Screenshot from 2017-10-11 18:05:01
Figure1: Operation of MANET

         Mobile Ad-hoc NETwork (MANET) is a self-organized and decentralized system that enables communication via wireless links between nodes either in single or multiple hops. Figure1 shows the operation of MANET with dynamic infrastructure. Packets are relayed from source node to destination node via neighboring nodes within its transmission range. Routing solution for MANET facing constraints such as energy, topology, density, bandwidth, and mobility, this can be managed effectively by utilizing the bio-inspired algorithms. The food searching behavior of insects can apply in the packet routing. This routing technique can successfully transmit the packet to the destination depending on the optimal path selection using the bio-inspired algorithm.

         Ants food searching activity solves the routing problem in MANET. Ant colony optimization helps to select the shortest path depends on the bandwidth, hop-count and congestion level for routing the packets from source to destination. Limited bandwidth, energy consumption, and dynamic topology make the computational problem in MANET. In this method, ants select the shortest way depends on the amount of pheromone content in the traveling path. Pheromone is like a chemical content which left from ants. Ants search for the food in the shortest way, where the pheromone deposit amount is high.

Artificial bee colony optimization imitates the intelligent foraging of bee swarms in nature. The scout bee searches the source area and finds out the best food source. Bee intimates the food source information via dancing, which is called waggle dance. Onlooker bee identifies the food source quality (high nectar) and distance using waggle dance. It collects the food from a source in the shortest way. In this optimization method, the source node selects the most efficient route to relay the packet in the dynamic network topology. Food sources represent the number of solutions and nectar value represents the quality of the solutions.

         PSO is adapted to find the optimal solution over the velocity and position of each particle. Particles move towards the optimal solution depends on the best fitness of position by adding a velocity. Every particle accelerates to move the previous best (pbest) position and compare its value with pbest to get its global best value (gbest). The objective of particle swarm optimization is to obtain the best routing solution with minimum delay and cost.

         In a MANET, dynamic nodes are willing to communicate with each node through optimal route. Firefly algorithm deals the routing issue depending on the natural behavior of fireflies. The distance between the fireflies is indirectly proportional to the brightness. The brightness of the light is related to the attractiveness which is taken as the routing parameters such as minimum congestion, minimum delay, and shortest path. One important advantage of firefly algorithm automatically subdivides the whole population and solve the optimization problems.

         Bat algorithm depends on the echolocation of bats in dark places. Many types of bats are lived in nature, and it has more magical senses like smell sense, hear sense, etc., Every bat makes the sound and senses the echo to find the food location. Echolocation behavior helps the bats to identify the distance of food or prey and obstacles. Bats able to adjust the loudness depending on the location of prey. It enables the nodes to find the static route from auxiliary path according to the link availability estimation in MANET.

         The genetic algorithm depends on the natural evaluation principles to rectify the optimization problem. It consists of individuals are called chromosomes, which represent the possible solutions. The genetic operators such as crossover and mutation create the next offspring from the current population. The offspring population is replaced with parent population. This process is continuously repeated to derive the new generations. The genetic algorithm can obtain the optimal route from the accessible multi-paths with considerable delay and bandwidth.

         Cuckoo search algorithm helps to choose the best option according to the probabilistic technique. Cuckoo drops their eggs in the nest of host bird, and eggs of both birds are more similar in color and shape. The probability of selecting the cuckoo’s egg is very poor. The host bird identifies the cuckoo’s egg to throw away from their nest. Cuckoo search optimization enables the mobile nodes to select the high capability routing paths and also it should be easy to execute.

Leave Comment

Your email address will not be published. Required fields are marked *

clear formSubmit