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Jaya clustering-based algorithm for multiobjective IoV network routing optimization

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Abstract

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results.

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Abbreviations

ACO:

Ant colony optimizer

Acc:

The maximum driving acceleration

CLPSO:

Comprehensive learning particle swarm optimizer

CH:

Cluster head

CM:

Cluster member

CACONET:

ACO-based clustering algorithm for VANETs

C-DRIVE:

Clustering based on direction in vehicular environment

DA-TRLD:

Dynamic aware transmission range on local traffic density

dim:

The dimensionality of the solution

GA:

Genetic algorithm

GA-ACO:

Integration between GA and ACO algorithms

GWOCNET:

Grey Wolf Optimization-Based Clustering Algorithm for vehicular ad-hoc networks

GS:

The size of grid

GOA:

The grasshopper optimization algorithm

IoV:

Internet of Vehicles

IoV-NRP:

IoV network routing problem

IoT:

Internet of Things

ITS:

Intelligent Transportation System

JAYA:

JAYA algorithm

JAYACIoV:

JAYA Clustering Algorithm for IoV

NoL:

Number of lanes

LW:

Lane width

LB:

Lower bound

MOPSO:

Multiobjective particle swarm optimizer

MFCA-IOV:

Moth Flame Clustering Algorithm for Internet of Vehicles

MA-DTR:

Mobility Aware Dynamic Transmission Range algorithm

MaxItr:

The maximum number of iterations

MNoC:

The maximum number of clusters

MCDRIVE:

Modified clustering based on direction in vehicular environment

NoC:

Number of clusters

NoV:

Number of distributed vehicles

NoCH:

The total number of cluster heads in the network

PA-JAYA:

Hybridization of JAYA algorithm with pigeon optimizer

PSO:

Particle swarm optimizer

PopSize:

The size of the population

RSU:

Roadside unit

S-JAYA:

Modified solution method of JAYA

SP:

Vehicles allowable speed range

TR:

Transmission range

UB:

Upper bound

VANETs:

Vehicular ad-hoc NETworks

VWCA:

VANETs weighted clustering algorithm

References

  • Aadil F, Bajwa KB, Khan S, Chaudary NM, Akram A (2016) CACONET: ant colony optimization (ACO) based clustering algorithm for VANET. PLoS One 11(5):e0154080

    Article  PubMed  PubMed Central  Google Scholar 

  • Aadil F, Ahsan W, Rehman ZU, Shah PA, Rho S, Mehmood I (2018) Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J Supercomput 74(9):4542–4567

    Article  Google Scholar 

  • Ahsan W, Khan MF, Aadil F, Maqsood M, Ashraf S, Nam Y, Rho S (2020) Optimized node clustering in VANETs by using meta-heuristic algorithms. Electronics 9(3):394

    Article  Google Scholar 

  • Akandwanaho S, Govender I (2019) Dynamic street parking space using 1469 memetic algorithm for optimization. In: Bobek V (ed) Smart urban development. IntechOpen, p 171. https://doi.org/10.5772/intechopen.86010

  • Awadallah MA, Al-Betar MA, Hammouri AI, Alomari OA (2020) Binary JAYA algorithm with adaptive mutation for feature selection. Arab J Sci Eng 45(12):10875–10890

    Article  Google Scholar 

  • Balzano W, Stranieri S (2019) ACOp: an algorithm based on ant colony optimization for parking slot detection. In: Workshops of the international conference on advanced information networking and applications. Springer, Berlin, pp 833–840

  • Bitam S, Mellouk A, Zeadally S (2013) HyBR: a hybrid bio-inspired bee swarm routing protocol for safety applications in vehicular ad hoc networks (VANETs). J Syst Archit 59(10):953–967

    Article  Google Scholar 

  • Braik M, Hammouri A, Atwan J, Al-Betar MA, Awadallah MA (2022) White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl Based Syst 243:108457

    Article  Google Scholar 

  • D’Angelo G, Palmieri F (2023) A co-evolutionary genetic algorithm for robust and balanced controller placement in software-defined networks. J Netw Comput Appl 212:103583

    Article  Google Scholar 

  • D’Angelo G, Della-Morte D, Pastore D, Donadel G, De Stefano A, Palmieri F (2023) Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach. Future Gener Comput Syst 140:138–150

