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Localization in wireless sensor network is attracting many researchers because of its significance in many real-time applications to identify the origin of events. Many applications such as battlefield, environment monitoring, habitat monitoring, and forest fire detection, etc. wherein origin of event occurrence is very important. Hence, researchers has proposed different algorithms for location identification of the sensor nodes in wireless sensor network, utilizing simulations or by creating the real-time scenarios. This paper presents the comprehensive review of localization algorithms based on soft computing techniques. This paper also presents the Particle Swarm Optimization (PSO) based clustering to find the location of the sensor nodes using the combination of Received Signal Strength Indicator (RSSI) and the Angle of Arrival (AoA). This combination of RSSI and AoA makes the clustering algorithm more efficient to achieve better accuracy in location estimation with less computational complexity. The lifetime of the sensor network which is mainly dependent on the energy dissipation of the sensor nodes is also considered in the proposed approach. The energy dissipation in the proposed approach can be reduced by adopting the density control strategy during cluster formation and reducing the communication among the sensor nodes and hence increasing the lifetime of the network. [ABSTRACT FROM AUTHOR]

Received Signal Strength (RSS) is notoriously inaccurate when used for range estimates in localization applications of wireless sensor networks. This paper demonstrates that better localization accuracies can be achieved by making the inter-node distance conform to the ranks of inter-node RSS, instead of the actual received signal strength when using the common Multi-Dimensional Scaling (MDS) localization approach. In order to further improve on the non-metric MDS localization results structural clustering constraints to the nodes were included in the optimization objective function. Experimental and simulation results show that the more clustering constraints that are included the better the localization results. [ABSTRACT FROM AUTHOR]

Localization is particularly important in wireless networks. With the rapid development of the Internet of Things and mobile Internet, mobility is becoming an increasingly growing topic. In this paper, we consider wireless network localization with the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) measurements which represent the mobility. To solve the maximum likelihood problem with the constraints that are imposed due to both TDOA and FDOA measurements, we adopt the alternating projection onto convex sets for source localization in sensor networks. Leveraging the convex approximation, we propose a convex boundary projection algorithm to estimate the source location. Numerical simulations show that our method is more efficient in distributed manner and the result is close to the Cramer-Rao lower bound performance under noisy measurements. [ABSTRACT FROM AUTHOR]

Due to energy limitation in WSN, any algorithm used in these systems should be energy efficient. Increasing accuracy of localization in Wireless Sensor Networks (WSNs) has been widely studied, without paying attention to energy consumption. In this paper, the accuracy of localization is increased by accurately measuring the distance between the sensors connected to the patients as nodes of a WSN. Limit error in Multiple Input Multiple Output (MIMO) has been calculated by CRB method. Simulation results approve the proposed relation. By using a so called Virtual MIMO (VMIMO) technique, it is shown that both distance estimation error and energy consumption are decreased. Optimum selection of the number of the transmitter and receiver nodes is achieved by the lowest possible energy consumption. Mathematical relation between energy consumption and localization error is presented and then verified by simulation. Using VMIMO decreases power of transmitters and this in turn will result in decreasing destructive effects of electromagnetic waves on patients. [ABSTRACT FROM AUTHOR]

In RSSI-based target localization, estimation error depends on several factors (e.g., location of sensors, target energy). Furthermore, when quantization is involved, localization accuracy largely depends on the number of allocated bits and quantization thresholds used by each sensor. In this paper, we consider the problem of meeting estimation error requirements at different points of interest within the deployment area as best as possible under a bit rate budget constraint. Due to the complexity of the problem, we first address the bit allocation problem by proposing three different sequential allocation algorithms. Simulation results indicate that the error performance of proposed algorithms is within 2% - 20% of that of the optimal solution. Secondly, we propose the ESAQ adaptive scheme that incorporates target parameters and error profile of each sensor in quantization threshold selection. Simulation results show that the ESAQ outperforms both the EDQ and EBHQ thresholding methods found in literature. [ABSTRACT FROM AUTHOR]

