In Wireless Sensor Networks (WSN), sensors observe various parameters in physical and environmental situations over a wide geographic area such as humidity, temperature, and pressure. The sensed information is transmitted to the base station or sink via a network. Sensors are operating on battery power, and it is difficult to replace. Hence energy usage of sensor nodes is the primary challenge in sensor networks. Cluster-based partition is a preferable data routing method. Each cluster consists two categories such as cluster head and cluster members. In cluster partitions, sensors are not necessary to transmit its data to the base station or sink individually. Cluster head collects the data from their cluster members and transmits the aggregated data to the sink. Bio-inspired algorithms are used to make the efficient routing from the sensor node to sink. Figure1 represents the basic structure of wireless sensor network.


Figure1: Wireless Sensor Network (WSN)

         Ants select the shortest path depending on the pheromone chemical deposit in the way from nest to food. Each ant follows the way of previous ants with higher pheromone deposit. This swarm activity of ants improves the routing performance in term of delay. Moreover, energy consumption of each sensor can also be reduced according to the foraging activity of real ants. Swarm intelligence behavior significantly improves network lifetime and packet delivery ratio.


         Cluster-based selection for data aggregation in sensor networks maintains the node energy and gets a prolonged lifetime of the network. Each cluster head selection depends on the conduct of bee swarms. The scout bee searches the food source area and finds the food source with higher nectar (juice) amount. It intimates food source information to the queen by a bee dance in the hive. The dance is called waggle dance which imitates the distance and fitness of food source. An employed bee collects the nectar after find out the best food source. The fitness of nectar represents the quality of routing solutions. Bee’s food searching behavior can obtain the best routing solution..

         Particle swarm optimization is an effective, simple, and efficient algorithm for optimization. PSO compares the fitness of particles to select the cluster head depending on the higher fitness value. Reduced energy consumption is obtained through the fitness evaluation.

         Cuckoo search algorithm depends on the selection of the best routing solution to achieve the network lifetime and reduced energy consumption. It is a probabilistic method for selecting the cuckoo’s egg from host bird eggs. Cuckoo drops their eggs in the host birds nest, and both are more similar to see. If the host bird identifies the difference of egg, it throws away or rebuilds their nest in another place. Cuckoos egg represents the best routing solution. CSA leads the higher energy conservation, and it enhances the network efficiency.

         WSN contains more sensors in large geographic region that can communicate with other sensors and transmit the sensed data to the base station. Minimum energy routing is the most important one during communication. The sensor routing protocols can improve the network lifetime using a firefly routing algorithm. Firefly produces flashes of light in short duration to attract prey or other fireflies. More attractiveness depends on the intensity of light flashes. Fireflies move toward another firefly with high-intensity light. This attractively based routing is used in sensor communication to attain the limited energy consumption.

         The main aim of bat algorithm is achieving the packet routing through the optimal paths which depend on the cluster formation and the cluster head selection. Bat algorithm depends on the echolocation based food searching of real bats. Bats make the sound louder and it can adjust its frequency depends on the received echo from prey or obstacles.

         Genetic algorithm motivates the routing methodology of biological evaluation such as crossover and mutation of various fittest chromosomes (solutions). In a genetic algorithm, individuals are selected for crossover (breeding) depending on their fitness amount. By combining these solutions, significantly produce a new individual solution. The new individual represents the new generation. This process is repeated for achieving the more fitness solutions. In sensor networks, it saves more energy and increases the lifetime using reproduction process.

         The wireless sensor networks have the issues of node deployment, localization, energy-aware clustering, and data aggregation. The Bioinspired algorithms motivate to formulate these routing issues as optimization problems. The conventional optimization techniques consume high energy and lead the energy consumption as a challenging problem when the network size increases. As the bio-inspired algorithms require moderate memory and computational resource, resulting in desirable results, especially in resource-constrained networks.

         The optimization of WSN deployment is to determine and place the resource constrained nodes with desirable network coverage, connectivity, and energy efficiency. The sparse sensor network unnoticed the events happened in the network, whereas the areas having dense sensor populations suffer from network congestion and high routing delay. The optimal placement of sensor nodes assures a reliable routing service, long network life, and delay tolerant routing. The Bio-inspired algorithms are suitable for the deployment of sensor nodes around a base station. Notably, the PSO algorithms reduce the searching space and improve the network lifetime significantly.

         The communication is the next energy expensive activity in sensor networks. The energy consumption increases exponentially with transmission distance. Thus, it is customary to the data transmission through multiple hops in WSNs. The lifetime of sensor nodes largely depends on the efficiency of data routing from its source to the sink node. Routing refers the determination of routing paths for a packet from a source node to a sink. Mostly, the WSN implements the hierarchical or cluster-based routing for energy efficiency. Each cluster has a node that acts as a cluster leader. Nodes that belong to a cluster forward their data to the cluster leader, which is responsible for sending the data to sink node. However, a node which plays a role of cluster leader for a long duration exhausts its batteries prematurely. To avoid this, the Bio-inspired algorithms select the cluster leader based on the remaining energy.

         The efficient trade-off between the data routing hops and data quality is crucial. Data aggregation defines the process of combining the data originating from multiple cluster members and reducing the communication overhead. A principal application of a sensor network is to detect an event. In a centralized network topology, each cluster leader collects local observations from the members and fuses the data without reducing the data quality. The cluster leader nodes act as fusion centers, which is responsible for sending the aggregated data to the sink node. To ensure an extended network lifespan, the Bio-inspired algorithms have provided optimization in several aspects of data aggregation, such as error and noise reduction.

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