Soumyadip Sengupta

An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks

We propose an online, multiobjective optimization (MO) algorithm to efficiently schedule the nodes of a wireless sensor network (WSN) and to achieve maximum lifetime. Instead of dealing with traditional grid or uniform coverage, we focus on the differentiated or probabilistic coverage where different regions require different levels of sensing. The MO algorithm helps to attain a better tradeoff among energy consumption, lifetime, and coverage. The algorithm can be run every time a node failure occurs due to power failure of the node battery so that it may reschedule the network. This scheduling is modeled as a combinatorial, multiobjective, and constrained optimization problem with energy and noncoverage as the two objectives. The basic evolutionary multiobjective optimizer used is known as decomposition-based multiobjective evolutionary algorithm (MOEA/D) which is modified by integrating the concept of fuzzy Pareto dominance. The performance of the resulting algorithm, which is called MOEA/DFD, is compared with the performance of the original MOEA/D, which is another very well known MO algorithm called nondominated sorting genetic algorithm (NSGA-II), and an IBM optimization software package called CPLEX. In all the tests, MOEA/DFD is observed to outperform all other algorithms.

Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity

The increased demand of Wireless Sensor Networks (WSNs) in different areas of application have intensified studies dedicated to the deployment of sensor nodes in recent past. For deployment of sensor nodes some of the key objectives that need to be satisfied are coverage of the area to be monitored, net energy consumed by the WSN, lifetime of the network, and connectivity and number of deployed sensors. In this article the sensor node deployment task has been formulated as a constrained multi-objective optimization (MO) problem where the aim is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes while maintaining connectivity between each sensor node and the sink node for proper data transmission. We assume a tree structure between the deployed nodes and the sink node for data transmission. Our method employs a recently developed and very competitive multi-objective evolutionary algorithm (MOEA) known as MOEA/D-DE that uses a decomposition approach for converting the problem of approximation of the Pareto fronts (PF) into a number of single-objective optimization problems. This algorithm employs differential evolution (DE), one of the most powerful real parameter optimizers in current use, as its search method. The original MOEA/D has been modified by introducing a new fuzzy dominance based decomposition technique. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. We have compared the performance of the resulting algorithm, called MOEA/DFD, with the original MOEA/D-DE and another very popular MOEA called Non-dominated Sorting Genetic Algorithm (NSGA-II). The best trade-off solutions from MOEA/DFD based node deployment scheme have also been compared with a few single-objective node deployment schemes based on the original DE, an adaptive DE-variant (JADE), original particle swarm optimization (PSO), and a state-of-the art variant of PSO (Comprehensive Learning PSO). In all the test instances, MOEA/DFD performs better than all other algorithms. Also the proposed multi-objective formulation of the problem adds more flexibility to the decision maker for choosing the necessary threshold of the objectives to be satisfied.

An improved Multiobjective Evolutionary Algorithm based on decomposition with fuzzy dominance

This paper presents a new Multiobjective Evolutionary Algorithm (MOEA) based on decomposition, with fuzzy dominance (MOEA/DFD). The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. The diversity is maintained through the uniformly distributed weight vectors. In addition, Dynamic Resource Allocation (DRA) is used to distribute the computational effort based on the utilities of the individuals. To assess the performance of the proposed algorithm, experiments were conducted on two general benchmarks and ten unconstrained benchmark problems taken from the competition on real parameter MOEAs held under the 2009 IEEE Congress on Evolutionary Computation (CEC). As per the IGD metric, MOEA/DFD outperforms other major MOEAs in most cases.

Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation

In some technical applications like multiobjective online control an evenly spaced approximation of the Pareto front is desired. Since standard evolutionary multiobjective optimization (EMO) algorithms have not been designed for that kind of approximation we propose an archive-based plug-in method that builds an evenly spaced approximation using averaged Hausdorff measure between archive and reference front. In case of three objectives this reference font is constructed from a triangulated approximation of the Pareto front from a previous experiment. The plug-in can be deployed in online or offline mode for any kind of EMO algorithm.

Energy-efficient differentiated coverage of dynamic objects using an improved evolutionary multi-objective optimization algorithm with fuzzy-dominance

We present an energy efficient sensor manager for differentiated coverage of dynamic object group changing their positions with time. The information about the location of the object group is provided to the sensor manager. The manager invokes optimization algorithm whenever the obtained coverage falls below a threshold to sleep schedule the sensor network. Multi-objective Optimization (MO) algorithms help in finding a better trade-off among energy consumption, lifetime, and coverage. Here the motion of the particle is modeled to follow a polynomial variation and with a constant acceleration. We formulate the scheduling problem as a combinatorial, constrained and multi-objective optimization problem with energy and non-coverage as the two objectives to be minimized. The proposed scheme uses a recent variant of a powerful MO algorithm known as Decomposition based Multi-Objective Evolutionary Algorithm (MOEA/D). Systematic comparison with the original MOEA/D and another well-known MO algorithm, NSGA-II (Non-dominated Sorting Genetic Algorithm) quantifies the superiority of the proposed approach.

