A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints of cost and power consumption make it infeasible to equip each sensor node in the network with a global position system(GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use several mobile anchors which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. This paper proposes a mobile anchor assisted localization algorithm based on regular hexagon (MAALRH) in two-dimensional WSNs, which can cover the whole monitoring area with a boundary compensation method. Unknown nodes calculate their positions by using trilateration. We compare the MAALRH with HILBERT, CIRCLES, and S-CURVES algorithms in terms of localization ratio, localization accuracy, and path length. Simulations show that the MAALRH can achieve high localization ratio and localization accuracy when the communication range is not smaller than the trajectory resolution.The work is supported by the Natural Science Foundation of Jiangsu Province of China, no. BK20131137; the Applied Basic Research Program of Nantong Science and Technology Bureau, no. BK2013032; and the Guangdong University of Petrochemical Technology's Internal Project, no. 2012RC0106. Jaime Lloret's work has been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516. Joel J. P. C. Rodrigues's work has been supported by "Instituto de Telecomunicacoes," Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, by national funding from the Fundacao para a Ciencia e a Tecnologia (FCT) through the Pest-OE/EEI/LA0008/2013 Project.Han, G.; Zhang, C.; Lloret, J.; Shu, L.; Rodrigues, JJPC. (2014). A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks. Scientific World Journal. https://doi.org/10.1155/2014/219371SLiu, Y., Yang, Z., Wang, X., & Jian, L. (2010). Location, Localization, and Localizability. Journal of Computer Science and Technology, 25(2), 274-297. doi:10.1007/s11390-010-9324-2Akcan, H., Kriakov, V., Brönnimann, H., & Delis, A. (2010). Managing cohort movement of mobile sensors via GPS-free and compass-free node localization. Journal of Parallel and Distributed Computing, 70(7), 743-757. doi:10.1016/j.jpdc.2010.03.007Akyildiz, I. F., Weilian Su, Sankarasubramaniam, Y., & Cayirci, E. (2002). 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Efficient Range-Free Monte-Carlo-Localization for Mobile Wireless Sensor Networks

Das Hauptproblem von Lokalisierungsalgorithmen für WSNs basierend auf Ankerknoten ist die Abhängigkeit von diesen. Mobilität im Netzwerk kann zu Topologien führen, in denen einzelne Knoten oder ganze Teile des Netzwerks temporär von allen Ankerknoten isoliert werden. In diesen Fällen ist keine weitere Lokalisierung möglich. Dies wirkt sich primär auf den Lokalisierungsfehler aus, der in diesen Fällen stark ansteigt. Des weiteren haben Betreiber von Sensornetzwerken Interesse daran, die Anzahl der kosten- und wartungsintensiveren Ankerknoten auf ein Minimum zu reduzieren. Dies verstärkt zusätzlich das Problem von nicht verfügbaren Ankerknoten während des Netzwerkbetriebs. In dieser Arbeit werden zunächst die Vor- und Nachteile der beiden großen Hauptkategorien von Lokalisierungsalgorithmen (range-based und range-free Verfahren) diskutiert und eine Studie eines oft für range-based Lokalisierung genutzten Distanzbestimmungsverfahren mit Hilfe des RSSI vorgestellt. Danach werden zwei neue Varianten für ein bekanntes range-free Lokalisierungsverfahren mit Namen MCL eingeführt. Beide haben zum Ziel das Problem der temporär nicht verfügbaren Ankerknoten zu lösen, bedienen sich dabei aber unterschiedlicher Mittel. SA-MCL nutzt ein dead reckoning Verfahren, um die Positionsschätzung vom letzten bekannten Standort weiter zu führen. Dies geschieht mit Hilfe von zusätzlichen Sensorinformationen, die von einem elektronischen Kompass und einem Beschleunigungsmesser zur Verfügung gestellt werden. PO-MCL hingegen nutzt das Mobilitätsverhalten von einigen Anwendungen in Sensornetzwerken aus, bei denen sich alle Knoten primär auf einer festen Anzahl von Pfaden bewegen, um den Lokalisierungsprozess zu verbessern. Beide Methoden werden durch detaillierte Netzwerksimulationen evaluiert. Im Fall von SA-MCL wird außerdem eine Implementierung auf echter Hardware vorgestellt und eine Feldstudie in einem mobilen Sensornetzwerk durchgeführt. Aus den Ergebnissen ist zu sehen, dass der Lokalisierungsfehler in Situationen mit niedriger Ankerknotendichte im Fall von SA-MCL um bis zu 60% reduziert werden kann, beziehungsweise um bis zu 50% im Fall von PO-MCL.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

