Natural Computing in Mobile Network Optimization

Nature is the immense gifted source for solving complex problems. It always helps to find the optimal solution to solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide research area of networks which has set of independent nodes. The characteristics involved in MANET’s are Dynamic, does not depend on any fixed infrastructure or centralized networks, High mobility. The Bio-Inspired algorithms are mimics the nature for solving optimization problems opening a new era in MANET. The typical Swarm Intelligence (SI) algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf Search Algorithm (WSA) and so on. This work mainly concentrated on nature of MANET and behavior of nodes. Also it analyses various performance metrics such as throughput, QoS and End-to-End delay etc.

International Journal of Distributed Sensor Networks, 2013

is survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. e marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of arti�cial systems' problems has been shaped into an important �eld with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. e process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.

Mobile Ad Hoc Networks (MANETs) are a developing type of wireless networks, in which nodes which are mobile in nature, associate on an automatic or ad hoc basis. Mobile Ad Hoc Networks are self-healing and selfforming networks that enable peer-level communications between mobile nodes without fixed infrastructure or reliance on centralized resources. MANETs deliver significant benefits in virtually any scenario including a cadre of highly mobile platforms or users, an environment in which fixed network infrastructure is impaired, impractical or impossible, and a strong requirement to share IP-based information. One of the most important issues in Mobile Ad-hoc Networks is Energy Efficiency, since the energy supplies are stored in batteries. It is important to maximize the minimum energy required by the node for data transmission to increase the network endurance. In this paper, we use an algorithm that is based on Nature inspired Ant Colony Optimization structure, for improving the energy efficiency in mobile adhoc networks and thus increasing the lifetime of the overall communication system.

Ad-hoc networks are wireless networks where nodes communicate with each other using multi-hop links. Each node itself acts as a router for the forwarding and receiving packets to/from other nodes. There are some critical issues in wireless network as battery life, routing, mobility, interference, dynamic topology etc. Routing in ad-hoc networks has been a challenging task due to dynamic network topology because of high degree of node mobility. Swarm intelligence based routing protocol helps to solve the routing problem using the collective decentralized, self behavior to search optimal path. This paper presents a review on various ants based routing algorithms.

Computer Science Review, 2021

In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGA-BACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area.

International Journal of Systems, Control and Communications, 2018

A mobile ad hoc network (MANET) is an autonomous system of mobile hosts (nodes) connected by a wireless link. However, the problem of designing routing protocols poses challenges to researchers due to the unpredictable and dynamic nature of ad hoc networks. Hence, bio-inspired algorithms are widely used to design adaptive routing strategies for MANETs. This paper proposes a routing protocol based on the hybridisation of ant colony optimisation (ACO) and 2-opt heuristic with the optimisation of ACO parameters. Given the vast scope of the parameters, a genetic algorithm is used to minimise the complexity of the problem. The implementation of the method is realised by MATLAB. To valid the results in terms of the quality of service parameters (i.e., normalised overhead load, end-to-end delay and throughput), a comparison was conducted using the ad hoc on-demand distance vector routing protocol.

There are multiple algorithms and protocols which are present in MANeT but no one is perfect for each situation because of the presence of high topology changes and dynamicity of number of nodes. That's the reason of opting biological algorithm like Ant based or Bee based algorithms which are swarm intelligence based nature inspired algorithms which finds out the best route as per the real time status of network. In communications network research, there is currently an increasing interest for the paradigm of autonomic computing. The idea is that networks are becoming more and more complex and that it is desirable that they can self-organize and self-configure, adapting to new situations in terms of traffic, services, network connectivity, etc. To support this new paradigm, future network algorithms should be robust, work in a distributed way, be able to observe changes in the network, and adapt to them. Nature's self-organizing systems like insect societies show precisely these desirable properties. Making use of a number of relatively simple biological agents (e.g., the ants) a variety of different organized behaviours are generated at the system-level from the local interactions among the agents and with the environment. The robustness and effectiveness of such collective behaviours with respect to variations of environment conditions are key-aspects of their biological success. This kind of systems is often referred to with the term Swarm Intelligence. Swarm systems have recently become a source of inspiration for the design of distributed and adaptive algorithms, and in particular of routing algorithms. Study and Performance Evaluation of Anthocnet and Beehocnet Nature Inspired Multihop Routing Protocols for Effective Routing in MANeT

Springer Lecture Notes …, 2004

In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive route setup with proactive route probing, maintenance and improvement. The algorithm is based on the Nature-inspired Ant Colony Optimization framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in this research area. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. AntHocNet is also more scalable than AODV.

2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, 2009

This paper presents the state of the art in biologically inspired ant based routing in wireless Mobile Ad hoc NETworks (MANET). The motivation for using ant-like mobile agents for providing routing information to mobile hosts in MANETs stems from the fact that antlike mobile agents do not require high bandwidth overhead compared to MANET proactive routing protocols for disseminating the topology information in the network. The increased connectivity information provided by the ants can be used to for making better routing decisions which can improve the network performance without causing high overhead.

International Journal of Hybrid Intelligence, 2019

VANET is a hybrid ad-hoc network between vehicles and road side units. Due to the high mobility of nodes, to find a stable data communication route in VANET is an open challenge. This paper introduces a new hybrid routing algorithm to find a stable route by using zone-based routing (ZBR), fuzzy logic, and NIBC algorithm. In proposed algorithm ZBR is used to divide the network into small and stable zones, fuzzy logic is used to find the quality of links between nodes, and NIBC to find the stable route in short time. Six techniques for the VANET has been implemented and compared in this paper. NS2.34 network simulator is used for simulations. Simulations were carried out for variable transmission rate and variable speed. Five performance parameters have been taken to analyse the results. Simulation results have shown that NIBC algorithms improve the performance of VANET.