Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability
Abstract
:1. Introduction
2. Evolutionary Algorithms for Optimization Problems
3. Evolutionary Algorithms in MANETs
3.1. Online and Offline Optimization
3.2. Centralized and Decentralized Systems
4. Evolutionary Algorithms in VANETs
4.1. Existing Evolutionary Algorithm Approaches in VANET
4.2. Self-Organization and Adjustability
4.3. Dynamic Change in Network Size (Scalability and Robustness)
4.4. Topology Management
4.5. Broadcasting Algorithms
4.6. Routing Protocols
4.7. Mobility Models
5. Cluster Formation and Parameter Optimization
6. Routing Parameter Optimization
7. Broadcast Scheduling in VANETs
- Reducing time periods.
- Enhancing the network stream using variables by increasing the number of broadcasts without human interaction.
8. Challenges in Using Evolutionary Approach for Ad Hoc Networks
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Protocol | Cluster Size | Performance Metric | Simulation Tool |
---|---|---|---|
MOBIC [1] | NA | Radio propagation | ns-2 |
Kayis [2] | Based on speed | Speed | No Simulation |
Su [3] | Based on traffic direction | Direction | MATLAB |
Rawshedh [4] | Based on speed | Speed, location, and direction | C testbed |
Maslekar [5] | NA | Location and direction | NCTUns |
RMAC [6] | NA | Speed, location, and direction | ns-2 |
APROVE [7] | Based on traffic direction | Distance and speed | ns-2 |
DBC [8] | Depends on density | Node density, link quality, node reputation, and traffic conditions | JiST/SWANS++, VanetMobiSim |
Almalag [9] | Depends on radio propagation | lane with the most traffic | ns-3 |
Wang [10] | NA | Vehicle density, link quality, and sustainability | MOVE, ns-2 |
ALM [11] | NA | Relative mobility | SUMO, SIDE/SMURPH |
VWCA [12,53] | Flexible | Direction | MATLAB |
AMACAD [13] | Flexible | Speed, location, and direction | JAVA testbed |
HCA [14] | Based on radio propagation | Radio propagation | OMNET++, SUMO |
ASPIRE [15] | NA | Network characteristics | ns-2 |
Zhang [16] | NA | Relative mobility | ns-2 |
Maslekar [17] | Based on radio propagation | Direction | NCTUns |
RWCP-MOFA [52] | Adaptable | PDR, no. of clusters, control packet overhead | NETSIM, MATLAB |
Algorithm Category | Protocol | Objective Type | Objectives | Performance Metrics |
---|---|---|---|---|
Genetic Algorithm | AMR [19] | Mono-objective | E2ED | Scalability, Complexity, Delay |
IGRP [20] | Mono-objective | Enhanced connectivity | Scalability, Complexity, Delay | |
Hybrid DTN [21] | Mono-objective | Delay | Complexity | |
xChange Mobile [42] | Multi-objective | Packet drop and bandwidth | Complexity | |
SLAB [22] | Multi-objective | Enhanced connectivity and bandwidth | Scalability, Complexity, Delay | |
Parallel Genetic Algorithm | GAP [23] | Multi-objective | Energy and configuration time | Scalability, Complexity, Delay |
Ant Colony Optimization | MAR-DYMO [24] | Mono-objective | Enhanced lifetime | Delay, PDR, Routing Overhead |
TACR [25] | Mono-objective | Malicious message detection | PDR, Routing Overhead | |
[26] | Multi-objective | Cost, bandwidth, and connectivity | Delay, PDR, Routing Overhead | |
MAV-AODV [27] | Multi-objective | Lifetime, hop count | Delay, PDR, Routing Overhead | |
Particle Swarm Optimization | pPSO [28] | Multi-objective | PDR, delay overhead | Delay, PDR, Routing Overhead |
[29] | Multi-objective | Adjusting system parameters | Delay, PDR, Routing Overhead | |
Bee Colony Optimization | QoSBee [30] | Mono-objective | Delay | Delay, PDR, Routing Overhead |
HyBR [31,70,71] | Mono-objective | Shortest path | Delay, PDR, Routing Overhead | |
BLA [32] | Multi-objective | Cost, delay, and bandwidth | Delay, Bandwidth | |
