Samrat L. Sabat

Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks

Network lifetime plays an integral role in setting up an efficient wireless sensor network. The objective of this paper is twofold. The first one is to deploy sensor nodes at optimal locations such that the theoretically computed network lifetime is maximum. The second is to schedule these sensor nodes such that the network attains the maximum lifetime. Thus, the overall objective of this paper is to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, we use this knowledge to compute locations of deployment such that the network lifetime is maximum. Further, the nodes are scheduled to achieve this upper bound. In this paper, we use artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling. A comparative study shows that artificial bee colony algorithm performs better for sensor deployment problem. The proposed heuristic was able to achieve the theoretical upper bound in all the experimented cases.

Artificial bee colony algorithm for small signal model parameter extraction of MESFET

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16-element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5-25GHz.

MESFET DC model parameter extraction using Quantum Particle Swarm Optimization

This paper presents two techniques for DC model parameter extraction for a Gallium Arsenide (GaAs) based MEtal Semiconductor Field Effect Transistor (MESFET) device. The proposed methods uses Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) methods for optimizing the difference between measured data and simulated data. Simulated data are obtained by using four different popular DC models. These techniques avoid complex computational steps involved in traditional parameter extraction techniques. The performance comparison in terms of quality of solution and execution time of classical PSO and QPSO to extract the model parameters are presented. The validity of this approach is verified by comparing the simulated and measured results of a fabricated GaAs MESFET device with gate length of 0.7 lm and gate width of 600 l m( 4� 150). Simulation results indicate that both the technique based on PSO and QPSO accurately extracts the model parameters of MESFET.

Integrated Learning Particle Swarm Optimizer for global optimization

This study proposes a novel Integrated Learning Particle Swarm Optimizer (ILPSO), for optimizing complex multimodal functions. The algorithm modifies the learning strategy of basic PSO to enhance the convergence and quality of solution. The ILPSO approach finds the diverged particles and accelerates them towards optimal solution. This novel study also introduces the particles updating strategy based on hyperspherical coordinates system. This is especially helpful in handling evenly distributed multiple minima. The proposed technique is integrated with comprehensive learning strategy to explore the solution effectively. The performance comparison is carried out against different high quality PSO variants on the set of standard benchmark functions with and without coordinate rotation and with asymmetric initialization. Proposed ILPSO algorithm is efficient in terms of convergence rate, solution accuracy, standard deviation, and computation time compared with other PSO variants. Friedman non-parametric statistical test followed by Dunn post analysis results indicate that the proposed ILPSO algorithm is an effective technique to optimize complex multimodal functions of higher dimension.

Sensor deployment in irregular terrain using Artificial Bee Colony algorithm

The main objective of sensor deployment problem in Wireless Sensor Network (WSN) is to use minimum number of sensor nodes with given sensing range that can cover any target in the coverage area to monitor the environment. The optimal sensor deployment enables accurate sensing information on target behavior with minimum sensing range and number of sensor nodes. The target coverage terrain in a locality need not be a smooth rectangle which makes the deployment problem more complex. The optimal sensor deployment is a problem of maximizing coverage and minimizing number of sensor nodes which has been proved to be NP-hard. Artificial Bee Colony (ABC) algorithm, inspired by the food foraging behavior of honey bees is recently being used for different optimization problems and found to be efficient for a wide range of applications including data clustering. In this paper, the sensor deployment problem is modeled as a data clustering problem and optimal solution to the deployment problem is obtained using ABC algorithm. The results show that ABC algorithm gives robust and good quality of solution.

Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems

Most of the real world science and engineering optimisation problems are non-linear and constrained. This paper presents a hybrid algorithm by integrating particle swarm optimisation with stochastic ranking for solving standard constrained numerical and engineering benchmark problems. Stochastic ranking technique that uses bubble sort mechanism for ranking the solutions and maintains a balance between the objective and the penalty function. The faster convergence of particle swarm optimisation and the ranking technique are the major motivations for hybridising these two concepts and to propose the stochastic ranking particle swarm optimisation (SRPSO) technique. In this paper, SRPSO is used to optimise 15 continuous constrained single objective benchmark functions and five well-studied engineering design problems. The performance of the proposed algorithm is evaluated based on the statistical parameters such mean, median, best, worst values and standard deviations. The SRPSO algorithm is compared with six recent algorithms for function optimisation. The simulation results indicate that the SRPSO algorithm performs much better while solving all the five standard engineering design problems where as it gives a competitive result for constrained numerical benchmark functions.

