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Detecting pollutants and/or odorants is a challenge in environmental monitoring. Wireless sensor network can sense and gather data pertaining to different environmental parameters. However, data collected by wireless sensor network has no significance if the location of data is unknown. Thus, localization is important in wireless sensor network used in environmental monitoring. Range-free localization like DV-Hop is preferred in many applications due to its simplicity and low cost. However, nodes in wireless sensor network are vulnerable to many attacks. Without proper measure the localization may be affected once nodes are compromised. A location-aware application may fail with such faulty localization. This paper gives a simple but secured localization for environmental monitoring on the top of DV-Hop algorithm utilizing fuzzy logic. Results obtained from simulation demand that the proposed algorithm can prevent attacks in range-free localization. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do. [ABSTRACT FROM AUTHOR]

Localization in wireless sensor networks (WSNs) is required to examine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. The anchor nodes positions are known as the nodes that have GPS unit incorporated with them. All sensor nodes are generally not configured with GPS as it is not suitable for indoor environments and/or underwater areas. A network becomes more expensive and utilizes more energy if all nodes are equipped with GPS that is a major drawback of WSNs. Various localization schemes have been proposed in literature, while most research proposals deal with the study of 2D applications. However, in the 3D applications, the area under observation may have a complexity in the sensing environment. An optimized algorithm is required for the determination of node location in 3D environment. In this paper, we propose an adaptive flower pollination algorithm (AFPA) with enhanced exploration and exploitation capabilities of conventional FPA for the localization of sensor nodes in WSN. To test the performance of AFPA, benchmark functions (CEC 2019) are used to compare it with other meta-heuristics. The results show that proposed AFPA outperforms in terms of convergence speed and provides better results for most of the benchmark functions. Also, the proposed AFPA is tested on WSN Localization problem, it provides least localization error in comparison to other techniques applied in 3D WSN environments. [ABSTRACT FROM AUTHOR]

Localization of sensor nodes is one of the major challenge in wireless sensor networks. It helps in identifying the geographically area where an event occurred. Most of the cases it is one time process wherein nodes are localized once in network life time. However, in real time scenario nodes may get dislocated within the network due to land slide or any event occurrence. In such events, localization of these sensor nodes is required. In this paper, we have proposed a new framework of localization for displaced sensor nodes. The proposed framework consider DV-Hop localization methods using particle swarm optimization (PSO). Radio irregularity model is also considered to show the applicability of proposed algorithm in anisotropic network. Simulation results show that our proposed localization framework effectively minimizes the elapsed time, localization error and energy consumption as compared to traditional framework of PSO DV-Hop. [ABSTRACT FROM AUTHOR]

Analysis of errors in computing locations of nodes in a wireless sensor network (WSN) is an important task. In this paper, we analyse the error of localization of sensors in a WSN. We analyse the effect of errors introduced in the location of sensors and the measurement of their distances to anchors. During our analysis, we focus on the error and normalized error. We analyse how the error and normalized error vary with errors in locations and transmission ranges of anchors. The localization error is normalized with respect to either coordinates of a node or transmission range of nodes in the network. [ABSTRACT FROM AUTHOR]

In a Wireless Sensor Network (WSN), system resources, such as the total node energy, need to be utilized effectively while executing the task of interest. Since a number of sensor nodes may be needed in various type of applications, it is also important to develop each sensor node in a cost effective manner. The purpose of this paper is to develop a custom made, low power, cost-effective wireless sensor node based on a MSP430G2553 microprocessor and nRf24l01+ wireless transceiver module. Then, using these nodes, we form a simple WSN testbed, and locate the position of a light emitting source. [ABSTRACT FROM AUTHOR]

Highlights • The proposed method alternately calculates the position and times of the source node that is more computationally efficient. • The convergence behavior of the proposed method is further theoretically proved. • First-order perturbation analysis shows that the performance of the proposed method can approximately reach the CRLB. • Compared with some previous methods, the proposed method requires fewer anchor nodes and less communication cost. Abstract This paper efficiently solves the problem of joint localization and synchronization in wireless sensor networks by using two-way message exchange mechanism. Employing an affine model of unsynchronized local clocks, we first establish the famous Maximum Likelihood (ML) estimator for joint localization and synchronization, which derives the best consistent solution. Unfortunately, achieving directly the ML estimator is usually difficult because the ML cost functions are highly non-linear and non-convex. In order to effectively obtain the ML estimator, a novel bi-iterative algorithm is proposed by alternately searching the sensor node location and clock parameters (clock skew and clock offset). We prove that the proposed algorithm is convergent, and analyze that its estimation accuracy for the sensor node locations and clock parameters can approximately reach the Cramer–Rao Lower Bound (CRLB) under the mild noise condition. Compared with some previous methods, the proposed bi-iterative algorithm is more computationally efficient, and takes fewer anchor nodes and less communication overhead. Simulation results verify theoretical analysis and show that the proposed algorithm has the better performance than the several existing algorithms. [ABSTRACT FROM AUTHOR]

