Our products are a result of scientific research of over 10 years. Below are some examples of our work, published in some reputable journals.
Bio-inspired Routing in Wireless Sensor Networks
Abstract: In order to increase network life time scalable, efficient and adaptive routing protocols are need of current time. Many energy efficient protocols have been proposed, the Clustering algorithm is also a basic technique used for energy efficiency. In this paper we propose an energy efficient routing protocol that is based on Artificial Bee Colony (ABC) algorithm of Bio Inspired. The presented protocol efficiently utilized characteristics of ABC algorithm such as foraging principle and waggle dance of honey bees. Waggle dance technique is used to find Routing Node (RN) that has maximum energy. Simulation results proves increase network life time and high throughput with minimum delay.
Interference and Bandwidth Aware Depth Based Routing Protocols in Underwater WSNs
Abstract: Many researcher has paid their to explore and monitor the under water environment. There are lot of application of Underwater WSNs like environment monitoring, exploration of under water surfaces, disaster preventions assisted navigation etc. Underwater sensors are totally different from the terrestrial sensors. Terrestrial sensor network uses the radio signal and underwater sensor network uses the acoustic signal. As the radio signal has not good strength that it can propagate in the water. The Radio signal can propagate over the large distance as compared to the acoustic signals. Therefore, acoustic signal are used. In this paper, we propose Energy Hole Repairing Depth based routing protocol (EHRDBR) and Interference-Bandwidth aware Depth based routing (IBDBR) protocol. In both protocols, nodes move toward the specific area where the other node dies and cover the energy hole. In EHRDBR, forwarder node is selected on the basis of the interference residual energy, and depth parameters. In IBDBR, interference, bandwidth, residual energy, and depth parameters are used to select the forward node. Our protocols have performed better in network lifetime, throughput and ene to end delay.
Towards Multiple Knapsack Problem Approach for Home Energy Management in Smart Grid
Abstract: The energy demand of residential end users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. In this paper, we describe a scheme to solve multiple knapsack problems (MKP) using heuristic algorithms. Keeping total energy consumption of each household appliance under certain threshold with maximum benefit is regarded as knapsack problem. Here, we design multiple knapsack problems for each hour of a day to schedule different numbers of appliance. To avoid peak hours, load is shifted in low and mid peak hours. Different algorithms are used to schedule household appliances. We use ant colony optimization (ACO) that is one of the meta-heuristic techniques to solve multiple knapsack problems which enables fast convergence rate for scheduling of appliances. Results show that propose scheme is an efficient method for home energy management to maximize user comfort and minimize electricity bills.
An Efficient Genetic Algorithm Based Demand Side Management Scheme for Smart Grid
Abstract: In this paper, we propose a novel strategy for a Demand Side Management (DSM) in a Smart Grid (SG). In this strategy, three types of loads are considered, i.e., residential load, commercial load and industrial load. The larger number of appliances of different power rating for each type of load is considered in this work. The focus of this work is to minimize the Peak to Average Ratio (PAR) to increase the efficiency of SG, by increasing the utilization of spinning reserves. On the other hand, our aim is to minimize the electricity consumption cost. Tackling the large number of appliances in an SG is a challenging task, because it increases the complexity of the problem. However, in literature the focus is on small number of appliance. In this work, the load scheduling problem is mathematically formulated and solved by using genetic algorithm. The simulation results show that the propose algorithm reduces the cost, while reducing the peak load demand of the SG.