    Article  Google Scholar 

  • Dasgupta S (2008) The hardness of k-means clustering. Department of Computer Science and Engineering, University of California, Los Angeles

  • Dehghani M, Montazeri Z, Trojovská E, Trojovskỳ P (2023) Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl Based Syst 259:110011

    Article  Google Scholar 

  • Ebadinezhad S, Dereboylu Z, Ever E (2019) Clustering-based modified ant colony optimizer for internet of vehicles (CACOIOV). Sustainability 11(9):2624

    Article  Google Scholar 

  • Fahad M, Aadil F, Ejaz S, Ali A (2017) Implementation of evolutionary algorithms in vehicular ad-hoc network for cluster optimization. In: 2017 Intelligent systems conference (IntelliSys). IEEE, London, UK pp 137–141

  • Fahad M, Aadil F, Khan S, Shah PA, Muhammad K, Lloret J, Wang H, Lee JW, Mehmood I et al (2018) Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Comput Electr Eng 70:853–870

    Article  Google Scholar 

  • Forrest A, Konca M (2007) Autonomous cars and society, vol 15. Worcester Polytechnic Institute, Worcester, p 23

    Google Scholar 

  • Goswami V, Verma SK, Singh V (2017) A novel hybrid GA-ACO based clustering algorithm for VANET. 2017 3rd International conference on advances in computing, communication and automation (ICACCA)(Fall). IEEE, Dehradun, India, pp 1–6

  • Gunduz M, Aslan M (2021) DJAYA: a discrete Jaya algorithm for solving traveling salesman problem. Appl Soft Comput 105:107275

    Article  Google Scholar 

  • Halsall F (1996) Data communications, computer networks and open systems: solutions manual, chapter 2. Electronic systems engineering series. Addison-Wesley Longman, Incorporated, North York, ON, Canada, pp 29–31

    Google Scholar 

  • Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam

    Google Scholar 

  • Han S, Ban D, Park W, Gerla M (2017) Localization of sybil nodes with electro-acoustic positioning in VANETs. GLOBECOM 2017–2017 IEEE global communications conference. IEEE, Singapore, pp 1–6

    Google Scholar 

  • Hashim FA, Hussien AG (2022) Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl Based Syst 242:108320

    Article  Google Scholar 

  • He H, Tan Y (2012) A two-stage genetic algorithm for automatic clustering. Neurocomputing 81:49–59

    Article  Google Scholar 

  • Husnain G, Anwar S (2021) An intelligent cluster optimization algorithm based on whale optimization algorithm for VANETs (WOACNET). PLoS One 16(4):e0250271

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Husnain G, Anwar S, Shahzad F (2023a) An enhanced AI-enabled routing optimization algorithm for internet of vehicles (IoV). Wirel Personal Commun 130(4):2623–2643

    Article  Google Scholar 

  • Husnain G, Anwar S, Sikander G, Ali A, Lim S (2023b) A bio-inspired cluster optimization schema for efficient routing in vehicular ad hoc networks (VANETs). Energies 16(3):1456

    Article  Google Scholar 

  • Jena UK, Das PK, Mishra K, Kabat MR (2021) Improved binary JAYA algorithm-based scheduling dynamic cloud requests for cloud-based computing. In: 2021 12th International conference on computing communication and networking technologies (ICCCNT). IEEE, Kharagpur, India, pp 1–6

  • Kaur G, Rani MS, Aseri TC (2015) Improved AODV routing protocol for mitigating effects of grayhole attack in VANET using genetic algorithm. Int J Comput Sci Eng Technol 5(7):234–239

  • Khan MF, Aadil F, Maqsood M, Bukhari SHR, Hussain M, Nam Y (2018) Moth flame clustering algorithm for internet of vehicle (MFCA-IoV). IEEE Access 7:11613–11629

    Article  Google Scholar 

  • Lin Y, Wang P, Ma M (2017) Intelligent transportation system (ITS): concept, challenge and opportunity. 2017 IEEE 3rd international conference on big data security on cloud (BigDataSecurity), IEEE international conference on high performance and smart computing (HPSC), and IEEE international conference on intelligent data and security (IDS). IEEE, Beijing, China, pp 167–172