Firefly Optimization | FA-OLSR [72] | Multi-objective | Parameter tuning | PDR, Mean Routing Load, End-to-End Delay |
Harmony Search Algorithm | EHSO [73] | Multi-objective | OLSR parameter tuning | PDR, End-to-End Delay, Overhead |
Protocol | Year | Mobility Model | Density | Broadcasting Ability | Traffic Model | Multi-Media Support | Coverage | Simulator |
---|---|---|---|---|---|---|---|---|
CFR MAC [35] | 2014 | Highway | High | Yes | Bi-directional | No | Low | N/A |
HER-MAC [36] | 2014 | Highway | Low | Yes | Bi-directional | Yes | N/A | MATLAB |
VeMAC [37] | 2011 | Highway/ Urban | High | Yes | Bi-directional | Yes | Short | MATLAB and NS2 |
ATSA [38] | 2013 | Highway | Medium | No | Bi-directional | No | N/A | MATLAB |
CS-TDMA [39] | 2014 | Highway | Medium | Yes | Bi-directional | Yes | N/A | MATLAB |
VeSOMAC [40] | 2007 | Highway | Low | No | Unidirectional | Yes | Medium | NS2 |
STDMA [41] | 2009 | Highway | High | Yes | Bi-directional | No | Long | MATLAB |
SOFTMAC [42] | 2009 | Highway | Low | N/A | Unidirectional | Yes | N/A | N/A |
DMMAC [43] | 2010 | Highway | Medium | Yes | Unidirectional | N/A | Short | NS2 |
ADFA [87] | 2020 | Highway | Medium | Yes | Unidirectional | No | Yes | NETSIM |
Type of Network | Reference | EA Used | Key Issues | Optimization Problem |
---|---|---|---|---|
MANET | Gutiérrez-Reina et al. [49] | GA | To improve the range of notification. | Topology |
Reina et al. [50] | GA | To improve communication. | Topology | |
Dengiz et al. [51] | PSO | To improve communication. | Topology | |
Kusyk et al. [99] | GA with Game theory | To improve communication. | Topology | |
Singh and Bhukya [100] | GA with local search | To minimize the energy of the network. | Broadcast | |
Reina et al. [101] | GA | To maximize accessibility and minimize delaying and re-transmittance packages. | Broadcast | |
Iturriaga et al. [102] | Parallel GA | To enhance exposure and reduce power, retransmission and limit the number of channels. | Broadcast | |
Yetgin et al. [103] | GA | To reduce energy and costs. | Routing | |
VANET | Schleich et al. [104] | GA | To maximize the clustering coefficient and reduce the distinction between the median route longitude of the subsequent network and the median route longitude of the selected chart. | Topology |
Cavalcante et al. [105] | GA | To improve communication. | Topology | |
Abdou et al. [106] | GA | To reduce or eliminate the percentage of collisions, the spread time, and the number of transmissions. | Broadcast | |
García-Nieto et al. [107] | PSO, DE, GA, ES, and SA | To reduce time and the amount of missed packets and improve messages transmitted. | Routing | |
Toutouh et al. [28] | PSO, DE, GA, ES, and SA | To reduce load and wait for routing and enhance message transmission. | Routing | |
García-Nieto and Alba [108] | PSO, DE, GA, ES, and SA | To reduce routing load and interruption and maximize package delivery. | Routing |
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Joshua, C.J.; Jayachandran, P.; Md, A.Q.; Sivaraman, A.K.; Tee, K.F. Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability. Sustainability 2023, 15, 4767. https://doi.org/10.3390/su15064767
Joshua CJ, Jayachandran P, Md AQ, Sivaraman AK, Tee KF. Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability. Sustainability. 2023; 15(6):4767. https://doi.org/10.3390/su15064767
Chicago/Turabian StyleJoshua, Christy Jackson, Prassanna Jayachandran, Abdul Quadir Md, Arun Kumar Sivaraman, and Kong Fah Tee. 2023. "Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability" Sustainability 15, no. 6: 4767. https://doi.org/10.3390/su15064767