In distributed sensor networks (DSNs), localization errors are highly dependent on the relative location of sensors with respect to the target of interest. In this paper, we study the problem of relocating sensors from their initial deployment positions to new positions that would result in meeting estimation requirements at multiple points in a region of interest as best as possible. More specifically, we develop a distributed virtual forces (VF)-based sensor relocation algorithm in which each sensor locally calculates its next location. In the proposed algorithm, VF forces are constructed in such a way that enables fast sensor relocation with the goal of minimizing the difference between achieved and required estimation requirements. The proposed algorithm is scalable and requires minimal information exchange between a sensor and its neighboring sensors. Simulation results are used to verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Adding a Tew shortcuts to a wireless sensor network can construct a small-world network, which not only reduces the average energy expenditure per sensor node, but also the non-uniformity energy expenditure across the nodes. Localization still is a fundamental and essential issue lor the small-world networks. This paper presents a localization algorithm of wireless sensor networks under a shortcut constraint. By constraining the lengths, quantity and directions of the shortcuts, the inconsistency between the small-world network and localization precision can be solved. Analysis, simulation and experimental results demonstrate that the proposed algorithm can decrease the working nodes, guarantee the sparseness of the network topology and improve the estimation accuracy of the node locations. [ABSTRACT FROM AUTHOR]

Mobile robot teams are particularly suited to many application scenarios where infrastructure is unavailable or damaged. For example, mobile robot teams can be useful for exploration in remote regions or search and rescue after a disaster. Localization of individual robots in these teams is essential for enabling many applications or improving the robot's performance in particular tasks. However, in infrastructure-less application scenarios, conventional techniques for localization have many disadvantages such as cost, deployment time, inaccuracy and energy use. Thus, there is a need for a localization scheme that works in infrastructure-less scenarios and is low-cost, quickly deployable and energy-efficient while providing reasonable accuracy for the applications. In this paper, we propose CoCoA, Coordinated Cooperative Ad-Hoc localization. In CoCoA only a subset of the robots in the mobile robot team are equipped with external localization devices (e.g. GPS, or laser rangers with SLAM). Subsequently, while robots perform their tasks, the subset of robots with localization devices help to localize other robots, avoiding the need for static landmarks to be deployed. This is achieved using a modified Bayesian inference-based localization algorithm previously proposed for localization in sensor networks. In addition, CoCoA coordinates this localization process using multicast to put wireless devices in sleep mode periodically which provides significant energy savings. Using detailed simulations and localization models calibrated from experimental data, we find that CoCoA is effective in reducing energy consumption while providing good localization accuracy. [ABSTRACT FROM AUTHOR]

Mobile robot teams are particularly suited to many application scenarios where infrastructure is unavailable or damaged. For example, mobile robot teams can be useful for exploration in remote regions or search and rescue after a disaster. Localization of individual robots in these teams is essential for enabling many applications or improving the robot's performance in particular tasks. However, in infrastructureless application scenarios, conventional techniques for localization have many disadvantages such as cost, deployment time, inaccuracy and energy use. Thus, there is a need for a localization scheme that works in infrastructure-less scenarios and is low-cost, quickly deployable and energy-efficient while providing reasonable accuracy for the applications. In this paper, we propose CoCoA, Coordinated Cooperative Ad-Hoc localization. In CoCoA only a subset of the robots in the mobile robot team are equipped with external localization devices (e.g. GPS, or laser rangers with SLAM). Subsequently, while robots perform their tasks, the subset of robots with localization devices help to localize other robots, avoiding the need for static landmarks to be deployed. This is achieved using a modified Bayesian inference-based localization algorithm previously proposed for localization in sensor networks. In addition, CoCoA coordinates this localization process using multicast to put wireless devices in sleep mode periodically which provides significant energy savings. Using detailed simulations and localization models calibrated from experimental data, we find that CoCoA is effective in reducing energy consumption while providing good localization accuracy. [ABSTRACT FROM AUTHOR]