    Chapter  Google Scholar 

  • Liu T, Wang Y, Wenjuan E, Tian D, Yu G, Wang J (2012) Vehicle collision warning system and algorithm at intersection under internet-connected vehicles environment. CICTP 2012: multimodal transportation systems-convenient, safe, cost-effective, efficient. Beijing, China, pp 1177–1185

    Chapter  Google Scholar 

  • Luo X, He Z, Zhao Z, Wang L, Wang W, Ning H, Wang J-H, Zhao W, Zhang J (2018a) Resource allocation in the cognitive radio network-aided internet of things for the cyber-physical-social system: an efficient jaya algorithm. Sensors 18(11):3649

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Luo X, He Z, Wang L, Wang W, Ning H, Wang J-H, Zhao W (2018b) An efficient JAYA algorithm for resource allocation in the cognitive-radio-networks-aided internet of things. 2018 IEEE international conference on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData). IEEE, Halifax, NS, Canada, pp 118–125

  • MacQueen J (1967) Classification and analysis of multivariate observations. 5th Berkeley symposium on mathematical statistics and probability. CA, USA, Berkeley, pp 281–297

    Google Scholar 

  • Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41(6):853–862

    Article  MathSciNet  Google Scholar 

  • Metawa N, Nguyen PT, Nguyen QLHTT, Elhoseny M, Shankar K (2021) Internet of things enabled financial crisis prediction in enterprises using optimal feature subset selection-based classification model. Big Data 9(5):331–342

    Article  PubMed  Google Scholar 

  • Rajwar K, Deep K, Das S (2023) An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev 56:13187–13257

    Article  Google Scholar 

  • Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  • Salim A, Khedr AM, Osamy W (2023a) IoVSSA: efficient mobility-aware clustering algorithm in internet of vehicles using sparrow search algorithm. IEEE Sens J 23(4):4239–4255

  • Salim A, Khedr AM, Alwasel B, Osamy W, Aziz A (2023b) Somaca: a new swarm optimization-based and mobility-aware clustering approach for the internet of vehicles. IEEE Access

  • Serway RA, Jewett JW (2018) Physics for scientists and engineers with modern physics Hardcover- Illustrated, 10th edn. Cengage Learning

    Google Scholar 

  • Sharma S, Kaushik B (2020) A comprehensive review of nature-inspired algorithms for internet of vehicles. 2020 International conference on emerging smart computing and informatics (ESCI). IEEE, Pune, India, pp 336–340

    Chapter  Google Scholar 

  • Sharma S, Kaushik B (2021) A survey on nature-inspired algorithms and its applications in the internet of vehicles. Int J Commun Syst 34(12):e4895

    Article  Google Scholar 

  • Smys S (2020) A survey on internet of things (IoT) based smart systems. J ISMAC 2(04):181–189

    Article  Google Scholar 

  • Trojovskỳ P, Dehghani M (2022) Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors 22(3):855

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: The 2003 Congress on evolutionary computation, 2003. CEC’03, vol 1. IEEE, Canberra, ACT, Australia, pp 215–220

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67–82

    Article  Google Scholar 

  • Yuan Y, Ren J, Wang S, Wang Z, Mu X, Zhao W (2022) Alpine skiing optimization: a new bio-inspired optimization algorithm. Adv Eng Softw 170:103158

    Article  Google Scholar 

  • Zitar RA, Al-Betar MA, Awadallah MA, Doush IA, Assaleh K (2021) An intensive and comprehensive overview of JAYA algorithm, its versions and applications. Arch Comput Methods Eng 29:763–779

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  • Zitar RA, Al-Betar MA, Awadallah MA, Doush IA, Assaleh K (2022) An intensive and comprehensive overview of JAYA algorithm, its versions and applications. Arch Comput Methods Eng 29(2):763–792

    Article  MathSciNet  PubMed  Google Scholar 

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Dalbah, L.M., Al-Betar, M.A. & Awadallah, M.A. Jaya clustering-based algorithm for multiobjective IoV network routing optimization. Soft Comput 28, 5639–5665 (2024). https://doi.org/10.1007/s00500-023-09350-y

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