Wireless body sensor networks are widely used for monitoring individuals in assisted living facilities and has emerged as a promising technology in e-healthcare. Such networks consist of sensors on the body or clothing of an individual for measuring vital signals such as heart beat, body temperature, and electrocardiogram. This enables patients to experience greater physical mobility and independence eliminating the need to stay in the hospital. Efficient and reliable transmission of data from on body sensors to medical personnel via multi-hop routing is critical for continuous health monitoring. In this paper, we propose a new routing algorithm for energy efficient routing in body sensor networks for reliable health monitoring. We model the routing problem as a constrained multi-objective optimization problem maximizing the throughput while minimizing the energy consumption subject to a constraint on end to end latency. We have designed a new constrained multi-objective genetic algorithm (CMOGA) for obtaining energy efficient routes. Simulation results show that CMOGA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.

In recent years, much attention has been given to wireless sensor networks (WSNs) research, due to the growth in Micro-Electro-Mechanical Systems technology. IEEE 802.15.4 is starting to emerge as the next generation wireless standard for low-rate wireless personal area networks. Zigbee is a standard that builds upon IEEE 802.15.4 and offers low power, low data rate, and short range networking for wireless battery powered devices. The Ad hoc On-demand Distance Vector (AODV) routing protocol enables the routing of data between a source and destination in mesh networks. The Dynamic MANET On-demand routing protocol (DYMO) is a successor to AODV that was designed for wireless ad-hoc networks. In this paper, we present a performance comparison of AODV and DYMO based on the Zigbee standard. The performance comparison is based on the following metrics: packet delivery ratio, end-to-end delay, and energy consumption. Our work was implemented for realistic node densities and simulation times in non-beacon enabled Zigbee networks. Our simulations show that the performance of the protocols depend on the network topology. The results obtained indicate that DYMO outperforms AODV with respect to packet delivery ratio (PDR) and energy consumption.

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

This paper investigates the probabilistic connectivity of wireless sensor networks in the presence of channel fading. Due to the stochastic nature of wireless channels in sensor networks, the geometric disk shaped model widely used for connectivity analysis of sensor networks can be misleading. In this paper, we develop a mathematical model to evaluate the probabilistic connectivity of sensor networks which incorporates the characteristics of wireless channels, multi-access interference, the network topology and the propagation environment. We present an analytical framework for the computation of node isolation probability and network connectivity under different channel fading models. We also analyze the connectivity of sensor networks in the presence of unreliable sensors. We present numerical and simulation results that compare different regular topologies in terms of several metrics such as node isolation probability, end-to-end connectivity, and network connectivity.

Efficient localization of a ballistic missile is an important task in missile defense problems. This paper formulates and solves the sensor placement problem for efficient estimation of the missile location. The first part of this paper develops a mathematical framework for the localization of the missile using multiple sensors based on Cramer-Rao lower bound (CRLB) analysis. We derive the Fisher information matrix to facilitate the evaluation of estimation accuracy. The second part of the paper presents an evolutionary algorithm for obtaining the sensor placements. Simulation results show that the evolutionary algorithm outperforms a greedy sensor placement algorithm and obtains sensor placements with very low estimation error.

An approach for data fusion in wireless sensor networks involves the use of mobile agents that selectively visit the sensors and incrementally fuse the data, thereby eliminating the unnecessary transmission of irrelevant or non-critical data. The order of sensors visited along the route determines the quality of the fused data and the communication cost. We model the mobile agent routing problem as a multi-objective optimization problem, maximizing the total detected signal energy while minimizing the energy consumption and path loss. Simulation results show that this problem can be solved successfully using evolutionary multi-objective algorithms such as EMOCA and NSGA-II. This approach also enables choosing between two alternative routing algorithms, to determine which one results in higher detection accuracy.

The authors present a guidance scheme that provides accurate tracking and faster minimization of tracking errors of the front and rear ends of automatic guided vehicles (AGVs). Sensors provided at the front and rear provide the position and orientation of the front and rear ends of the vehicle relative to the track. Control laws that make use of this information have been devised to speedily achieve accurate tracking of the front and rear ends of the vehicle with minimum overshoots. The control laws are chosen based on the presence or absence of curvature and also based on the relative location of the longitudinal axis of the vehicle relative to the track. The gains are modified online to achieve proper tracking. Simulation results are provided for illustration.>