Wireless Sensor Localization: Error Modeling and Analysis for Evaluation and Precision

Wireless sensor networks (WSNs) have shown promise in a broad range of applications. One of the primary challenges in leveraging WSNs lies in gathering accurate position information for the deployed sensors while minimizing power cost. In this research, detailed background research is discussed regarding existing methods and assumptions of modeling methods and processes for estimating sensor positions. Several novel localization methods are developed by applying rigorous mathematical and statistical principles, which exploit constraining properties of the physical problem in order to produce improved location estimates. These methods are suitable for one-, two-, and three-dimensional position estimation in ascending order of difficulty and complexity. Unlike many previously existing methods, the techniques presented in this dissertation utilize practical, realistic assumptions and are progressively designed to mitigate incrementally discovered limitations. The design and results of a developed multiple-layered simulation environment are also presented that model and characterize the developed methods. The approach, developed methodologies, and software infrastructure presented in this dissertation provide a framework for future endeavors within the field of wireless sensor networks

Geometric Constraint Based Range Free Localization Scheme For Wireless Sensor Networks (WSNs)

Localization of the wireless sensor networks (WSNs) is an emerging area of research. The accurate localization is essential to support extended network lifetime, better covering, geographical routing, and congested free network. In this thesis, we proposed four distributed range-free localization schemes. The proposed schemes are based on the analytical geometry, where an arc is used as the geometric primitive shape. The simulation and experimental validation are performed to evaluate the performance of the proposed schemes. First, we have proposed a mobile beacon based range-free localization scheme (MBBRFLS). The proposed scheme resolved the two underlying problems of the constraint area based localization: (i) localization accuracy depends on the size of the constraint area, and (2) the localization using the constraint area averaging. In this scheme, the constraint area is used to derive the geometric property of an arc. The localization begins with an approximation of the arc parameters. Later, the approximated parameters are used to generate the chords. The perpendicular bisector of the chords estimate the candidate positions of the sensor node. The valid position of the sensor node is identified using the logarithmic path loss model. The performance of proposed scheme is compared with Ssu and Galstyan schemes. From the results, it is observed that the proposed scheme at varying DOI shows 20.7% and 11.6% less localization error than Ssu and Galstyan schemes respectively. Similarly, at the varying beacon broadcasting interval the proposed scheme shows 18.8% and 8.3% less localization error than Ssu and Galstyan schemes respectively. Besides, at the varying communication range, the proposed scheme shows 18% and 9.2% less localization error than Ssu and Galstyan schemes respectively. To further enhance the localization accuracy, we have proposed MBBRFLS using an optimized beacon points selection (OBPS). In MBBRFLS-OBPS, the optimized beacon points minimized the constraint area of the sensor node. Later, the reduced constraint area is used to differentiate the valid or invalid estimated positions of the sensor node. In this scheme, we have only considered the sagitta of a minor arc for generating the chords. Therefore, the complexity of geometric calculations in MBBRFLS-OBPS is lesser than MBBRFLS. For localization, the MBBRFLS-OBPS use the perpendicular bisector of the chords (corresponding to the sagitta of minor arc) and the approximated radius. The performance of the proposed MBBRFLS-OBPS is compared with Ssu, Galstyan, and Singh schemes. From the results, it is observed that the proposed scheme using CIRCLE, vii SPIRAL, HILBERT, and S-CURVE trajectories shows 74.68%, 78.3%, 73.9%, and 70.3% less localization error than Ssu, Galstyan, and Singh schemes respectively. Next, we have proposed MBBRFLS using an optimized residence area formation (ORAF). The proposed MBBRFLS-ORAF further improves the localization accuracy. In this scheme, we have used the adaptive mechanism corresponding to the different size of the constraint area. The adaptive mechanism defines the number of random points required for the different size of the constraint area. In this scheme, we have improved the approximation accuracy of the arc parameters even at the larger size of the constraint area. Therefore, the localization accuracy is improved. The previous scheme MBBRFLS-OBPS use the residence area of the two beacon points for approximation. Therefore, the larger size of the constraint area degrades the approximation accuracy. In the MBBRFLS-ORAF, we have considered the residence area of the three non-collinear beacon points, which further improves the localization accuracy. The performance of the proposed scheme is compared with Ssu, Lee, Xiao, and Singh schemes. From the results, it is observed that the proposed MBBRFLS-ORAF at varying communication range shows 73.2%, 48.7%, 33.2%, and 20.7% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Similarly, at the different beacon broadcasting intervals the proposed MBBRFLS-ORAF shows 75%, 53%, 38%, and 25% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Besides, at the varying DOI the proposed MBBRFLS-ORAF shows 76.3%, 56.8%, 52%, and 35% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Finally, we have proposed a localization scheme for unpredictable radio environment (LSURE). In this work, we have focused on the radio propagation irregularity and its impact on the localization accuracy. The most of the geometric constraint-based localization schemes suffer from the radio propagation irregularity. To demonstrate its impact, we have designed an experimental testbed for the real indoor environment. In the experimental testbed, the three static anchor nodes assist a sensor node to perform its localization. The impact of radio propagation irregularity is represented on the constraint areas of the sensor node. The communication range (estimated distance) of the anchor node is derived using the logarithmic regression model of RSSI-distance relationship. The additional error in the estimated distances, and the different placement of the anchor nodes generates the different size of the constraint areas. To improve the localization accuracy, we have used the dynamic circle expansion technique. The performance of the proposed LSURE is compared with APIT and Weighted Centroid schemes using the various deployment scenarios of the anchor nodes. From the results, it is observed that the proposed LSURE at different deployment scenarios of anchor nodes shows 65.94% and 73.54% less localization error than APIT and Weighted Centroid schemes

