Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
S B Goyal
 -- 1527 2022-07-15 02:52:21 |
2 format corrected.
Beatrix Zheng
 Meta information modification 1527 2022-07-15 16:49:42 | |
3 format corrected.
Beatrix Zheng
 + 15 word(s) 1542 2022-07-15 16:51:42 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Khan, M.;  Ilavendhan, A.;  Babu, C.N.K.;  Jain, V.;  Goyal, S.B.;  Verma, C.;  Safirescu, C.O.;  Mihaltan, T.C. Clustering Based Optimal Cluster Head Selection. Encyclopedia. Available online: https://encyclopedia.pub/entry/25173 (accessed on 21 December 2024).
Khan M,  Ilavendhan A,  Babu CNK,  Jain V,  Goyal SB,  Verma C, et al. Clustering Based Optimal Cluster Head Selection. Encyclopedia. Available at: https://encyclopedia.pub/entry/25173. Accessed December 21, 2024.
Khan, Mudassir, A. Ilavendhan, C. Nelson Kennedy Babu, Vishal Jain, S. B. Goyal, Chaman Verma, Calin Ovidiu Safirescu, Traian Candin Mihaltan. "Clustering Based Optimal Cluster Head Selection" Encyclopedia, https://encyclopedia.pub/entry/25173 (accessed December 21, 2024).
Khan, M.,  Ilavendhan, A.,  Babu, C.N.K.,  Jain, V.,  Goyal, S.B.,  Verma, C.,  Safirescu, C.O., & Mihaltan, T.C. (2022, July 15). Clustering Based Optimal Cluster Head Selection. In Encyclopedia. https://encyclopedia.pub/entry/25173
Khan, Mudassir, et al. "Clustering Based Optimal Cluster Head Selection." Encyclopedia. Web. 15 July, 2022.
Clustering Based Optimal Cluster Head Selection
Edit
This entry is adapted from the peer-reviewed paper 10.3390/en15134528

The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT.

RPL fish swarm bio-inspired approach energy optimization grid formation convolution clustering data transmission cluster head alive and dead node

1. Introduction

Every object should be smart in today’s technological world. The Internet of Things (IoT) is a new paradigm that gives common objects and things a whole new dimension. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT. It has a high impact on usage in everyday life [1]. The usage at home, industry and institutions is growing exponentially every day. It is considered one of the most influential technologies of the modern era. The devices added to IoT are growing in leaps and bounds every day. Homes, classrooms and cities are becoming smart with IoT [2][3].
IoT is a new paradigm that connects computers, humans, devices and objects together for communication. There are many definitions given for IoT [4]. A popular definition is: IoT is a connection between the physical and digital worlds. Sensors and actuators are used to connect the digital and physical worlds. IoT is a concept in which computing and networking capabilities are incorporated virtually into any device. The capabilities are used to query the state of the object as well as change it, if possible. IoT is the networking of persons, things, objects and devices that communicate with each other to achieve a complex task, where a high degree of collective intelligence is required [5]. For computing and communications, IoT makes use of sensors, actuators, transceivers and processors. IoT cannot be considered as a single or standalone technology. It is a large collection of connected technology that works synchronously [6].
The network layer in IoT performs the major task of establishing connections among nodes and the server. It is the core layer that does the addressing, routing, formation and maintenance of the network. The network layer has protocols that perform the connectivity and networking tasks. The protocols of the network layer are: IPv4, IPv6, 6LoWPAN, 6TiSCH, 6Lo, IPv6 over Bluetooth Low Energy, IPv6 over G.9959, etc. [7]. IoT consists of LLNs that have low power, low energy and scarce computing resources. The conventional routing protocols for the networks may not be suitable for LLNs. The network layer protocols available for IoT are IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)​ [8], Cognitive RPL (CORPL) [9], Channel Aware Routing Protocol (CARP) [10], Enhancement over CARP (E-CARP) [11] and others.
RPL has become a standard routing protocol suitable for LLNs due to the following characteristics in comparison with the rest: (i) RPL has a better packet reception ratio (PRR) and energy consumption; (ii) it has lesser churn and control traffic overhead; (iii) it had a shorter convergence time; (iv) it is independent of the link layer. Additionally, RPL has the following features: (i) self-healing; (ii) auto-configuration; (iii) Loop avoidance and detection; (iv) independence and transparency; (v) multiple edge routers [12].
Various features of RPL remain the main reason for preferring RPL over other protocols. In spite of its features, RPL also has a lot of room for improvement since the IoT is exponentially growing, and improved routing support is required [13]. Various enhancement methods have been devised for the RPL based on its function and application. Each enhancement method focuses on improving any one of the limitations of RPL or adding more effectiveness to the existing function of RPL [14].