Localization is one of the most important challenges of wireless sensor networks because the location information is typically used in other domains such as coverage, deployment, routing, and target tracking. There exist some localization algorithms that facilitate the sensor nodes to locate itself using the mobile anchor node position. Some crucial attempts have been made in the past for optimizing the mobile anchor node trajectory with good accuracy. This paper presents a novel path planning scheme, D-connect, which ensures the localization of all the sensor nodes with minimum trajectory length. The performance of the proposed scheme is evaluated through a series of simulations. Experimental results reveal that the shortest path for traversing the whole area can be traced with the minimum localization error using this method. It also shows that D-connect outperforms the existing methods in terms of the anchor node trajectory length as well as the localization error. [ABSTRACT FROM AUTHOR]

Both localization and synchronization of mobile nodes are important for wireless networks. In this paper, we propose new methods for network localization and synchronization (NLS) using full-duplex radios through only two frames of transmission. Specifically, all nodes simultaneously transmit their signature signals in the first frame, while receiving others’ signals via full-duplex radios. In the second frame, nodes transmit either scrambled versions of their received signals in the first frame or a digital packet of the channel parameter estimates of the received signals. We develop distributed algorithms to estimate the arrival times of different components in the received signals. These arrival times are then used to determine the local network geometry and clock offsets. The Cramér-Rao lower bounds for internode distances and clock offsets are derived, and the former can be translated into error bounds of the node positions. Compared with conventional frequency division duplex or time-division duplex, we demonstrate the high efficiency of NLS using full-duplex radios, revealing its potential beyond data communications in future wireless networks. [ABSTRACT FROM AUTHOR]

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. [ABSTRACT FROM AUTHOR]

In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireless localization system. Building upon the concept of the restricted Bayesian approach, a generalized optimization strategy, called the restricted scheme, is proposed for power allocation of jammer nodes, and its theoretical properties are characterized. In the restricted scheme, the aim is to maximize the average Cramer-Rao lower bound (CRLB) of target nodes while keeping their minimum CRLB above a predefined level in the presence of average (total) and peak power constraints. It is proved that the average CRLB achieved by the restricted scheme is a strictly decreasing and concave function of the constraint on the minimum CRLB level. A closed-form solution is obtained for the restricted scheme when the tradeoff parameter and the total power limit are below certain thresholds. In addition, it is shown that the optimal solution of the restricted scheme corresponds to the use of at most NT jammer nodes, where NT is the number of target nodes, and that the optimal solution of the minimum CRLB maximization scheme is determined by at most NJ target nodes, where NJ is the number of jammer nodes. Extensions of the restricted scheme and an alternative scheme that aims to maximize the number of disabled target nodes (whose CRLBs are above a preset level) are considered, and the corresponding optimal strategies for jammer power allocation are identified. Numerical examples are provided to verify the theoretical derivations for various network configurations. [ABSTRACT FROM AUTHOR]

In many applications of wireless sensor network, the position of the sensor node is useful to identify the actuating response of the environment. The main idea of the proposed localization scheme is similar with most of the existing localization schemes, where a mobile beacon with global positioning system broadcast its current location coordinate periodically. The received information of the coordinates help other unknown nodes to localize themselves. In this paper, we proposed a localization scheme using mobile beacon points based on analytical geometry. Sensor node initially choose two distant beacon points, in-order to minimize its residence area. Later using the residence area, sensor node approximate the radius and half length of the chord with reference to one of the distant beacon point. Then the radius and half length of the chord are used to estimate the sagitta of an arc. Later, sensor node estimate its position using radius, half length of the chord, and sagitta of an arc. Simulation result shows the performance evaluation of our proposed scheme on various trajectories of mobile beacon such as CIRCLE, SPIRAL, S-CURVE, and HILBERT. [ABSTRACT FROM AUTHOR]