Cooperative Localization on Computationally Constrained Devices

Cooperative localization is a useful way for nodes within a network to share location information in order to better arrive at a position estimate. This is handy in GPS contested environments (indoors and urban settings). Most systems exploring cooperative localization rely on special hardware, or extra devices to store the database or do the computations. Research also deals with specific localization techniques such as using Wi-Fi, ultra-wideband signals, or accelerometers independently opposed to fusing multiple sources together. This research brings cooperative localization to the smartphone platform, to take advantage of the multiple sensors that are available. The system is run on Android powered devices, including the wireless hotspot. In order to determine the merit of each sensor, analysis was completed to determine successes and failures. The accelerometer, compass, and received signal strength capability were examined to determine their usefulness in cooperative localization. Experiments at meter intervals show the system detected changes in location at each interval with an average standard deviation of 0.44m. The closest location estimates occurred at 3m, 4m and 6m with average errors of 0.15m, 0.11m, and 0.07m respectively. This indicates that very precise estimates can be achieved with an Android hotspot and mobile nodes

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known

Security in Mobile Networks: Communication and Localization

Nowadays the mobile networks are everywhere. The world is becoming more dependent on wireless and mobile services, but the rapid growth of these technologies usually underestimates security aspects. As wireless and mobile services grow, weaknesses in network infrastructures become clearer. One of the problems is privacy. Wireless technologies can reduce costs, increase efficiencies, and make important information more readily and widely available. But, there are also risks. Without appropriate safeguards, these data can be read and modified by unauthorized users. There are many solutions, less and more effective, to protect the data from unauthorized users. But, a specific application could distinguish more data flows between authorized users. Protect the privacy of these information between subsets of users is not a trivial problem. Another problem is the reliability of the wireless service. Multi-vehicle systems composed of Autonomous Guided Vehicles (AGVs) are largely used for industrial transportation in manufacturing and logistics systems. These vehicles use a mobile wireless network to exchange information in order to coordinate their tasks and movements. The reliable dissemination of these information is a crucial operation, because the AGVs may achieve an inconsistent view of the system leading to the failure of the coordination task. This has clear safety implications. Going more in deep, even if the communication are confidential and reliable, anyway the positioning information could be corrupted. Usually, vehicles get the positioning information through a secondary wireless network system such as GPS. Nevertheless, the widespread civil GPS is extremely fragile in adversarial scenarios. An insecure distance or position estimation could produce security problems such as unauthorized accesses, denial of service, thefts, integrity disruption with possible safety implications and intentional disasters. In this dissertation, we face these three problems, proposing an original solution for each one