Wireless sensor networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational intelligence (CI) techniques are particularly suitable for enhancing these systems. However, when embedding CI into wireless sensors, severe hardware limitations must be taken into account. In this paper we investigate the possibility to perform an online, distributed optimization process within a WSN. Such a system might be used, for example, to implement advanced network features like distributed modelling, self-optimizing protocols, and anomaly detection, to name a few. The proposed approach, called DOWSN (distributed optimization for WSN) is an island-model infrastructure in which each node executes a simple, computationally cheap (both in terms of CPU and memory) optimization algorithm, and shares promising solutions with its neighbors. We perform extensive tests of different DOWSN configurations on a benchmark made up of 15 continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance. Finally, we profile energy and memory consumption of DOWSN to show the efficient usage of the limited hardware resources available on the sensor nodes.
Another issue which arises in WSNs (especially mobile networks) is finding the exact position of nodes. Similarly to optimal deployment, localization can be formulated as an optimization problem where the position error (difference between actual and estimated position) has to be minimized. In @cite_44 , a micro-Genetic Algorithm is used to improve the accuracy of Ad-hoc Positioning System (APS), a WSNs-specific localization algorithm. An embedded implementation of a hybrid method combining the Gauss-Newton Algorithm (GNA) and the custom PSO @cite_45 is described in @cite_55 . Further applications of PSO and EAs are presented in @cite_45 , @cite_22 , while @cite_40 propose a complete survey of bio-inspired techniques for localization in WSNs. An alternative approach is proposed in @cite_12 , where the localization problem is formulated in terms of pattern recognition and solved by means of a kernel-based learning algorithm.
@cite_22 @cite_55 @cite_44 @cite_40 @cite_45 @cite_12
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With the emergence of wireless sensor networks and pervasive computing, innovative location-aware technologies and services are being investigated. Several iterative approaches employing distributed computations over sensors have been proposed in the literature for locating all the sensor nodes in the network. Due to their iterative nature these techniques are inefficient in terms of power, a very precious resource in sensor networks. This paper presents a novel power efficient approach aimed at identifying the locations of all the nodes in a sensor network given the location of a small subset of nodes. The technique, using evolution strategies, is independent of the ranging method used to estimate distances between nodes and involves sink nodes in the computation. The proposed approach provides substantial energy savings over existing techniques while providing comparable accuracy, and requires the presence of at least one neighbor for each sensor node compared to at least 3 neighbors for most of the existing techniques In this paper, a low-cost microcontroller-based system that uses the pedometer measurement and communication ranging between neighboring nodes of a wireless sensor network for localization is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. As the localization requires solving of nonlinear equations in real time, two optimization approaches, namely, the Gauss-Newton algorithm and the particle swarm optimization have been studied. The localization and optimization algorithms have been implemented with a microcontroller. The performance has been evaluated with experimental results. Wireless sensor networks are widely adopted in many location-sensitive applications including disaster management, environmental monitoring, military applications where the precise estimation of each node position is inevitably important when the absolute positions of a relatively small portion as anchor nodes of the underlying network were predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance path measures. Most of the existing localization methods focus on using different heuristic-based or mathematical techniques to increase the precision in position estimation. However, there were recent studies showing that nature-inspired algorithms like the ant-based or genetic algorithms can effectively solve many complex optimization problems. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm, as a post-optimizer into some existing localization methods such as the Ad-hoc Positioning System (APS) to further improve the accuracy of their position estimation. Obviously, our proposed MGA is highly adaptable and easily integrated into other localization methods. Furthermore, the remarkable improvements attained by our proposed MGA on both isotropic and anisotropic topologies of our simulation tests prompt for several interesting directions for further investigation. Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented. For many wireless sensor network applications, the localization of sensor nodes is an essential requirement. In this paper, a low cost localization system based on the measurements from a pedometer and communication ranging between neighboring nodes is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. The localization information is obtained through a probability based algorithm that requires the solving of a nonlinear optimization problem. To obtain the optimum location of the sensor nodes, the particle swarm optimization (PSO) scheme that can be realized with a microcontroller for real time application is investigated in this paper. Experimental results show that the proposed approach yields good performance. We show that the coarse-grained and fine-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory. This stems from an observation that the kernel function, which is a similarity measure critical to the effectiveness of a kernel-based learning algorithm, can be naturally defined in terms of the matrix of signal strengths received by the sensors. Thus we work in the natural coordinate system provided by the physical devices. This not only allows us to sidestep the difficult ranging procedure required by many existing localization algorithms in the literature, but also enables us to derive a simple and effective localization algorithm. The algorithm is particularly suitable for networks with densely distributed sensors, most of whose locations are unknown. The computations are initially performed at the base sensors, and the computation cost depends only on the number of base sensors. The localization step for each sensor of unknown location is then performed locally in linear time. We present an analysis of the localization error bounds, and provide an evaluation of our algorithm on both simulated and real sensor networks.