Jasbir Singh Saini

Meta-heuristic range based node localization algorithm for Wireless Sensor Networks

Accurate location of target nodes is highly desirable in a Wireless Sensor Network (WSN) as it has a strong impact on overall performance of the WSN. This paper proposes the application of H-Best Particle Swarm Optimization (HPSO) and Biogeography Based Optimization (BBO) algorithms for distributed optimal localization of randomly deployed sensors. The proposed HPSO algorithm is modeled for fast and mature convergence, though previous PSO models had only fast convergence but less mature. Biogeography is a school work (collective learning) of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i.e., problem solutions. WSN localization problem is formulated as an NP-Hard optimization problem because of its size and complexity. In this work, an error model is described for estimation of optimal node location in a manner such that the location error is minimized using HPSO and BBO algorithms. Proposed HPSO and BBO algorithms are matured to optimize the sensors locations and perform better as compared to the existing optimization algorithms such as Genetic Algorithms (GAs), and Simulated Annealing Algorithm (SAA). Comparative study reveals that the HPSO yields improved performance in terms of faster, matured, and accurate localization as compared to global best (gbest) PSO. The performance results on experimental sensor network data demonstrate the effectiveness of the proposed algorithms by comparing the performance in terms of the number of nodes localized, localization accuracy and computation time.

Range-free 3D node localization in anisotropic wireless sensor networks

Graphical abstractDisplay Omitted HighlightsPropose two computationally effective range-free (RF) 3D node localization schemes using applications of biogeography based optimization (BBO) and hybrid particle swarm optimization (HPSO) for anisotropic wireless sensor networks.Nodes are randomly deployed with constraints over three layer boundaries. The anchor nodes are randomly distributed over top layer only and target nodes are distributed over the middle and bottom layers.Non-linearity between received signal strength (RSS) and distance is modeled using fuzzy logic system (FLS) to reduce the computational complexity and further optimized by HPSO and BBO to minimize the error.Knowledge based edge weight of the anchor node to determine the accurate coordinates of the target node.A novel proximity based performance index, to evaluate the proposed schemes. In this paper, we propose two computationally efficient range-free 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.

Computational Intelligence based algorithm for node localization in Wireless Sensor Networks

Accurate location of target nodes is highly desirable in a Wireless Sensor Network (WSN) as it has a strong impact on overall performance of the WSN. This paper proposes the application of H-Best Particle Swarm Optimization (HPSO) and Biogeography Based Optimization (BBO) algorithms for distributed optimal localization of randomly deployed sensors. The proposed HPSO algorithm is modeled for fast and mature convergence, though previous PSO models had only fast convergence but less mature. Biogeography is a school work (collective learning) of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i.e., problem solutions. WSN localization problem is formulated as an NP-Hard optimization problem because of its size and complexity. In this work, an error model is described for estimation of optimal node location in a manner such that the location error is minimized using HPSO and BBO algorithms. Proposed HPSO and BBO algorithms are matured to optimize the sensors locations and perform better as compared to the existing optimization algorithms such as Genetic Algorithms (GAs), and Simulated Annealing Algorithm (SAA). Comparative study reveals that the HPSO yields improved performance in terms of faster, matured, and accurate localization as compared to global best (gbest) PSO.

Secure communication using memristor based chaotic circuit

Chaotic signals are difficult to be identified & predicted as they have an element of randomness & are aperiodic. Hence, recently many researchers have been trying to utilize these characteristics of chaotic signals in secure communication systems. In this paper, a memristor based chaotic system has been used for the purpose of signal masking in communication systems. Simulation results verify the success of the scheme in a secure communication application.

