WiBiA: Wireless Sensor Networks Based on Biomimicry Algorithms

Abstract

The goal of biomimicry is to resolve problems by studying and mimicking the characteristics of organisms or design elements in nature. Although wireless sensor networks are used in various fields, they have limited network lifespan. Research has thus focused on observing and modeling the behavioral principles of various organisms to use in biomimicry algorithms for efficient routing techniques in large-scale networks. In this study, we examine the pheromones used in ant communication, and accordingly design techniques for energy-efficient traffic distribution implemented on a network. We designed a biomimicry technology called the wireless sensor networks based on biomimicry algorithms or WiBiA. By analyzing and applying similarities between communication and biological systems, the performance of our system improved in terms of network lifespan and optimized path selection. In simulations, the proposed routing algorithm yielded short information-collection time and lowenergy consumption, and thus maximized the energy efficiency of the network.

Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks

“…As shown in Figure 1 [ 33 ], when an ant finds food (F), it transports it to its nest (N) leaving behind a pheromone trail (b) on its path (a). When a swarm of ants transport food through multiple paths, pheromones accumulate on the path with the shortest distance between the nest and the food.…”

“…Because CATI, which operates as presented in Algorithm 1, can determine the state of the neighboring nodes based on their type, the overall network performance can be improved by predicting or detecting link disconnections [ 33 ]. Algorithm 1: Collision Avoidance through Interrupts [When node A receives a packet] if RREQ message in a packet if node A is in the interrupt state for battery or mobility ignore the packet else process the packet using the existing routing algorithm else if receive interrupt message for battery or mobility if the interrupt state is destined to node A initiate the route-discovery mechanism of the existing routing algorithm else forward the packet with the alternative route [At the end of a time interval (for lifetime & mobility)] if # of forwarding packets for the time interval ≥ threshold change the state to battery check or mobility check send a <Battery Check> or <Mobility Check> message to the source of last received packet if # of forwarding packets for the time interval < threshold change the state to normal …”

“…The algorithm has been applied to various optimization problems related to communication networks, path exploration by a mobile object, scheduling, and job allocation [31,32]. As shown in Figure 1 [33], when an ant finds food (F), it transports it to its nest (N) leaving behind a pheromone trail (b) on its path (a). When a swarm of ants transport food through multiple This study focuses on the indirect information transmission method using pheromones in the ACO algorithm [26].…”

“…Sensors 2020, 20, x FOR PEER REVIEW 6 of 13 transmission, path recovery is first performed using an alternate node to improve the frequency of path rescan. Because CATI, which operates as presented in Algorithm 1, can determine the state of the neighboring nodes based on their type, the overall network performance can be improved by predicting or detecting link disconnections [33].…”

“…Furthermore, by setting the residual energy threshold of the node and notifying the source node before the path is disconnected, it is possible to reduce the data loss and transmission delay that occur owing to path resetting. Because CATI, which operates as presented in Algorithm 1, can determine the state of the neighboring nodes based on their type, the overall network performance can be improved by predicting or detecting link disconnections [33].…”