WSN into groups, where a cluster head is selected to each sensor group. The role of the cluster head is to collect data from sensor nodes in its group, aggregate, and transmit the collected data to the sink node (base station).

Figure 5.

Clustering concept in WSN.

Duty cycling management schemes manage the power consumption to extend the WSN lifetime. Duty cycling approaches play a key role in enhancing the energy consumption and the WSN lifetime. Several algorithms have been proposed [54, 55, 56, 57, 58, 59] that estimate the duty cycle for each sensor node by switching among wakeup and sleep modes in order to minimize the total power consumption for each sensor node.

The mobility-based approaches consider employing mobile sensor nodes to attain energy conservation in the WSN. In WSNs, the mobile nodes are employed to collect the sensed data from stationary sensor nodes distributed over the area of interest. Many research works [60, 61, 62, 63, 64, 65, 66, 67, 68] have conducted employing mobile sensor nodes in their studies, with the aim of minimizing the power consumption for fixed sensor nodes and minimize the multihop commination over the WSN. Figure 6 presents the concept of employing a mobile robot node in the WSN field.

Figure 6.

Employment of mobile nodes to collect the sensed data.

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4. Conclusion

A WSN is made up of a set of sensor nodes that are supplied by batteries to operate and interconnected using radio links to guarantee reception, processing, and transmission. Energy consumption is a critical issue in WSNs. Various significant challenges have been overcome to maximize the WSN lifetime, and hence increase the WSN throughput. This chapter discusses several energy management solutions for WSNs, ranging from deployment and connectivity to routing and securing information. In this chapter, the energy management schemes were divided into four main categories: battery management, system management, transmission power management, and other management schemes. Each energy management scheme was discussed, in addition to presenting several research work that support the discussed energy management scheme.