Technological advances in wearable and integrated sensing devices have witnessed an increase of interest in using Wireless Body Area Network (WBAN) for advanced sensing purposes including monitoring, tracking and controlling systems and states. This paper proposes a WBAN-based control system to assist firefighters combating indoor fires (and other natural disasters) and finding their way to save human lives. To this end, we develop a novel real-time graph, called Temporal MobileWeighted Graph, to integrate special functions such as (relative) localization, safe navigation, continuous communication, and efficient hazard estimation. We design a real time solution aiming at monitoring the firefighter's health, controlling the environment changes, and predicting firefighters' isolation time by estimating the horizon of risk deterioration. A radio communication monitoring module is implemented to assist firefighter's mobility and minimize radio interferences. A set of numerical experiments has been conducted to evaluate the performance of the system. [ABSTRACT FROM AUTHOR]

Localization is the key supporting technology for wireless sensor networks. Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference and the localization performance is seriously affected. In this paper, we focus on the problem of node localization with malicious beacon nodes and solve it based on a repeated game method. Trust evaluation method is introduced to distinguish malicious beacon nodes. A repeated game framework is established and the strategies and payoffs are analyzed. Both malicious and normal nodes are driven by profits to a cooperation state and the whole localization performance is improved. Extensive simulation results show normal nodes can obtain higher location precision based on the repeated game. Malicious beacon nodes prefer to take the long-term cooperation attitude. The total losses to the network are less than the total profits caused by malicious nodes with game control. [ABSTRACT FROM AUTHOR]

Sensor node localization with an acceptable accuracy is one of the most important challenges for location-aware applications of Wireless Sensor Networks (WSNs). The Received Signal Strength (RSS)-based localization techniques are very attractive because of their simplicity and low cost. However, those methods suffer from evident estimation errors. In our previous work, we developed fuzzy set-based localization method, called Fuzzy Ring Overlapping Range Free (FRORF) localization. In this paper we further extend the concept and propose an improved FRORF localization method, IFRORF for short, which uses two-layer fuzzy processing. IFRORF is an area-based localization approach that partitions the localization space into overlapping rings modeled as fuzzy sets with ambiguous boundaries, and then generates fuzzy set of regions by intersecting rings from different fuzzy ring sets. IFRORF estimates the location based on degrees of sensor node membership in regions in the fuzzy set of regions that are additionally tuned using the second layer of fuzzy processing. Simulations demonstrate that IFRORF method outperforms localization accuracy of FRORF. [ABSTRACT FROM AUTHOR]

Location awareness is important for Wireless Sensor Network, since many applications such as environment monitoring, vehicle tracking and event mapping, need the location knowledge of the sensor nodes. This paper presents the design and implementation of an Efficient Received Signal Strength (ERSS) based localization algorithm for indoor wireless sensor network. The limitation of accuracy in RSS based approach is alleviated by one-dimensional Kalman filter and min-max algorithm. The performance of the developed ERSS localization algorithm is validated in real time with the customized sensor nodes. [ABSTRACT FROM AUTHOR]

Sensor node localization with an acceptable accuracy is one of the most important challenges for location-aware applications of Wireless Sensor Networks (WSNs). The Received Signal Strength (RSS)-based localization techniques are very attractive because of their simplicity and low cost. However, those methods suffer from evident estimation errors. In our previous work, we developed fuzzy set-based localization method, called Fuzzy Ring Overlapping Range Free (FRORF) localization. In this paper we further extend the concept and propose an improved FRORF localization method, IFRORF for short, which uses two-layer fuzzy processing. IFRORF is an area-based localization approach that partitions the localization space into overlapping rings modeled as fuzzy sets with ambiguous boundaries, and then generates fuzzy set of regions by intersecting rings from different fuzzy ring sets. IFRORF estimates the location based on degrees of sensor node membership in regions in the fuzzy set of regions that are additionally tuned using the second layer of fuzzy processing. Simulations demonstrate that IFRORF method outperforms localization accuracy of FRORF. [ABSTRACT FROM AUTHOR]