To localize the wireless sensor networks nodes, only the hop-based information (i.e., hops’ number, average hop size, and so on) has been, so far, exploited by range-free techniques, with poor-accuracy, however. In this paper, we show that localization accuracy may greatly benefit from joint exploitation, at no cost, of the information already provided by the forwarding nodes (i.e., relays) between each anchor (i.e., position aware) and sensor nodes pair. As such, we develop a novel range-free localization algorithm, derive its average location estimation error (LEE) in closed-form, and compare it in LEE performance with the best representative algorithms in the literature. We show that the proposed algorithm outperforms them in accuracy. In contrast to the latter, we further prove that it is able to achieve an LEE average and variance of about 0 when the number of sensors is large enough, thereby achieving an unprecedented accuracy performance among range-free techniques. [ABSTRACT FROM PUBLISHER]

One of the main parameters in wireless sensor networks (WSNs) is the design of energy-efficient protocols. And accuracy is another central goal of localization. Since sensor nodes run on battery power, any WSN application and accurate localization needs to be energy-efficient. In this paper, the accuracy of localization is increased by accurate measurement of the distance between the mobile sensors. Limit error in multiple-input multiple-output (MIMO) has been calculated by CRB method. Virtual MIMO (VMIMO) technique can obtain better localization precision and the localization is energy-efficient. Optimum selection of the number of the transceiver nodes is obtained by the lowest possible energy consumption, the existent localization error, and speed of nodes. Mathematical relation between energy consumption and localization of mobile nodes is presented and then verified by simulation. VMIMO decreases power of transmitters and this in turn will result in decreasing destructive effects of electromagnetic sensitivity (EMS) on body. Furthermore, optimized localization parameters will increase the efficiency of the system and network lifetime. [ABSTRACT FROM AUTHOR]

Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly being used in remote environment monitoring, security surveillance, military applications, and health monitoring systems among many other applications. Designing efficient localization techniques have been a major obstacle towards the deployment of WSN for these applications. In this paper, we present a novel lightweight iterative positioning (LIP) algorithm for next generation of wireless sensor networks, where we propose to resolve the localization problem through the following two phases: (1) initial position estimation and (2) iterative refinement. In the initial position estimation phase, instead of flooding the network with beacon messages, we propose to limit the propagation of the messages by using a random time-to-live for the majority of the beacon nodes. In the second phase of the algorithm, the nodes select random waiting periods for correcting their position estimates based on the information received from neighbouring nodes. We propose the use of Weighted Moving Average when the nodes have received multiple position corrections from a neighbouring node in order to emphasize the corrections with a high confidence. In addition, in the refinement phase, the algorithm employs low duty-cycling for the nodes that have low confidence in their position estimates, with the goal of reducing their impact on localization of neighbouring nodes and preserving their energy. Our simulation results indicate that LIP is not only scalable, but it is also capable of providing localization accuracy comparable to the Robust Positioning Algorithm, while significantly reducing the number of messages exchanged, and achieving energy savings. [ABSTRACT FROM AUTHOR]

Area-based localization algorithms use only the position of some reference nodes, called anchors, to estimate the residence area of the remaining nodes. Existing algorithms use a triangle, a ring or a circle as the geometric shape that defines the node’s residence area. However, existing algorithms suffer from two major problems: (1) in some cases, they might make wrong decisions about a node presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error and high ratio of localizable nodes. In this paper, we overcome these shortcomings by introducing a new approach for determining the node’s residence area that is geometrically shaped as a half-symmetric lens. A novel half symmetric lens based localization algorithm (HSL) is proposed. HSL yields smaller residence areas, and consequently, better location accuracy than contemporary schemes. HSL further employs Voronoi diagram in order to boost the percentage of localizable nodes. The performance of HSL is validated through mathematical analysis, extensive simulations experiments and prototype implementation. The validation results confirm that HSL achieves better location accuracy and higher ratio of localizable nodes compared to competing algorithms. [ABSTRACT FROM AUTHOR]