References

  1. 1. Khan JA, Qureshi HK, Iqbal A. Energy management in wireless sensor networks: A survey. Computers & Electrical Engineering. 2015;41:159-176
  2. 2. Mansour S, Nasser N, Karim L, Ali A. Wireless sensor network-based air quality monitoring system. In: 2014 International Conference on Computing, Networking and Communications (ICNC). New York: IEEE; 2014. pp. 545-550
  3. 3. Alhmiedat TA, Omar F, Taleb AA, Alsswey A. Road safety and energy saving proposed system: A Zigbee WSN approach. International Journal of Online Engineering. 2015;11(2):55-59
  4. 4. Alhmiedat T, Ghassan G. A low cost zigbee sensor network architecture for indoor air quality monitoring. International Journal of Computer Science & Information Security. 2017;15(1):140-144
  5. 5. Alhmiedat T, Omar F, Taleb AA. A hybrid tracking system for zigbee WSNS. In: 2014 6th International Conference on Computer Science and Information Technology (CSIT). New York: IEEE; 2014. pp. 71-74
  6. 6. Arroyo P, Herrero JL, Suárez JI, Lozano J. Wireless sensor network combined with cloud computing for air quality monitoring. Sensors. 2019;19(3):691
  7. 7. Muduli L, Mishra DP, Jana PK. Application of wireless sensor network for environmental monitoring in underground coal mines: A systematic review. Journal of Network and Computer Applications. 2018;106:48-67
  8. 8. Alhmiedat T, Salem AA. A hybrid range-free localization algorithm for zigbee wireless sensor networks. International Arab Journal of Information Technology (IAJIT). 2017;14:647-653
  9. 9. Alhmiedat T. An adaptive indoor positioning algorithm for ZigBee WSN. In: Fifth International Conference on the Innovative Computing Technology (INTECH 2015). New York: IEEE; 2015. pp. 51-55
  10. 10. Singh J, Kaur R, Singh D. A survey and taxonomy on energy management schemes in wireless sensor networks. Journal of Systems Architecture. 2020;111:101782
  11. 11. Zhang Y, Li W. Modeling and energy consumption evaluation of a stochastic wireless sensor network. EURASIP Journal on Wireless Communications and Networking. 2012;2012(1):1-11
  12. 12. Jin S, Yue W, Sun Q. Performance analysis of the sleep/wakeup protocol in a wireless sensor network. International Journal of Innovative Computing Information and Control. 2012;8(5):3833-3844
  13. 13. Alhmiedat TA, Yang SH. A ZigBee-based mobile tracking system through wireless sensor networks. International Journal of Advanced Mechatronic Systems. 2008;1(1):63-70
  14. 14. Kaebeh Yaeghoobi SB, Soni MK, Tyagi SS. Dynamic and real-time sleep schedule protocols for energy efficiency in WSNs. International Journal of Computer Network and Information Security (IJCNIS). 2016;8(1):9-17
  15. 15. Shah T, Javaid N, Qureshi TN. Energy efficient sleep awake aware (EESAA) intelligent sensor network routing protocol. In: 2012 15th international multitopic conference (INMIC). New York: IEEE; 2012. pp. 317-322
  16. 16. Alhmiedat T, Samara G, Salem AO. An indoor fingerprinting localization approach for ZigBee wireless sensor networks. arXiv preprint arXiv:1308.1809. 2013
  17. 17. Alhmiedat T, Salem AO, Taleb AA. An improved decentralized approach for tracking multiple mobile targets through ZigBee WSNs. arXiv preprint arXiv:1307.3295. 2013
  18. 18. Patel S, Sherrill D, Hughes R, Hester T, Huggins N, Lie-Nemeth T, et al. Analysis of the severity of dyskinesia in patients with Parkinson’s disease via wearable sensors. In: Proceedings International Workshop on Wearable and Implantable Body Sensor Networks, IEEE Computer Society. New York: IEEE; 2006. pp. 123-126
  19. 19. Babayo AA, Anisi MH, Ali I. A Review on energy management schemes in energy harvesting wireless sensor networks. Renewable and Sustainable Energy Reviews, Elsevier. 2017;76:1176-1184
  20. 20. Ni X, Yuan D, Teng Y, Song M. Energy efficient power allocation scheme for multi-cell with hybrid energy sources. In: International Symposium on Personal, Indoor and Mobile Radio Communications—(PIMRC): Mobile and Wireless Networks. New York: IEEE; 2015. pp. 1611-1616
  21. 21. Buwaya J, Rolim J. Bounding distributed energy balancing schemes for WSNs via modular subgames. In: International Conference on Distributed Computing in Sensor Systems (DCOSS), IEEE Computer Society. 2016. pp. 153-160
