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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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. This paper presents a novel approach to placing access points (APs) in wireless local area networks (WLAN). Unlike the traditional methods focusing on coverage maximization, we investigate how to place APs from the perspective of an indoor location system. We present a framework for linking the placement of APs and the positioning performance. Our algorithm, namely maximizing SNR (signal-to-noise ratio), aims at choosing a proper set of APs' locations so that the signal is maximized and the noise is minimized simultaneously. The proposed algorithm regards the location discriminant information as the signal and the degree of unstable measurements as the noise. Such numerical values provide a good theoretical formulation to measure the influence of the AP deployment. In our location system, we utilize such SNR ratio to deploy APs so as to reduce the positioning errors. Our location system is developed in a real-world WLAN environment, where the realistic measurements were collected. The experimental results show that the positioning accuracy is improved based on our deployment approach in different scenarios. Sensor positioning is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). Current localization approaches can be divided into two groups: range-based and range-free. Due to the high costs and critical as-sumptions, the range-based schemes are often impractical for WSNs. The existing range-free schemes, on the other hand, suffer from poor accuracy and low scalability. Without the help of a large number of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose the Rendered Path (REP) protocol. To the best of our knowledge, REP is the only range-free protocol for locating sen-sors with constant number of seeds in anisotropic sensor net-works. In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.