Many indoor localization studies are now actively underway. Generally, the indoor localization algorithms are based on the IEEE 802.15.4 wireless sensor networks. However, because of the sensor nodes' limitations, their radios are more susceptible to noise and interference than other wireless technologies. These are critical factors in the rigorous indoor environment, whose effects may not be the same on all channels of a sensor network. Many buildings are equipped with IEEE 802.11 access point (AP) to provide internet service. IEEE 802.11 AP can have a serious impact on the IEEE 802.15.4 performance if the channel allocation is not carefully taken into account. In this paper, we analyze the effect of IEEE 802.11 AP on location errors in received signal strength indicator (RSSI)-based indoor localization system with IEEE 802.15.4. We performed several tests in a house in order to minimize the influence of other IEEE 802.11 APs. We performed transmitting/receiving test by varying the location of IEEE 802.11 AP. Also, we composed an indoor localization system, and measured the location error of mobile node by varying the location of IEEE 802.11 AP. Our study shows that co-channel interference due to use IEEE 802.11 AP affects the RSSI value of sensor node. Ultimately, it has a harmful influence on location error. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), the use of the same set of measurement data for simultaneous localization and synchronization is potentially useful for achieving higher estimation accuracy, and lower communication overhead and power consumption. In this paper, we first analyze the impact of asynchronous sensor nodes (SNs) on the accuracy of time-based localization schemes, and the impact of inaccurate SN location information on the accuracy of synchronization based on packet delay measurement, to illustrate the necessity and significance of simultaneous localization and synchronization of SNs. We then consider the joint localization and synchronization problem for two cases. In the first case, we assume that the beacon information is perfectly known. The Maximum Likelihood (ML) estimator is first formulated, which is computationally expensive. A new closed-form Joint Localization and Synchronization I (JLS-I) estimator is then proposed to provide a computationally efficient solution. In the second case, we assume that the beacon locations and timings are known inaccurately, and develop the ML and JLS-II estimators accordingly. JLS-II is based on Weighted Least Square and Generalized Total Least Square, and is of low complexity. The Cramer-Rao Lower Bounds (CRLBs) and the analytical Mean Square Errors of the proposed estimators are derived, and we also analytically show that JLS-I can achieve the corresponding CRLB. Simulation results demonstrate the effectiveness of the proposed estimators compared to other approaches. With only a three-way message exchange, JLS-I can attain the CRLB and JLS-II can provide close to optimal performance in their respective scenarios. They are also robust against the Geometric Dilution of Precision problem, and outperform existing algorithms in NLOS scenarios. Our results demonstrate the advantages of JLS-I and JLS-II in reduced computational complexity with lower power consumption and communication cost while achieving high estimation accuracy. They are therefore attractive solutions to the simultaneous localization and synchronization problem in WSNs where energy and network resources are the most important considerations. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, location awareness along with the sensed data is important for most of the applications. Although there are many localization techniques in the literature, some of these techniques involve tiny sensor nodes, which need to be computationally powerful and complex in hardware to estimate their locations. This paper proposes a modified sectoral sweeper-based localization estimation (M-SSLE) method, which is the improved version of our previously studied SSLE method, to achieve position information of sensor nodes. This technique requires only a central node having a smart antenna capability to estimate locations of sensor nodes and simplified message formats. Using the SSLE technique, the computational burden for location estimation is carried out by a central node and no hardware or software modification to tiny sensor devices is required. The M-SSLE is implemented using an OPNET modeler for realistic fading channel conditions. The detailed implementation methodology in OPNET is presented in terms of node and process models. The performance of the M-SSLE method is evaluated through different network scenarios and channel parameters in terms of localization error, average throughput, and node energy consumption. Furthermore, its localization performance is compared with the SSLE and Cramer Rao Bound methods in the two different scenarios for the deployment of the sensor nodes. The M-SSLE average localization errors of 4.3 and 8.9 m are demonstrated under the log-distance and the log-normal shadowing channel conditions in a 250 m x 250 m area, respectively. [ABSTRACT FROM AUTHOR]

Transceiver-free object tracking is to trace a moving object that does not carry any communication device in an environment with some monitoring nodes predeployed. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So, the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result, it can reach the tracking accuracy to around 1 m by just using three nodes in a triangle area with 4 m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26 m. Our system scales well to a large deployment field without sacrificing the latency and accuracy. [ABSTRACT FROM AUTHOR]

The knowledge of sensors' locations is crucial information for many applications in Wireless Sensor Networks (WSNs). When sensor nodes are deployed in hostile environments, the localization schemes are vulnerable to various attacks, e.g., wormhole attack, pollution attack, range enlargement/reduction attack, and etc. Therefore, sensors' locations are not trustworthy and need to be verified before they can be used by location-based applications. Previous verification schemes either require group-based deployment knowledge of the sensor field, or depend on expensive or dedicated hardware, thus they cannot be used for low-cost sensor networks. In this paper, we propose a lightweight location verification system that performs both “on-spot” and “in-region” location verifications. The on-spot verification intends to verify whether the locations claimed by sensors are far from their true spots beyond a certain distance. We propose two algorithms that detect abnormal locations by exploring the inconsistencies between sensors' claimed locations and their neighborhood observations. The in-region verification verifies whether a sensor is inside an application-specific verification region. Compared to on-spot verification, the in-region verification is tolerable to large errors as long as the locations of sensors don't cause the application to malfunction. We study how to derive the verification region for different applications and design a probabilistic algorithm to compute in-region confidence for each sensor. Experiment results show that our on-spot and in-region algorithms can verify sensors' locations with high detection rate and low false positive rate. They are robust in the presence of malicious attacks that are launched during the verification process. Moreover, compared with previous verification schemes, our algorithms are effective and lightweight because they do not rely on the knowledge of deployment of sensors, and they don't require expensive or dedicated hardware, so our algorithms can be used in any low-cost sensor networks. [ABSTRACT FROM AUTHOR]