Location awareness is important for Wireless Sensor Network, since many applications such as environment monitoring, vehicle tracking and event mapping, need the location knowledge of the sensor nodes. This paper presents the design and implementation of an Efficient Received Signal Strength (ERSS) based localization algorithm for indoor wireless sensor network. The limitation of accuracy in RSS based approach is alleviated by one-dimensional Kalman filter and min-max algorithm. The performance of the developed ERSS localization algorithm is validated in real time with the customized sensor nodes. [ABSTRACT FROM AUTHOR]

Taking the low-cost into consideration, Received Signal Strength Indicator (RSSI) technology is often used in positioning system. This paper proposes a RSSI localization algorithm which could improve the localization accuracy, coverage and robustness. First, a training process is conducted to build the log-distance path loss model. Then we utilize Bootstrap method to construct the Feasible Solution Set (FSS) for unknown node's coordinate. Finally the estimated coordinate of unknown node is figured out with interval analysis. Furthermore, the algorithm is applied to an indoor positioning system (IPS) developed by ourselves to evaluate its performance. The simulation results show that our algorithm achieves better accuracy, coverage and robustness than other two localization algorithms. And the experimental results show that compared with other four famous IPSs, the IPS using our localization algorithm obtain a better trade-off between accuracy and cost. [ABSTRACT FROM AUTHOR]

The precision and accuracy of range measurement is an important issue for real time localization in wireless sensor networks (WSNs). This paper reports on round-trip time-of-flight (RT-ToF) and received signal strength (RSS) for point to point range estimation using 2.4GHz IEEE 802. 15.4 compliant transceivers. Two main objectives are examined, firstly, the performance limits forToF and RSS based range measurements are compared with the fundamental Cramer-Rao Lower Bound (CRLB). Secondly the range where the error for RSS ranging is expected to be greater then the error for ToF ranging is considered. We term this the 'cross-over' range (CR) of RSS and ToF ranging. Knowledge of this cross-over range can be used to reduce power consumption by disabling the ToF engine and using the RSS within the cross-over range. Performance results and channel parameters have been obtained in outdoor and indoor for the line-of-sight (LOS) and non-line-of-sight (NLOS) environments. Both outdoor and indoor experimental results and analysis are presented. For indoor and outdoor unobstructed (LOS) environment over short range, it is demonstrated that RSS is a good candidate for range estimation at ranges less than 7m. Uncertainty in RSS based range estimation increases with distance and beyond 7m severely limits RSS performance. Further analysis over long range demonstrates that ToF is a good candidate for range estimation at greater than 7m. [ABSTRACT FROM AUTHOR]

Node localization plays an important role in many current and envisioned WSN applications. Most of existing algorithms are not applicable for networks in a concave area. To address this problem, a novel greedy optimization localization algorithm (GOLA) is proposed. The key of GOLA is the design of neighborhood function which only uses distances between neighbor nodes to generate a new set of estimated locations from an old one, two correction operations are used to alleviate the flip ambiguity. Furthermore, a method of initial solution generation is given. Both rangebased and range-free localization can use GOLA to estimate locations of nodes if distances between neighbor nodes are measured by hardware or estimated by distance estimation algorithms. Simulation results indicate that GOLA can achieve more accurate and reliable localization results in a concave area. [ABSTRACT FROM AUTHOR]

Location is one of the most essential contexts for manyWireless Sensor Network (WSN) applications. Despite its potentiality in mobile beacon assisted localization, Unmanned Aerial Vehicle (UAV) still suffers from considerable performance degradation due to RF irregularity in 3D space and the long distance environment. In this study, we design and implement an aerial beacon assisted localization system, named ABAL, which is based on the RSS maxima generated by straight line movement of a common omnidirectional antenna with a very narrow radiation angle in vertical plane. ABAL takes advantage of the narrow vertical radiation angle of antenna to enhance the recognizability of RSS maxima, and calculates sensor location using the perpendicular relationship between the line from sensor node to RSS maxima and the straight line, tolerating RF irregularity and enabling effective localization in long distance environment. Instead of simulation, we developed a prototype system and conducted comprehensive experiments in real flights. The evaluation results show that an impressive improvement of 30%~150% over RSS ranging based approach can be achieved with the minimum mean absolute error (1.8 m) close to the error of high-quality single point GPS (1.5 m). [ABSTRACT FROM AUTHOR]

