\chapter*{ABSTRACT}
%% ===================================================================================================================================================
%% ===================================================================================================================================================
%%   ABSTRACT
%% ===================================================================================================================================================
%% ===================================================================================================================================================

Over the last few years, Wireless Sensors Networks (WSNs) witness tremendous growth in every sphere of life due to low-cost and multi-functional capabilities which make them functional even in diverse and complex environments such as health-care monitoring, surveillance activities, or smart farming to name a few. Further research in electronics and wireless communication spark off rapid development of highly advance Sensor Nodes (SNs) that can operate in dynamic environment for long duration. Every SN is equipped with a transceiver and a processing unit, backed by a power supply unit with limited battery life, which  may lead to premature exhaust of the network, due to excessive usage. As manual recharging of batteries or human intervention  is not possible in various complex deployments of WSNs, efficient use of the residual energy becomes a tough challenge to support network for a longer duration.

Researchers around the world are heavily involved in designing better and efficient SNs  however, network life of a WSN can also be extended by devising energy-efficient routing techniques. Cluster based hierarchical approach prove to be an efficient way to save network energy, which helps to increase network life by effectively utilize the node energy thus support dynamic WSNs environment for long duration. Further, Computational Intelligence (CI) based metaheuristic like Particle Swarm Optimization (PSO), Genetic Algorithm (GA)  and more recently, Artificial Bee Colony (ABC), have been used extensively as population based optimization methods for optimal Cluster Head (CH) selection and optimal route discovery for energy-efficient clustering protocols in WSNs.

In this research work, we propose an energy-efficient hierarchical routing protocol \emph{BeeSwarm}, based on a nature inspired improved Artificial Bee Colony \((iABC)\) metaheuristic. Our proposed protocol consist of three phases viz. 1. BeeCluster , 2. BeeSearch and 3. BeeCarrier for optimal CH selection, optimal route discovery and data transmission respectively. The proposed protocol is evaluated with other existing well known WSNs routing protocols on \emph{Nature Inspired Tool for Sensor Simulation} (NITSS), a simulation tool specially developed to evaluate routing protocols for WSNs environments with different scenarios. Simulation results prove that \emph{BeeSwarm} outperforms other existing routing protocols over various performance metric and establish it as a better energy-efficient routing protocol among its peers  even for scalable WSNs.