Abstract: In wireless sensor networks (WSNs), irregularly deployed nodes can significantly degrade the performance of the localization system. In this paper, we propose a novel localization scheme for the irregularly deployed WSNs. The basic approach is to control the transmission of location messages by using the fuzzy c-means (FCM) clustering algorithm. Next, each node selects its localization method according to the node density. Simulation studies show that the proposed approach can enhance the localization accuracy, while reducing the retransmission messages in the irregularly deployed WSNs. [Copyright &y& Elsevier]

In this paper, we analyze the problem of acoustic ranging between sensor nodes in an underwater environment. The underwater medium is assumed to be composed of multiple isogradient sound speed profile (SSP) layers where in each layer the sound speed is linearly related to the depth. Furthermore, each sensor node is able to measure its depth and can exchange this information with other nodes. Under these assumptions, we first show how the problem of underwater localization can be converted to the traditional range-based terrestrial localization problem when the depth information of the nodes is known a priori. Second, we relate the pair-wise time of flight (ToF) measurements between the nodes to their positions. Next, based on this relation, we propose a novel ranging algorithm for an underwater medium. The proposed ranging algorithm considers reflections from the seabed and sea surface. We will show that even without any reflections, the transmitted signal may travel through more than one path between two given nodes. The proposed algorithm analyzes them and selects the fastest one (first arrival path) based on the measured ToF and the nodes' depth measurements. Finally, in order to evaluate the performance of the proposed algorithm we run several simulations and compare the results with other existing algorithms. [ABSTRACT FROM AUTHOR]

Existing localization approaches are divided into two groups: 1) range based and 2) range free. The range-free schemes often suffer from poor accuracy and low scalability, whereas the range-based localization approaches heavily depend on extra hardware capabilities or on the absolute received signal strength indicator (RSSI) values, which is far from practical. In this paper, we propose a mobile-assisted localization scheme called Perpendicular Intersection (PI), setting up a delicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of a perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosB motes and evaluated PI in both indoor and outdoor environments. Through comprehensive experiments, we show that PI achieves high accuracy, significantly outperforming the existing range-based and the mobile-assisted localization schemes. [ABSTRACT FROM PUBLISHER]

Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization. [ABSTRACT FROM AUTHOR]

Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system availability in case of a sensor failure of a five-node Time Difference of Arrival (TDOA) localization method. We solve the ambiguity of two possible solutions in the four-sensor TDOA problem in each combination of four nodes of the system by maximizing the distance between the two possible solutions in every target possible location. In addition, we perform a Genetic Algorithm Optimization in order to find an optimized node location with a trade-off between the system behavior under failure and its normal operating condition by means of the Cramer Rao Lower Bound derivation in each possible target location. Results show that the optimization considering sensor failure enhances the average values of the convergence region size and the location accuracy by 31% and 22%, respectively, in case of some malfunction sensors regarding to the non-failure optimization, only suffering a reduction in accuracy of less than 5% under normal operating conditions. [ABSTRACT FROM AUTHOR]

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations. [ABSTRACT FROM AUTHOR]

Pipeline failures cause significant environmental and property damage and fatalities every year. Current methods have a lack of sensitivity to pinpoint the leak location with sufficient accuracy when the leak is at the stable regime. The acoustic emission (AE) method relies on propagating elastic waves caused by the leak turbulence. The current AE leak detection methodology relies on two neighbor sensors, and requires either a long wiring system or a wireless system with clock synchronization, which raises many other challenges. In this paper, the new leak localization approach is proposed to detect the leak from a single point measurement with two sensors designed to be sensitive to the wave motions generated in pipes at two orthogonal directions (radial and axial). The approach is demonstrated on a laboratory-scale steel pipe in comparison with the conventional two-neighbor sensor approach. The result indicates that the proposed localization approach successfully pinpoints the leak location with good accuracy, which enables the implementation of single-point wireless acoustic emission sensing in long-range pipeline networks. [ABSTRACT FROM AUTHOR]