Wireless Sensor Network Security Model Using Zero Knowledge Protocol

Wireless Sensor Networks (WSNs) offer an excellent opportunity to monitor environments, and have a lot of interesting applications, some of which are quite sensitive in nature and require full proof secured environment. The security mechanisms used for wired networks cannot be directly used in sensor networks as there is no user-controlling of each individual node, wireless environment, and more importantly, scarce energy resources. In this paper, we address some of the special security threats and attacks in WSNs. We propose a scheme for detection of distributed sensor cloning attack and use of zero knowledge protocol (ZKP) for verifying the authenticity of the sender sensor nodes. The cloning attack is addressed by attaching a unique fingerprint to each node, that depends on the set of neighboring nodes and itself. The fingerprint is attached with every message a sensor node sends. The ZKP is used to ensure non transmission of crucial cryptographic information in the wireless network in order to avoid man-in-the middle (MITM) attack and replay attack. The paper presents a detailed analysis for various scenarios and also analyzes the performance and cryptographic strength.

A modified Sage-Husa adaptive Kalman filter for denoising Fiber Optic Gyroscope signal

Fiber Optic Gyroscope (FOG) is a key component in Inertial Navigation System. The performance of FOG degrades due to different types of random errors in the measured signal. Although Kalman filter and its variants like Sage-Husa Kalman filters are being used to denoise the Gyroscope signal the performance of Kalman filter is limited by the initial values of measurement and process noise covariance matrix, and transition matrix. To address this problem, this paper uses modified Sage-Husa adaptive Kalman filter to denoise the FOG signal. In this work, the random error of fiber optic gyroscope is modeled using a first order auto regressive (AR) model and used the coefficients of the model to initialize the transition matrix of Sage-Husa Adaptive Kalman filter. Allan variance analysis is used to quantify the random errors of the measured and denoised signal. The performance of proposed algorithm is compared with conventional Kalman filter and the simulation results show that the modified SageHusa adaptive Kalman filter (SHAKF) algorithm outperforms the conventional Kalman filter technique while denoising FOG signal.

Differential Evolution and swarm intelligence techniques for analog circuit synthesis

This paper presents an application of different variants of Differential Evolution (DE) and swarm intelligence techniques for analog circuit synthesis. A CMOS Miller Operational Trans-conductance Amplifier (OTA) that satisfies certain design specifications is designed as a case study. Two important swarm intelligence techniques namely Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) are used for this problem. The optimization of CMOS parameters are carried out using evolutionary algorithms in MATLAB interfaced with WINSPICE circuit simulator. Chaotic DE algorithms are used for performance comparison along with ABC and PSO. The results show that all three evolutionary techniques are capable of designing the Miller OTA satisfying most of the design specifications. However Chaotic DE algorithm performs better in terms of quality of solution, robustness and computational time as compared to standard DE, PSO and ABC algorithms.

FPGA implementation and performance study of spectrum sensing based on entropy estimation using cyclic features

This work presents a spectrum sensing technique based on the entropy of frequency domain autocorrelation of receiving signal at different cyclic frequencies. The performance of the proposed sensing technique is compared with other sensing techniques such as energy detection using Bayesian and Neyman-Pearson criteria, entropy estimation under frequency domain, cyclostationary feature detection. The performance of sensing algorithms is also analyzed for single node and multinode/cooperative environment under most probable channel effects such as fading, shadowing, receivers uncertainty and free space path loss using Monte-Carlo methods. Simulation results reveal that the proposed sensing technique is able to detect signals of signal-to-noise ratio up to -24dB with five nodes in cooperation while maintaining a false alarm probability of 0.1 and a detection probability of 0.9. The proposed sensing algorithm is also implemented in Virtex-4 Field Programmable Gate Arrays.