Stochastic Algorithms for 3D Node Localization in Anisotropic Wireless Sensor Networks

This paper proposes two range based 3D node localization algorithms using application of Hybrid Particle Swarm Optimization (HPSO) and Biogeography Based Optimization (BBO) for anisotropic Wireless Sensor Networks (WSNs). Target nodes and anchor nodes are randomly deployed with constraints over three layer boundaries. The anchor nodes are randomly distributed over top layer only and target nodes over middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and an heterogenous property (different battery backup statuses) of devices are considered. PSO models provide fast but less mature convergence whereas the proposed HPSO algorithm provides fast and mature convergence. Biogeography is based upon the collective learning of geographical allotment of biological organisms. BBO has a new comprehensive energy based on the science of biogeography and apply migration operator to share selective information between different habitats, i.e., problem solutions. Due to size and complexity of WSN, localization problem is articulated as an NP-hard optimization problem . In this work, an error model in a highly noisy environment is depicted for estimation of optimal node location to minimize the location error using HPSO and BBO algorithms. The simulation results establish the strength of the proposed algorithms by equating the performance in terms of the number of target nodes localized with accuracy, and computation time. It has been observed that existing sensor networks localization algorithms are not significant to support the rescue operations involving human lives. Proposed algorithms are beneficial for rescue operations too to find out the accurate location of target nodes in highly noisy environment.

Heat transfer and friction factor of solar air heater having duct roughened artificially with discrete multiple v-ribs

In this paper, results of an experimental investigation on heat transfer and friction in a rectangular duct roughened with discrete multi v-shaped rib on one of its broad wall are presented. The discretizing of multi v-shaped rib is done by providing small symmetrical gap equal to rib height in both legs of multi v-rib. The artificially roughened duct was investigated having width to height ratio (W/H) of 12, relative width ratio (W/w) of 6, relative roughness pitch (P/e) of 10, relative roughness height (e/Dh) of 0.0433, angle of attack (α) of 60°, and relative gap distance (Gd/Lv) of 0.55. The relative gap width (g/e) was varied from 0.5 to 1.5. The heat transfer and friction factor results obtained experimentally were compared with those of smooth duct under similar experimental conditions. It is seen that there is a significant change in Nusselt number and friction factor as a result of providing discrete multi v-shape ribs. The enhancement in Nusselt number is found to be 6.32 times (that of smooth s...

Fuzzy FPGA based captive power management

The use of captive power is on the rise across the globe. In India alone, the estimated capacity of captive power plants is nearly 20,000 MW and most of the practical power plants are run sub-optimally by operators (albeit experienced ones). This calls for low-cost dedicated automatic controllers for the efficient management of captive power. Some studies have been reported in recent literature for the power management of islanded power plants, ad hoc network etc., but most studies are confined to computer simulations only. This paper reports a novel practical implementation of a dedicated real-time fuzzy logic controller on a low cost field programmable gate array chip for the management of 125 kW of captive power available from a medium capacity diesel generator set. A college campus has been chosen as the site for this implementation since it represents requirements typically suited for captive power use

Experimental investigation of thermo hydraulic performance due to angle of attack in solar air heater duct equipped with V–shaped perforated blockages

An experimental investigation has been performed to study the effect on thermo hydraulic performance parameter of V–shaped perforated blockages attached on one broad heated wall of a rectangular solar air heater duct. The rectangular duct was equipped with V–shaped perforated blockages having aspect ratio of 12. The geometrical parameters of blockage were taken as relative blockages height value of 0.8, relative pitch ratio value of 8, open area ratio value of 20%. An angle of attack was varied from 30° to 75°. The experiment encompassed with Reynolds number from 2,000 to 20,000. Experimental data has been collected to calculate Nusselt number, friction factor and thermo–hydraulic performance parameter. It was observed that there was a significant effect on Nusselt number, friction factor and thermo hydraulic performance parameter due to angle of attack.

GA optimized time delayed feedback control of chaos in a memristor based chaotic circuit

Chaotic state of a nonlinear system may be harmful due to its extreme sensitivity to initial conditions and irregularity in behavior. This paper addresses the problem of controlling chaos in a memristor based chaotic circuit using time delayed feedback method. Genetic algorithm has been used as a search tool to optimize the feedback path gain. Extensive computer simulations demonstrate that successful chaos control can be achieved by using this scheme, leading the systems chaotic state towards a fixed point or sustained oscillations depending on the range of feedback gain values.

Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller

This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.