In location-based routing, forwarding decisions depend on correct location information of the nodes. The existing location verification schemes suffer from high computational requirements and the need for additional equipment, which may limit their applicability. In this work, the Transitive Location Verification algorithm is proposed, which offers location verification along with good utilization of network resources. It is designed to eliminate the requirements that limit the applicability of location verification in WSNs, such as additional expensive hardware and the use of special nodes. Therefore, any node can play the role of the verifier without any impact on its normal operation; though with a slight overhead. As a result, the verifier nodes, and their locations, will differ with time such that a malicious node will not be able to fake its location. Two schemes are proposed for verification transition within the network: One-directional transition and Three-directional transition. The simulation results demonstrated that the proposed algorithm achieves high performance in terms of the percentage of nodes verified, the number of rounds, and the number of times the verification functionality is transferred. [ABSTRACT FROM AUTHOR]

In location-based routing, forwarding decisions depend on correct location information of the nodes. The existing location verification schemes suffer from high computational requirements and the need for additional equipment, which may limit their applicability. In this work, the Transitive Location Verification algorithm is proposed, which offers location verification along with good utilization of network resources. It is designed to eliminate the requirements that limit the applicability of location verification in WSNs, such as additional expensive hardware and the use of special nodes. Therefore, any node can play the role of the verifier without any impact on its normal operation; though with a slight overhead. As a result, the verifier nodes, and their locations, will differ with time such that a malicious node will not be able to fake its location. Two schemes are proposed for verification transition within the network: One-directional transition and Three-directional transition. The simulation results demonstrated that the proposed algorithm achieves high performance in terms of the percentage of nodes verified, the number of rounds, and the number of times the verification functionality is transferred. [ABSTRACT FROM AUTHOR]

RFID technologies are gaining much attention as they are attractive solutions to many applications. Localization based on active RFID technologies provides a much needed added-value to further expand the application domain. LANDMARC was the first attempt using active RFID for indoor location sensing with satisfactory results. However, the LANDMARC approach suffers from two drawbacks. First, it does not work well in a closed area with severe radio signal multi-path effects. Second, to further improve the localization accuracy, more reference tags are required which is costly and may trigger the RF interference phenomenon. The proposed VIRE approach can overcome the above drawbacks without any additional cost. Based on the concept of virtual reference tags, a proximity map is maintained by each reader. An elimination algorithm is used to eliminate those unlikely locations to reduce the estimation error. Our experimental results show that the new method consistently enhances the precision of indoor localization from 17 to 73 percent over the LANDMARC approach at different tag locations in different environments. [ABSTRACT FROM AUTHOR]

Compass, face and geographical routing, for ad hoc and wireless sensor networks, rely on nodes knowing their geographic location and locations of other nodes. For location-unaware nodes without self-positiong devices (e.g., GPS), Garcia-Alfaro et al. proposed location determination algorithms leveraging location reports from neighbors. The evil ring is an attack on the location determination algorithms of Garcia-Alfaro et al. When inquired, an attacker returns a fake location sitting on a circle centered at the victim's location and with radius equal to the attacker-victim separation distance. The calculation of the distance between the victim and the attacker is not affected. A locationunaware node correctly determines its location. The attack, however, misleads such as node in getting and using wrong locations for its malicious neighbors. We introduce and analyze an evil ring attack detection algorithm. Location-unaware nodes cross-check the consistency of the information collected from neighbors. Neighbors perpetrating the evil ring attack are uncovered. O(n) messages are sufficient to localize all location-unaware nodes, to detect all liars, and to construct valid neighbor tables. Simulation results demonstrate that our algorithm, named Cross- Check, outperforms the Garcia-Alfaro et al. algorithm Majority-Three NeighborSignals. We compared the percentage of location-unaware nodes that successfully derive valid neighbor tables. Simulations were conducted under equivalent topology conditions, varying the percentage of evil ring attack perpetrators. [ABSTRACT FROM AUTHOR]