Mobile sinks were introduced in wireless sensor networks (WSNs) to mitigate the infamous hotspot problem. However, routing in mobile-sink-based WSNs requires frequent updation of sink location information to all the sensor nodes; which is an energy-expensive process for resource-constrained WSNs. Therefore, it is required to develop a green routing protocol that can minimize the energy overhead in sink location updation as well as reduce the data delivery delay. This article proposes a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings only. Simulation results show that DGRP outperforms existing routing protocols in terms of energy consumption and throughput. In addition to this, DGRP results in $\approx 26$ %, $\approx 39$ %, and $\approx 35$ % improvement in data delivery delay for a varying number of sensor nodes, sink speeds, and network sizes, respectively, when compared with the state of the art.

There are two major challenges in mobile sink based wireless sensor networks (WSNs) concerning to the query-driven scenarios, first, dissemination of queries to their respective region of interests (RoIs), and second, routing the data towards mobile sink. Due to sink mobility, routing of data packets to the sink becomes difficult because sink's query injection location and data collection location (current location of sink) may not be the same. Moreover, advertising mobile sink's location by flooding, introduces extensive burden on sensor nodes. In this paper, we propose a virtual ring infrastructure based query-driven ring routing protocol (QRRP) to reduce the overhead of updating mobile sink location information as well as routing the data towards current location of the sink. QRRP takes the advantage of proposed angle based routing to route the queries from mobile sink to their respective RoIs, and data from sensor nodes to the sink. Simulation results on the proposed approach show that energy consumption and data delivery delay are significantly reduced as compared to state-of-the-art mechanisms.

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. The accuracy and response time of estimation are critical issue in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

In this article, we propose a new algorithm to solve the problem of robotic path planning in static environment where the source and destination are given. A grid-based map has been used to represent the robotic world. The basic algorithm is built on an evolutionary approach, where the path evolves along with generations with each generation adding to the maximum possible complexity of the path. Along with complexity we optimise the total path length as well as the minimum distance from the obstacle in the robotic path. It may be seen that the value of evolutionary parameter number of individuals as well as the maximum complexity is less at start and more at the later stages of the algorithm. We use a Gaussian increase in these values whose parameter may be adjusted to control the time and output. Seven genetic operators have been implemented that include selection, crossover, soft mutation, hard mutation, insert, delete and elite. The phenotype representation consists of the coordinate where the robot is supposed to make a turn. This happens by the traversal of the path using these points by the evolutionary algorithm. Momentum determines the speed of the algorithm in this traversal.

The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speedup. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both the hierarchies make use of an evolutionary algorithm for planning. Both these hierarchies optimize as the robot travels in the map. The static environment path is increasingly optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction. Experimentation was done in a variety of scenarios with static and mobile obstacles. In all cases the robot could optimally reach the goal. Further, the robot was able to escape from the sudden occurrence of obstacles.