2. The Standardization of RPL

Several reports have been published in 2009 and 2010 [15][16] to identify the routing requirements for the standardization of RPL based on its application in various routing environments. The widespread usage of RPL and its customisation has necessitated substantial study and development. The control packets in the network are necessary to establish a connection and maintain the network. The frequent change and resetting of the network in a mobile setup led to overhead in the link. An efficient way of detecting and controlling congestion is required [17]. LLNs are backbone networks of IoT. They are constrained by energy, memory and processing capacity. The traditional and popular network protocols are not suitable for LLNs due to these constraints. Among the existing routing protocols, RPL is more suitable for LLNs, due to its special features such as auto configuration, self-healing, loop avoidance, multiple edge routers and robustness [18]. RPL is also easily malleable to various environments of LLNs. This section presents an overview of RPL with the background, characteristics and various components.
The Internet Engineering Task Force (IETF) envisioned the standardization of IPv6RPL and started the Routing over Low-power and Lossy networks (ROLL) working group in 2008. The working group aimed at the standardization of RPL, which has the following implicit characteristics [19]: (i) LLNs are constituted by hundreds of nodes that are constrained by energy, size, processing power and memory. (ii) The constrained nodes of LLNs are connected to each other through lossy links, which have low data rates and are unstable. (iii) The traffic patterns of these LLNs may be point-to-multipoint, multipoint-to-point or, in some cases, point-to-point [20], such as urban settings [21], industrial settings, home automation and building automation [22].
Multi-hop WSN node restrictions are closer to BS’s demand to infuse traffic from some other channel, enabling their energy to be spent quicker and possibly leading to very high remaining energy. As a consequence, Distributed Wedge Merging in Multi-Hop Access (DWMA) is presented here as a possible solution to the energy hole problem and routing. The major objective is to remove energy gaps while reducing the likelihood of them emerging in the future. To avoid energy holes from occurring, this DWMA method is combined with a nearby wedge [23].
In heterogeneous networks, violating the response and broadcast buffer specifications has resulted in uneven traffic loads, congestion and, as a result, packet loss in RPL, according to the author. This research discusses the CBR-RPL technique, which uses a unique drop-aware Objective Function (OF) to arrange nodes into route data. The newly defined OF takes into account both queue occupancy and node transceiver drop rates [24]. The Energy Hole problem, which is common in WSN, drastically affects the lifetime of any established network. The energy diffusion required for data packet forwarding between HN is reduced when a good Head Node (HN) selection technique is used [25].
PEGASIS (Power-Efficient Gathering in Sensor Information Systems) is an energy-saving protocol that tries to extend the network’s lifespan by reducing energy consumption. This research proposes a modification of the PEGASIS approach. SNs are sorted into groups, clustering is carried out using the k-means method and every group is assigned the PEGASIS label. Rechargeable sensor nodes were also used in the suggested strategy. The sensor node’s Euclidean distance from the base station and the sensor node’s residual energy are utilised to determine the chain leader. Every CH’s datum is instantly forwarded to the BS [26]. Various surveys on energy use, energy gaps and attacks on RPL and LLP systems can be found in [27][28][29][30]. The glowworm swarm-based approach cast-off energy-based transmission strategy has been presented to decrease energy consumption caused by control overhead [31]. A least-square support vector machine (LS-SVM) based on modified particle swarm optimization (MPSO) is developed. To begin, the MPSO’s inertial weight is adjusted to accomplish faster iterations, and an LS-SVM-based MPSO’s prediction model is constructed. Second, the predictive simulation was performed and confirmed using the MPSO’s optimised parameters, and the MPSO and PSO predicted values were compared [32]. This work introduces a resilient clustering routing mechanism for WSNs. To estimate the number of cluster heads and identify the best cluster heads, the technique employs the Locust Search (LS-II) approach. After the cluster heads have been identified, other sensor elements are allocated to the cluster heads that are closest to them [33]. Based on the Optimal Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, a methodology for an energy-efficient clustering algorithm for gathering and transferring data is developed. The new optimised threshold function is used in the selection of CH. LEACH, on the other hand, is a hierarchy routing protocol that picks cluster head nodes at random in a loop, resulting in a higher cluster headcount but higher power consumption. In order to improve the energy per unit node and packet delivery ratio with less energy use, the Centralised Low-Energy Adaptive Clustering Hierarchy Protocol is the best [34]. WSNs are designed for specialised applications, such as monitoring or tracking, in both indoor and outdoor conditions, where battery capacity is a major issue. Several routing protocols are designed to solve this problem. A sub-cluster LEACH-derived approach is also proposed in order to improve performance. The Sub-LEACH with LMNN surpassed its competitors in terms of energy efficiency, according to simulation data [35].