  22. 22. Salem AA, Alhmiedat T. Energy-efficient clustering WSN system for environment monitoring applications. International Journal. 2020;8(5):2126-2132
  23. 23. Ye W, Heidemann J, Estrin D. An energy-efficient MAC protocol for wireless sensor networks. IEEE INFOCOM. 2002;3:1567-1576
  24. 24. Jamieson K, Balakrishnan H. Sift: A MAC protocol for event-driven wireless sensor networks. In: European Workshop on Wireless Sensor Networks. Berlin: Springer; 2003. pp. 1-23
  25. 25. Van Dam T, Langendoen K. An adaptive energy-efficient mac protocol for wireless sensor networks. In: Sensor and System. New York: ACM; 2003. pp. 1-10
  26. 26. Sohrabi K, Gao J, Ailawadhi V, Pottie GJ. Protocols for self-organization of a wireless sensor network, allerton conference on communication, computing and control. IEEE Personal Communications. 2000;16-27:1002-1006
  27. 27. Kalidindi R, Ray L, Kannan R, Iyengar S, Hall C, Rouge B. Distributed energy aware MAC Layer Protocol For Wireless Sensor Networks Louisiana State University, International Workshop on Wireless Networks 2003. pp. 1-5
  28. 28. Heinzelman WR, Chandrakasan A, Balakrishnan H. Energy-efficient Communication Protocol for Wireless Microsensor Networks. In: Proceedings of the 33rd Hawaii International Conference on System Sciences. New York: IEEE Computer Society; 2000. pp. 1-10
  29. 29. Manjeshwar A, Agrawal DP. TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In: Proc.of the 15th Parallel and Distributed Processing Symp. San Francisco: IEEE Computer Society; 2001. pp. 2009-2015
  30. 30. Aliouat Z, Harous S. Energy efficient clustering for wireless sensor networks. International Journal of Pervasive Computing and Communications. 2014;10:469-480
  31. 31. Bozorgi SM, Shokouhi Rostami A, Hosseinabadi AAR, Balas VE. A new clustering protocol for energy harvesting-wireless sensor networks. Computers and Electrical Engineering. 2017;64:233-247. DOI: 10.1016/j.compeleceng.2017.08.022
  32. 32. Kulik J, Heinzelman W. Negotiation-based protocols for disseminating information. In: Wireless Networks. Vol. 8. Kluwer Academic Publishers; 2002. pp. 169-185
  33. 33. Braginsky D, Estrin D. Rumor routing algorthim for sensor networks. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications—WSNA’02. 2002. pp. 22-23
  34. 34. Intanagonwiwat C, Govindan R, Estrin D, Heidemann J, Silva F. Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking. 2003;11:2-16
  35. 35. Karp B, Kung HT. GPSR : Greedy perimeter stateless routing for wireless networks. In: International Conference on Mobile Computing and Networking, Proceedings of the 6th Annual ACM/IEEE. 2000. pp. 234-254
  36. 36. Sanchez JA, Ruiz PM, Liu J, Stojmenovic I. Bandwidth-efficient geographic multicast routing protocol for wireless sensor networks. IEEE Sensors Journal. 2007;7:627-636
  37. 37. Shah RC, Rabaey JM. Energy aware routing for low energy ad hoc sensor networks. In: Proceedings of IEEE Wireless Communication. New York: IEEE; 2002. pp. 350-355
  38. 38. Jakobsen MK, Madsen J, Hansen MR. DEHAR : A distributed energy harvesting aware routing algorithm for Ad-Hoc multi-hop wireless sensor networks. IEEE. 2010:1-9
  39. 39. Alhmiedat T. Low-power environmental monitoring system for ZigBee wireless sensor network. KSII Transactions on Internet and Information Systems (TIIS). 2017;11(10):4781-4803
  40. 40. Zheng R, Kravets R. On-demand power management for ad hoc networks. Ad Hoc Networks. 2005;3:51-68
  41. 41. Abiodun AS, Anisi MH, Ali I, Akhunzada A, Khan MK. Reducing Power Body Area Networks. IEEE Consumer Electronics Magazine. 2017:38-47
  42. 42. Alhmiedat TA, Yang S. Tracking multiple mobile targets based on ZigBee standard. In: Proceedings of the 35th Annual Conference of the IEEE Industrial Electronics Society. 2009
  43. 43. Shimada H, Ando H, Shimada T. Pipeline stage unification: a low-energy consumption technique for future mobile processors. In: ZSLPED’O3. ACM; 2003. pp. 326-329
  44. 44. Silva A, Liu M, Moghaddam M. Power-management techniques for wireless sensor networks and similar low-power communication devices based on nonrechargeable batteries. Journal of Computer Networks and Communications. 2012;2012:1-10
  45. 45. Dargie W. Dynamic power management in wireless sensor networks: state-of-the-art. IEEE Sensors Journal. 2012;12:1518-1528