A Multi-Objective Evolutionary approach for linear antenna array design and synthesis

The linear antenna array design problem is one of the most important in electromagnetism. While designing a linear antenna array, the goal of the designer is to achieve the “minimum average side lobe level” and a “null control” in specific directions. In contrast to the existing methods that attempt to minimize a weighted sum of these two objectives considered here, in this paper our contribution is twofold. First, we have considered these as two distinct objectives which are optimized simultaneously in a multi-objective framework. Second, for directivity purposes, we have introduced another objective called the “maximum side lobe level” in the design formulation. The resulting multi-objective optimization problem is solved by using the recently-proposed decomposition-based Multi-Objective Particle Swarm Optimizer (dMOPSO). Our experimental results indicate that the proposed approach is able to obtain results which are better than those obtained by two other state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs). Additionally, the individual minima reached by dMOPSO outperform those achieved by two single-objective evolutionary algorithms.

An improved multi-objective optimization algorithm based on fuzzy dominance for risk minimization in biometric sensor network

Biometric system is very important for recognition in several security areas. In this paper we deal in designing biometric sensor manager by optimizing the risk. Risk is modeled as a multi-objective optimization with Global False Acceptance Rate and Global False Rejection Rate as two objectives. In practice, multiple biometric sensors are used and the decision is taken locally at each sensor and the data is passed to the sensor manager. At the sensor manager the data is fused using a fusion rule and the final decision is taken. The optimization involves designing the data fusion rule and setting the sensor thresholds. We have implemented a recent fuzzy dominance based decomposition technique for multi-objective optimization called MOEA/DFD and have compared its performance on other contemporary state-of-arts in multi-objective optimization field like MOEA/D, NSGAII. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. We have simulated the algorithms on different number of sensor setups consisting of 3, 6, 8 sensors respectively. We have also varied the apriori probability of imposter from 0.1 to 0.9 to verify the performance of the system with varying threat. One of the most significant advantages of using multi-objective optimization is that with a single run just by changing the decision making logic applied to the obtained Pareto front one can find the required threshold and decision strategies for varying threat of imposter. But with single objective optimization one need to run the algorithms each time with change in threat of imposter. Thus multi-objective representation appears to be more useful and better than single objective one. In all the test instances MOEA/DFD performs better than all other algorithms.

Risk minimization in biometric sensor networks: an evolutionary multi-objective optimization approach

Biometric systems aim at identifying humans by their characteristics or traits. This article addresses the problem of designing a biometric sensor management unit by optimizing the risk, which is modeled as a multi-objective optimization (MO) problem with global false acceptance rate and global false rejection rate as the two objectives. In practice, when multiple biometric sensors are used, the decision is taken locally at each sensor and the data are passed to the sensor manager. At the sensor manager, the data are fused using a fusion rule and the final decision is taken. The optimization process involves designing the data fusion rule and setting of the sensor thresholds. In this work, we employ a fuzzy dominance and decomposition-based multi-objective evolutionary algorithm (MOEA) called MOEA/DFD and compare its performance with two state-of-the-art MO algorithms: MOEA/D and NSGA-II in context to the risk minimization task. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. The MO algorithms are simulated on different number of sensor setups consisting of three, six, and eight sensors. The a priori probability of imposter is also varied from 0.1 to 0.9 to verify the performance of the system with varying degrees of threat. One of the most significant advantages of using the MO framework is that with a single run, just by changing the decision-making logic applied to the obtained Pareto front, one can find the required threshold and decision strategies for varying threats of imposter. However, with single-objective optimization, one needs to run the algorithms each time with change in the threat of imposter. Thus, multi-objective formulation of the problem appears to be more useful and better than the single-objective one. In all the test instances, MOEA/DFD performs better than all the other algorithms.

An improved multi-objective algorithm based on decomposition with fuzzy dominance for deployment of wireless sensor networks

The aim of this paper is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes maintaining connectivity between each sensor node and the sink node for proper data transmission. We have also assumed tree structure of communication between the deployed nodes and the sink node for data transmission. We have modeled the sensor node deployment problem as a multi-objective constrained problem maintaining all the above requirements. We have proposed a new fuzzy dominance based decomposition technique called MOEA/DFD and have compared its performance on other contemporary state-of-arts in multi-objective optimization field like MOEA/D and NSGAII. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. MOEA/DFD performs better than all other algorithms.

A New Rank Constraint on Multi-view Fundamental Matrices, and Its Application to Camera Location Recovery

Accurate estimation of camera matrices is an important step in structure from motion algorithms. In this paper we introduce a novel rank constraint on collections of fundamental matrices in multi-view settings. We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6. Moreover, this matrix forms the symmetric part of a rank 3 matrix whose factors relate directly to the corresponding camera matrices. We use this new characterization to produce better estimations of fundamental matrices by optimizing an L1-cost function using Iterative Re-weighted Least Squares and Alternate Direction Method of Multiplier. We further show that this procedure can improve the recovery of camera locations, particularly in multi-view settings in which fewer images are available.