References

  1. Ul Hassan, T.; Asim, M.; Baker, T.; Hassan, J.; Tariq, N. CTrust-RPL: A control layer-based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks-based Internet of Things applications. Trans. Emerg. Telecommun. Technol. 2021, 32, e4224.
  2. Kim, H.; Kim, H.S.; Bahk, S. MobiRPL: Adaptive, robust, and RSSI-based mobile routing in low power and lossy networks. J. Commun. Netw. 2022, 1–19, early access.
  3. Garg, S.; Mehrotra, D.; Pandey, S.; Pandey, H.M. Network efficient topology for low power and lossy networks in smart corridor design using RPL. Int. J. Pervasive Comput. Commun. 2021. ahead-of-print.
  4. Tharini, V.J.; Vijayarani, S. IoT in healthcare: Ecosystem, pillars, design challenges, applications, vulnerabilities, privacy, and security concerns. In Incorporating the Internet of Things in Healthcare Applications and Wearable Devices; IGI Global: Hershey, PA, USA, 2020; pp. 1–22.
  5. Hua, J.; Shunwuritu, N. Research on term extraction technology in the computer field based on wireless network technology. Microprocess. Microsyst. 2021, 80, 103336.
  6. Mabrouki, J.; Azrour, M.; Dhiba, D.; Farhaoui, Y.; El Hajjaji, S. IoT-based data logger for weather monitoring using Arduino-based wireless sensor networks with remote graphical applications and alerts. Big Data Min. Anal. 2021, 4, 25–32.
  7. Lecluyse, C.; Minnaert, B.; Kleemann, M. A Review of the Current State of Technology of Capacitive Wireless Power Transfer. Energies 2021, 14, 5862.
  8. Mogensen, R.S.; Rodriguez, I.; Schou, C.; Mortensen, S.; Sørensen, M.S. Evaluation of the impact of wireless communication in production via factory digital twins. Manuf. Lett. 2021, 28, 1–5.
  9. Aijaz, A.; Su, H.; Aghvami, A.H. CORPL: A routing protocol for cognitive radio enabled AMI networks. IEEE Trans. Smart Grid 2014, 6, 477–485.
  10. Basagni, S.; Petrioli, C.; Petroccia, R.; Spaccini, D. CARP: A channel-aware routing protocol for underwater acoustic wireless networks. Ad Hoc Netw. 2015, 34, 92–104.
  11. Zhou, Z.; Yao, B.; Xing, R.; Shu, L.; Bu, S. E-CARP: An energy efficient routing protocol for UWSNs in the internet of underwater things. IEEE Sens. J. 2015, 16, 4072–4082.
  12. Knežević, Ž.; Beck, N.; Milković, Đ.; Miljanić, S.; Ranogajec-Komor, M. Characterisation of RPL and TL dosimetry systems and comparison in medical dosimetry applications. Radiat. Meas. 2011, 46, 1582–1585.
  13. Safaei, B.; Salehi, A.A.M.; Monazzah, A.M.H.; Ejlali, A. Effects of RPL objective functions on the primitive characteristics of mobile and static IoT infrastructures. Microprocess. Microsyst. 2019, 69, 79–91.
  14. Zhang, L.; Li, C.; Shi, H.; Xia, Y. Techniques to improve the hit rate of unicast node-to-node (n2n) delivery in channel-hopping and multi-hop low-power and lossy networks (LLNS). Tech. Discl. Commons 2021, 4097, 1–9. Available online: https://www.tdcommons.org/dpubs_series/4097 (accessed on 28 May 2022).
  15. Jara, A.J.; Zamora, M.A.; Skarmeta, A.F. HWSN6: Hospital wireless sensor networks based on 6LoWPAN technology: Mobility and fault tolerance management. In Proceedings of the 2009 International Conference on Computational Science and Engineering, Vancouver, BC, Canada, 29–31 August 2009; Volume 2, pp. 879–884.
  16. Islam, M.M.; Hassan, M.M.; Huh, E.N. Sensor proxy mobile IPv6 (SPMIPv6)-A mobility-supported framework IP-WSN. In Proceedings of the 2010 13th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 23–25 December 2010; pp. 295–299.
  17. Jara, A.J.; Zamora, M.A.; Skarmeta, A.F. An Initial Approach to Support Mobility in Hospital Wireless Sensor Networks Based on 6LoWPAN (HWSN6). J. Wirel. Mob.Netw. UbiquitousComput. Dependable Appl. 2010, 1, 107–122.
  18. Petäjäjärvi, J.; Karvonen, H. Soft handover method for mobile wireless sensor networks based on 6lowpan. In Proceedings of the 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), Barcelona, Spain, 27–29 June 2011; pp. 1–6.