  46. 46. Tsai KL, Ye MY, Tsai SH, Wang YY, Zhuang YH. Attack-resistant power management scheme for wireless sensor network. In: 2015 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2015. 2015. pp. 1-4
  47. 47. Aloulou R, Lucas De Peslouan P-O, Mnif H, Alicalapa F, Luk JDLS, Loulou M. A power management system for energy harvesting and wireless sensor networks application based on a novel charge pump circuit. International Journal of Electronics. 2015;103:1-12
  48. 48. Pughat A, Sharma V. Performance analysis of an improved dynamic power management model in wireless sensor node. Digital Communications and Networks. 2017;3:19-29
  49. 49. Pan JS, Dao TK. A compact bat algorithm for unequal clustering in wireless sensor networks. Applied Sciences. 2019;9(10):1973
  50. 50. Ahmad B, Jian W, Ali ZA, Tanvir S, Khan MSA. Hybrid anomaly detection by using clustering for wireless sensor network. Wireless Personal Communications. 2019;106(4):1841-1853
  51. 51. Alhmiedat T. An adaptive energy-efficient data collection system for ZigBee wireless sensor networks. International Journal of Distributed Sensor Networks. 2015;11(12):734937
  52. 52. Han Y, Li G, Xu R, Su J, Li J, Wen G. Clustering the wireless sensor networks: a meta-heuristic approach. IEEE Access. 2020;8:214551-214564
  53. 53. Bozorgi SM, Bidgoli AM. HEEC: A hybrid unequal energy efficient clustering for wireless sensor networks. Wireless Networks. 2019;25(8):4751-4772
  54. 54. Joseph V, Sharma V, Mukherji U. Optimal sleep-wake policies for an energy harvesting sensor node. IEEE Communications Society. 2009:1-6
  55. 55. Zhang Y, Feng CH, Demirkol I, Heinzelman WB. Energy-efficient duty cycle assignment for receiver-based convergecast in wireless sensor networks. In: Global Telecommunications Conference (GLOBECOM) IEEE. 2010. pp. 0-4
  56. 56. Yang F, Augé-Blum I. Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSNs under real-time constraints. Computer Networks. 2011;55:497-513
  57. 57. Rout RR, Ghosh SK. Enhancement of lifetime using duty cycle and network coding in wireless sensor networks. IEEE Transactions on Wireless Communications. 2013;12:656-667
  58. 58. Shrestha N, Youn JH, Sharma N. A code-based sleep and wakeup scheduling protocol for low duty cycle sensor networks. Journal of Advances in Computer Networks. 2014;2:188-192
  59. 59. Cheng L, Niu J, Gu Y, Luo C, He T. Achieving efficient reliable flooding in low- duty-cycle wireless sensor networks. IEEE/ACM Transactions on Networking. 2016;24:1-14
  60. 60. Basagni S, Carosi A, Melachrinoudis E, Petrioli C, Wang ZM. Controlled sink mobility for prolonging wireless sensor networks lifetime. Wireless Networks. 2008;14:831-858
  61. 61. Kim HS, Abdelzaher TF, Kwon WH. Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks. ACM Sensory Systems. 2003:193-204
  62. 62. Khan AH, Jafri MR, Javaid N, Khan ZA, Qasim U, Imran M. DSM: dynamic sink mobility equipped DBR for underwater WSNs. Procedia Computer Science. 2015;52:560-567
  63. 63. Taleb AA, Alhmiedat TA, Taleb RA, Hassan OA. Sink mobility model for wireless sensor networks. Arabian Journal for Science and Engineering. 2014;39(3):1775-1784
  64. 64. Khan MA, ul Amin N. Energy efficient clustering using fixed sink mobility for wireless sensor networks. International Journal of Advanced Computer Science and Applications (IJACSA). 2016;7:505-510
  65. 65. Taleb AA, Alhmiedat T. Depth first based sink mobility model for wireless sensor networks. International Journal of Electrical and Electronics Computer Systems. 2014;19:9-14
  66. 66. Vijayalakshmi K, Manickam JML. Mobisink- intelligent mobility pattern based routing protocol for efficient data gathering in large scale wireless sensor networks. International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), IEEE. 2016:21-25
  67. 67. Taleb AA, Alhmiedat T, Hassan OA, Turab NM. A survey of sink mobility models for wireless sensor networks. Journal of Emerging Trends in Computing and Information Sciences. 2013;4(9):679-687
  68. 68. Xing G, Li M, Wang T, Jia W, Huang J. Efficient rendexvous algorithms for mobility-enabled wireless sensor networks. IEEE Transactions on Mobile Computing. 2012;11:47-60

Written By

Tareq Alhmiedat

Submitted: 12 January 2022 Reviewed: 22 March 2022 Published: 12 June 2022