  19. Ha, M.; Kim, D.; Kim, S.H.; Hong, S. Inter-MARIO: A fast and seamless mobility protocol to support inter-PAN handover in 6LoWPAN. In Proceedings of the 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, FL, USA, 6–10 December 2010; pp. 1–6.
  20. Koster, V.; Dorn, D.; Lewandowski, A.; Wietfeld, C. A novel approach for combining Micro and Macro Mobility in 6LoWPAN enabled Networks. In Proceedings of the 2011 IEEE Vehicular Technology Conference (VTC Fall), San Francisco, CA, USA, 5–8 September 2011; pp. 1–5.
  21. Bag, G.; Mukhtar, H.; Shams, S.S.; Kim, K.H.; Yoo, S.W. Inter-PAN mobility support for 6LoWPAN. In Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology, Busan, Korea, 11–13 November 2008; Volume 1, pp. 787–792.
  22. Bag, G.; Raza, M.T.; Kim, K.H.; Yoo, S.W. LoWMob: Intra-PAN mobility support schemes for 6LoWPAN. Sensors 2009, 9, 5844–5877.
  23. Saravanakumar, V.; DWMA: An Energy Hole Reduction Mechanism on RPL for 6LoWPAN. EasyChair Prepr. 2020. Available online: https://easychair.org/publications/preprint/n6hC (accessed on 28 May 2022).
  24. Shirbeigi, M.; Safaei, B.; Mohammadsalehi, A.; Monazzah, A.M.H.; Henkel, J.; Ejlali, A. A cluster-based and drop-aware extension of RPL to provide reliability in IoT applications. In Proceedings of the 2021 IEEE International Systems Conference (SysCon), Vancouver, BC, Canada, 15 April–15 May 2021; pp. 1–7.
  25. Sharmin, N.; Karmaker, A.; Lambert, W.L.; Alam, M.S.; Shawkat, M.S.T. Minimizing the energy hole problem in wireless sensor networks: A wedge merging approach. Sensors 2020, 20, 277.
  26. Elsheikh, A.H.; Abd Elaziz, M.; Vendan, A. Modeling ultrasonic welding of polymers using an optimized artificial intelligence model using a gradient-based optimizer. Weld. World 2021, 66, 1–18.
  27. Abd Elaziz, M.; Elsheikh, A.H.; Oliva, D.; Abualigah, L.; Lu, S.; Ewees, A.A. Advanced metaheuristic techniques for mechanical design problems. Arch. Comput. Methods Eng. 2022, 29, 695–716.
  28. Bhale, P.; Dey, S.; Biswas, S.; Nandi, S. Energy-efficient approach to detect sinkhole attack using roving IDS in 6LoWPAN network. In International Conference on Innovations for Community Services; Springer: Cham, Switzerland, 2020; pp. 187–207.
  29. Sujatha, R.; Srivaramangai, P. Performance Comparison of Black Hole Attack Detection Mechanism in 6lowpan over Manet. Int. J. Adv. Res. Comput. Sci. 2018, 9.
  30. Nandi, S. Energy-Efficient Approach to Detect Sinkhole Attack Using Roving IDS in 6LoWPAN Network. In Innovations for Community Services: 20th International Conference, I4CS 2020, Bhubaneswar, India, 12–14 January 2020, Proceedings; Springer Nature: Berlin/Heidelberg, Germany, 2019; Volume 1139, p. 187.
  31. Sampathkumar, A.; Mulerikkal, J.; Sivaram, M. Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks. Wirel. Netw. 2020, 26, 4227–4238.
  32. Liu, G.; Zhu, H. Displacement Estimation of Six-Pole Hybrid Magnetic Bearing Using Modified Particle Swarm Optimization Support Vector Machine. Energies 2022, 15, 1610.
  33. Rodríguez, A.; Pérez-Cisneros, M.; Rosas-Caro, J.C.; Del-Valle-Soto, C.; Gálvez, J.; Cuevas, E. Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme. Energies 2021, 14, 3019.
  34. Bharany, S.; Sharma, S.; Badotra, S.; Khalaf, O.I.; Alotaibi, Y.; Alghamdi, S.; Alassery, F. Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol. Energies 2021, 14, 6016.
  35. Mittal, M.; De Prado, R.P.; Kawai, Y.; Nakajima, S.; Muñoz-Expósito, J.E. Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks. Energies 2021, 14, 3125.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Computer Science, Interdisciplinary Applications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mudassir Khan
,
A. Ilavendhan
,
C. Nelson Kennedy Babu
,
Vishal Jain
,
S. B. Goyal
,
Chaman Verma
,
Calin Ovidiu Safirescu
,
Traian Candin Mihaltan
View Times: 731
Entry Collection: Remote Sensing Data Fusion
Revisions: 3 times (View History)
Update Date: 15 Jul 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback