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Wireless Sensor Networks are a collection of nodes which contain tiny devices having low power and work with minimal cost. In such a network, the functioning of these nodes plays a very important role to sense data and also forward the sensed data to the target observer. For segregation of nodes in effective locations, there is a need to localize these nodes by combining location information with the sensed data for tracking and monitoring of malicious nodes, goods tracking etc. The various approaches towards localization may be classified as range-free or range-based, both having their own pros and cons. This paper presents a discussion on different localization approaches, followed by simulation results of localization using lognormal shadowing model for distance estimation and trilateration for location computation. The results depict the individual impacts of node density and area of sensed region on the localization error. It is observed that with a fixed node count, the errors in location estimates increase with the increase in node density. It is also depicted that the localization error increases towards the boundary of a network. Since the approach discussed in the paper uses loss in received power for distance computation, there is no cost overhead for additional hardware involved in the implementation. Thus the approach used is cost effective. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are networks consisting of many sensors, each of which acquires data and communicates with each other through wireless equipment in time. To make the data obtained by each sensor node meaningful, the precise localization technology of WSNs should be investigated. As an easy-to-implement localization algorithm, DV-Hop has been studied by many researchers. But its localization accuracy needs to be further improved. In this paper, an improved DV-Hop localization algorithm (2DHYP-GA DV-Hop) is proposed, which combines the 2D hyperbolic localization algorithm and an improved adaptive genetic algorithm (IAGA) to estimate the unknown node coordinates, and improves the localization accuracy. In addition, the radio irregularity model is considered in this paper to evaluate the proposed algorithm in anisotropic networks. Simulation results show that the accuracy of the proposed algorithm is 15.9%, 11.1%, and 7.6% than the GA DV-Hop, the PSO DV-Hop, and the IAGA DV-Hop, respectively. The stability of our proposed algorithm is 11.3%, 26.5%, and 16.6% higher than the GA DV-Hop, the PSO DV-Hop, and the IAGA DV-Hop, respectively, and the convergence speed is also the best. [ABSTRACT FROM AUTHOR]

The localization problem in wireless sensor networks (WSN) has recently received justifiable interest from researchers. Various optimization/learning algorithms are used to determine the accurate node localization. The main aim of this paper is to develop a novel Optimized Localization Learning Algorithm (OLLA) and analyze it with well-noticed localization-based learning algorithms, namely Approximate Point in Triangle (APIT), Localization Algorithm using Expected hop Progress (LAEP), Randomized Approximate Nearest Neighbors (RANN) and Standard-based Particle Swarm Optimization (SPSO). The performance is investigated based on indoor and outdoor scenarios, on specific parameters: absolute localization error, relative localization error, root mean square error, and probability distribution on various anchor nodes. Simulation results show that the proposed learning algorithm OLLA performs well in both scenarios. Besides this, numerous localization-based learning algorithms are discussed, and comparative analyses of well-known learning algorithms with merits and limitations are also presented. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks, node localization is one of the most important system parameters. Determining the exact position of nodes in these networks is one of vital and tedious tasks. This paper presents a review of the most localization methods which optimize the localization error. It provides a new taxonomy of techniques used in this field, including Mobile Anchor, Machine Learning, Matematical Models and Meta-heuristics. In this later, we survey its different algorithms such as Genetic Algorithm, Particle Swarm optimization, Ant Colony Optimization, BAT optimization algorithm, Firefly Optimization Algorithm, Flower Pollination Algorithm, Grey Wolf Optimization algorithm, Artificial Bees Colony Optimization Algorithm, Fish Swarm Optimization Algorithm and others. Further, the comparison between these metaheuristics algorithms based localization optimization is done. Finally, a comprehensive discussion of the performance parameters such as accuracy, convergence rate, energy consumption and the number of localized nodes is given. [ABSTRACT FROM AUTHOR]

The underwater sensor networks (UWSNs) exhibit different characteristics from terrestrial WSNs. To make the sensed data meaningful, one of basic task is the localization of sensor nodes. In UWSNs, this is not feasible to use global positioning system due to its issue of propagation. This senses localization as a fundamental as well as sound issue in the UWSNs. In spite of continuous and sincere research effort, the time synchronization between the sensor nodes for their localization is also a tough job. To address these issues, a localization scheme free from the concept of time synchronization for large scale 3D-UWSNs based on dive and rise mobile beacons floating over the sea surface has been proposed in the given paper. The analytical analysis of the proposed scheme is also presented in the paper. In the simulation section, the performance of the proposed scheme is evaluated in terms of coverage and the number of localized nodes. A discussion based on various obtained results is also given in the paper. [ABSTRACT FROM AUTHOR]

Beamforming is one of the vibrant emerging techniques for the next generation of wireless communication. It has many applications including localization of objects in indoor and outdoor environment, radar, D2D communication, vehicular networks, astronomy, and biomedical instruments. The main requirements of beamforming are the antenna with narrow beamwidth and a network for steering the beam in the desired direction. In this paper, a Quad-element microstrip array antenna with H-type feeding and corporate feeding are proposed as beamforming antennas to locate the user equipment in indoor WiFi, and WLAN environment. The performance metrics such as, voltage standing wave ratio (VSWR), return loss and gain are also evaluated for the variety of arrangement of array elements with the single element microstrip antenna. The test result shows that the proposed array antenna provides unidirectional at 2.26 and 6.35 GHz. It measures a VSWR of less than 2 dB which is desirable. Moreover, it gives a return loss of −12.63 dB at 6.54 GHz and −16.21 dB at 6.63 GHz. It shows that designed antenna is suitable for beamforming for user equipment localization at indoor environment. [ABSTRACT FROM AUTHOR]

Due to the rising number of aged people in the last decades, many apprehensions about their quality of life and their personal security in their own homes came to surface especially when these persons prefer to live alone. Compared to other age groups, old persons are more susceptive to domestic incidents, such as sudden illnesses and falls. Therefore, using new technologies in elderly homecare became one of the most urgent issues to study. These technologies are classified into three categories: real time vital sign monitoring, activity recognition and indoor localization. In this paper we present our work in indoor positioning which is the combination of three existing technologies in a more effective, precise and accurate way: Bluetooth Low Energy, Acoustic and Light Fidelity (LiFi). Our main contribution is the proposition of new "dilution of precision" (DOP) indicators as precision weights of the results given by each technology. Depending on these new DOP-like indicators, we were able to judge how much precise and accurate the returned results are. The experiments we conducted proved that precision is much better when using these new precision metrics. [ABSTRACT FROM AUTHOR]

The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. [ABSTRACT FROM AUTHOR]

Obtaining localization information is tedious as it adopts the characteristics of the environment in which the sensors are positioned—many techniques for localization attempt to solve localization issues in the wireless sensor networks (WSNs). Empirical limits exist on the amalgamation of existing techniques and a minimum number of anchor nodes that can be positioned in such scenarios. The restricted capacity to communicate capacity can levy a consequential limit on the data that can be processed. Moreover, this node position information is useful for routing techniques/some application-specific tasks. Estimations in anchor node placements also affect the localization accuracy. To overcome those problems, in this paper, we propose an efficient optimal localization technique (EOLT) for WSN using hybrid machine learning algorithms. The first contribution of the proposed EOLT mechanism is to introduce the artificial algae optimization algorithm for cluster formation based on the multiple constraints that calculate the location of unknown nodes. Second, we present the Hunting search-based decision-making algorithm for the cluster head (CH) computation process. The computed CH utilizes the destination address to address location information, allowing for the automatic construction of a routing path to reach the CH without the need for routing discovery. Moreover, the proposed EOLT mechanism uses a backtrack search optimization algorithm for congestion prediction and optimal links to manage user mobility. Finally, to check the efficacy of EOLT, it has been compared with an intelligent mechanism for routing data based on nodes localization (IMRL), efficient video communication for wireless multimedia sensor networks (MEVI), Power efficient range-free localization algorithm for WSNs (PERLA) routing protocols on the anvil of various performance parameters including energy consumption, throughput, packet delivery rate, death rate, and network lifetime. The EOLT mechanism is implemented in the Network Simulator tool, and the result of the EOLT shows that its performance is better in terms of the performance parameters considered compared to other routing protocols. Significantly, EOLT demonstrates concurrent enhancements of 60%, 34%, 25.6%, 45.6%, and 39.34% in energy consumption, throughput, delay, packet delivery rate, and network lifetime, respectively, when the number of sensor nodes varies, as compared to IMRL. In contrast, IMRL surpasses the other two protocols under consideration, namely MEVI and PERLA. Similarly, when the number of rounds varies, EOLT achieves improvements of 25%, 24.6%, 35%, 32.1%, and 29.12% for the mentioned performance parameters in the same order. [ABSTRACT FROM AUTHOR]

Sensor node localization is an important feature of many applications, including wireless sensor networks and location-based services. The accurate localization of sensor nodes improves system performance and reliability. This research emphasizes the benefits of using hybrid machine learning and optimization strategies for sensor node localization. Machine Learning (ML) algorithms, such as neural networks and support vector machines, are used to simulate complex correlations between sensor readings and related locations. These models enable precise prediction of node placements based on received signal strength, time of arrival, or other sensory inputs. The survey conducted in this study aims to uncover the latest advancements in localization strategies within Wireless Sensor Networks through the utilization of ML and Optimization Techniques. By thoroughly examining the existing literature, research gaps have been identified when localization techniques are solely employed. To provide a comprehensive understanding, this survey offers a detailed classification of localization algorithms, covering various aspects. Furthermore, the paper elaborates on the implementation of Optimization and Machine Learning approaches, exploring potential combinations with localization techniques. Through the use of analytical tables, the survey presents a comprehensive overview of sensor node localization using ML and optimized approaches. Additionally, the study addresses the challenges encountered and identifies potential future directions for the integration of these techniques. [ABSTRACT FROM AUTHOR]

EEG is gaining recognition in the field of real-time applications. However, the EEG inverse problem leads to poor spatial resolution in brain source localization. This paper presents an overview of the existing EEG inverse solution methods. Further, a comparative analysis of recent techniques has been presented. This work discusses the challenges associated with the existing source reconstruction algorithms. The main focus is on the recent reports in this field that have combined the EEG denoising in the pre-processing phase along with the inverse localization approaches. Out of various existing techniques, SLORETA offers better localization results but its noise sensitivity is very high. It has been validated in a comparative analysis for simulated dipole sources with no noise. To illustrate the advantage of using pre-processed data with inverse localization, the classification accuracy of conventional methods has been compared. The accuracy has been analyzed for depression signals using the Naïve Bayes, RF, and SVM classifiers. The VMD- SLORETA method shows better accuracy as compared to EMD-SLORETA and SLORETA only. The existing EEG localization methods are efficient but the spatial resolution is still to be improved in the presence of various noise sources in raw EEG. More accurate localization is achieved by implementing denoising in combination with the source localization framework. There is a need to investigate further stages of EEG signal processing along with optimal feature selection. Further, additional studies should be conducted to improve the noise sensitivity of other existing localization systems using pre-processing approaches. [ABSTRACT FROM AUTHOR]

In this paper, a three-dimensional target localization problem in widely separated multiple-input multiple-output radars is solved using two specific techniques based on time difference of arrival measurements. These techniques are provided in terms of transmitter and receiver antennas, which are named as technique_t and technique_r, respectively. The localization problem is rewritten as a non-convex optimization problem which is based on a least-squares method without any initial estimation. Therefore, a convex semidefinite programming problem is obtained by utilizing the semidefinite relaxation method for the problem which can be performed via the CVX toolbox. Several simulations are provided to evaluate the positioning accuracy in terms of bi-static range error for 3 and 4 transmitter/receiver antennas, different antenna arrangements, and near/far target. In other simulations, the localization accuracy is evaluated in terms of the empirical cumulative density function of positioning error. The results show that the proposed techniques have better accuracy and performance in different scenarios in comparison with other compared methods. The last simulation also demonstrates that the computational time of the mentioned techniques is 0.69 s which is suitable for real-time processing. [ABSTRACT FROM AUTHOR]

DV-Hop and its various improvements overexpose hop size and hop count for localization. Whereas hop size is always erroneous and hops path is not a straight line, which leads to a faulty location estimation. The proposed model optimized distance range free (ODR) localization algorithm limits the use of hop size and hop count to approximate nearly a straight line distance between a known and an unknown node without additional hardware and without increasing extra communication. The refrain use of hop size and hop count improves localization accuracy of ODR and makes it robust against those network variables which affect the hop size accuracy adversely. In fact, ODR modifies the last two steps of DV-Hop. DV-Hop finds hop size in its second step. Here ODR rectifies this hop size and then a centroid is obtained from the minimum distant anchor nodes for an unknown node. Now a minimum possible distance known as base distance is estimated with a routing table assistance. In the last step, DV-Hop uses least square regression to localize, while ODR exploits linear optimization to comprehend the base distance for localization. The paper establishes ODR analytically and rugged with ranging error of the omnidirectional antenna coverage pattern experimentally. [ABSTRACT FROM AUTHOR]

Prime goal of WSN deployment in large specific area aims to sense the environment and execute the defined application with the help of essential location information of the devices. Through localization technique, location information is assigned to the unknown devices within the area of interest. Due to its definite solution capabilities with fast convergence rate, bio inspired application become popular to solve numerous applications in the field of wireless sensor network (WSN) applications. In this paper, a newly developed meta- heuristic algorithm based on the social behavior of chickens named as chicken swarm optimization (CSO) is proposed to solve the WSN node localization problem. Two performance metrics which are node precision and computation time are investigated using three different bio inspired algorithms that are particle swarm optimization (PSO), binary particle swarm optimization (BPSO) and penguin search optimization algorithm (PeSOA) respectively. Results are demonstrated using simulation graph where CSO performs more precise accuracy having a ratio of 55% over PSO and BPSO and 10% over PeSOA. For computation time, proposed algorithm performs a computation time that is shorter by 30% than PeSOA as well as 50 and 40% than PSO and BPSO, respectively. [ABSTRACT FROM AUTHOR]

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss-Newton algorithm. [ABSTRACT FROM AUTHOR]

Localization accuracy and low operating costs are substantial and key issues in operating and managing wireless sensor networks. The literature has many algorithms which used optimization techniques to promote the localization accuracy. This paper proposes a novel and an efficient algorithm based on state of art of black box optimization techniques, in order to promote the localization accuracy. This optimization technique is called hierarchical structure poly-particle swarm optimization. The proposed algorithm considers that the ranging technique is based on the cheapest distance estimation method which is receive signal strength indicator (RSSI). The proposed algorithm is more charming to promote the localization accuracy, where it has special characteristics such as the easy implementation of HSPPSO. In addition, RSSI is the cost efficiency measurement method. Simulation results demonstrate the effectiveness of the proposed algorithm comparing to other algorithms which based on optimization, e.g., ant colony, genetic algorithm, and basic PSO. This is clearly evident in some of the evaluation metrics such as localization accuracy, localization rate, and complexity. [ABSTRACT FROM AUTHOR]

Localization is a fundamental issue in wireless sensor networks (WSN). Among typical range-free localization algorithms, distance vector-hop (DV-Hop) is preferred as it can localize unknown nodes with less than three neighbor anchors. However, the accuracy of DV-Hop localization is low due to its multi-hop nature and defective position-calculation procedure. This paper first investigates a neglected issue during position calculation, i.e., reference anchor selection problem in the third step of DV-Hop. Then in order to solve this problem, two new algorithms are proposed. As for the first algorithm named as RAS DV-Hop, since every anchor can act as a reference anchor, an unknown node can obtain several candidate positions, and the best candidate that has the most similar distance to anchors with the unknown node is chosen as the final estimated position. The second algorithm named as GOS DV-Hop provides a global optimized solution for the third step of DV-Hop, achieves better accuracy than RAS DV-Hop, but has higher computation complexity. Simulation results indicate that both proposed algorithms achieves better precision, compared to typical DV-Hop based algorithms. [ABSTRACT FROM AUTHOR]

Accurate and fast localization of randomly deployed sensor nodes is needed for many applications in wireless sensor networks. Localization also benefits in recognizing the geographically area where an event took place. There is no meaning of any event information without the knowledge of its location coordinates. DV-Hop is one of the main range free localization technique, which estimates the position of nodes using distance vector. Particle swarm optimization is suitable for the localization issues because of its fast computing speed and high precision. To further reduce the positioning error, the traditional DV-Hop localization algorithm based on single objective optimization algorithm is converted into a multi objective optimization algorithm. In our proposed scheme, we have considered six different single objective functions and three different multi objective functions. In this paper, a multi objective particle swarm optimization based DV-Hop localization is proposed in 3-dimensional wireless sensor networks. The proposed functions has been evaluated on the basis of computation time, average localization error and localization error variance. The simulation results show that our proposed multi objective function performs better as compared to traditional single objective function. [ABSTRACT FROM AUTHOR]

This paper presents a resilient localization scheme for wireless sensor networks (WSNs). It suits well in estimation of node position under a corrupted radio environment. Position computation is based on information of angle-of-arrivals (AoA) and references obtained from a few mobile anchors. In the network, anchors are equipped with smart antennas and global positioning system receivers. They broadcast signals in a synchronous and periodic fashion. The neighboring nodes having the signals with received signal strength values above a prescribed threshold level, respond with their respective IDs. Anchors evaluate AoA information from these signals using estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm. Next, they forward beacon messages, containing their references and estimated angles, to the corresponding nodes and move along random trajectories. After receiving three sets of such data, at least, nodes can initiate selective segregation of the inconsistent position estimations. Simulation results attaining higher degree of localization accuracy validate its competency over the existing schemes. [ABSTRACT FROM AUTHOR]

During the launch mission of a launch vehicle (LV), localization of the LV is very important for ensuring flight safety. Usually, active localization systems such as the ground tracking radar systems and onboard navigation systems of the LV are responsible for acquiring the relevant location information for localization. However, if, in addition to the existing localization methods, passive localization using ground telemetry stations can be implemented, the reliability of the flight safety control for the LV can be improved. Time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) are typical localization techniques used for an emitting target. In this paper, we examine passive TDOA/AOA localization that uses two ground telemetry stations, and propose a technique to improve its localization accuracy, based on second-order polynomial regression and the removal of the estimated multipath delays in the TDOA/AOA results. By comparing the results of our proposed technique with the measured data from an on-board GPS used in the 3rd Korea Space Launch Vehicle-1 (KSLV-1) mission, our study confirms that the localization accuracy has been improved. [ABSTRACT FROM AUTHOR]

A Mobile Ad hoc Network (MANET) is a network composed of numerous autonomous mobile nodes. In recent times, the opportunistic network, a type of MANET is gaining a lot of significance among the researchers, as it is capable of communicating with the sink node through an efficient selection of relay nodes. In the opportunistic networks, the node does not seek any knowledge about the network topology as it selects the efficacious relay node for transmission of packets. However, MANET requires nodal information about network topology. In the opportunistic network, the data stockpiled in the packets are transmitted from a source node to a sink node by utilizing relay node opportunistically for every hop. However, this type of communication leads to delayed data delivery with increased hops as a consequence of the unsystematic selection of relay nodes. To overcome these constraints, this article focuses on the selection of optimal relay nodes for attaining faster data delivery, by unveiling the location and by predicting the mobility pattern of the neighbor nodes. Hence, this research paper proposes Particle Swarm Optimization algorithm for the selection of optimal relay nodes by locating the neighbor nodes within an established Inter-Communication Range employing Cartesian based localization technique and by analyzing their mobility pattern using recurrent neural network-long short-term memory prediction model. The results of the proposed methodology are compared with four other existing methods, namely, MaxProp, Spray and Wait, and Epidemic. The comparative results infer that the proposed method is efficient in terms of performance, reduced hops, reduced delay with enhanced packet delivery ratio, and improved overhead ratio. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed. [ABSTRACT FROM AUTHOR]

Randomly deployed sensor nodes can form a wireless sensor network (WSN) in order to track changes in a certain physical quantity. These sensors can exchange the measurement data with themselves and with an outer interface. The measured data may be a location specific. Therefore, the localization in WSNs becomes a necessity. This localization can be carried out by using a lot of measurement models. These models include; time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA), and received signal strength. TOA and TDOA based localization can offer a very good accuracy than the other types. In this paper, the cooperative TOA localization with virtual anchors selection is proposed in order to increase the accuracy of the localization procedure. The proposed cooperative TOA procedure is explained and mathematically analyzed based on linear least square algorithm. Simulation results show that the proposed cooperative TOA can reduce the mean square positioning error. Furthermore, it is more tolerable to noise and errors than the ordinary TOA. In addition, it can provide a 3 dB performance over the ordinary TOA method. [ABSTRACT FROM AUTHOR]

User localization is one of the key service-enablers in broadband mobile communications. Moreover, from a different point of view, next steps towards automatic network optimization also depend upon the capability of the system to perform real-time user localization, in order to obtain the traffic distribution. The aim of this paper is to get deeper into the feasibility and accuracy of different localization mechanisms ranging from triangulation to database correlation. Call tracing data extracted from a real operating mobile network have been used to assess these algorithms after the execution of an extensive measurements campaign. Results show that enhanced triangulation offers the best performance even outperforming other more sophisticated mechanisms like fingerprinting, without introducing any change in the network and without requiring any special characteristic of the user equipment. Indeed, the lack of precision of channel estimates, which for the same position could differ up to 10 dB, introduces a large uncertainty that harms localization mechanisms based on database correlation. Finally, this paper identifies the areas for improvement in triangulation to reach its maximum potential, provides details for its implementation and analyzes the performance of the different proposed enhancements. [ABSTRACT FROM AUTHOR]

In this paper, algorithms for navigating a mobile robot through wireless sensor networks are presented. The mobile robot can navigate without the need for a map, compass, or GPS module while interacting with neighboring sensor nodes. Two navigation algorithms are proposed in this paper: the first uses the distance between the mobile robot and each sensor node and the second uses the metric calculated from one-hop neighbors' hop-counts. Periodically measuring the distance or metric, the mobile robot can move toward a point where these values become smaller and finally come to reach the destination. These algorithms do not attempt to localize the mobile robot for navigation, therefore our approach permits cost-effective robot navigation while overcoming the limitations of traditional navigation algorithms. Through a number of experiments and simulations, the performance of the two proposed algorithms is evaluated. [ABSTRACT FROM AUTHOR]

Tag localization in passive UHF RFID has become increasingly important, since the RFID system was introduced. Primarily the localization techniques in RFID are dependent on precise reckoning. Multiple researches have been made to solve the challenge of localization of RFID tags. These finding differs on the basis of cost and reliability. UHF RFID system using passive tags is a suitable option because of low cost, high reliability and global acceptance. In this paper a two-dimensional tag matrix-based localization algorithm is designed localize the passive tags using mobility of RFID reader. The algorithm is based on estimation equations for localization of tag. The data is collected from the scenario by the mobility of reader and location is estimated by the using estimation equations. Results revealed that the proposed design gives the desired result with greater level of accuracy as compared to other localization schemes. And it can be easily applied to various RFID applications. [ABSTRACT FROM AUTHOR]

Localization in wireless sensor networks (WSNs) is a necessity as there is a vital need to have location information combined with the measured quantities. The received signal strength (RSS) based localization is a simple and a cheap model but it provides a low positioning accuracy. However, its accuracy can be enhanced by applying a cooperation mechanism among sensors. In this paper, the performance of RSS, based localization, and its cooperative version are studied, analyzed, and simulated in WSNs which operate in the millimeter wave frequency bands. Moreover, this performance is compared with that of WSNs which operate in radio bands. It can be concluded that, the positioning accuracy is high in WSNs operating in millimeter wave bands. Moreover, their performance is better than that of the corresponding WSNs operating in radio bands especially at low levels of signal to noise ratio. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are fundamental blocks of Internet of Things (IoT) which sense a physical phenomenon and aggregates the sensed data. Numerous IoT applications require human intervention for network maintenance. Several approaches have been proposed in the literature for creating the location map (localization) that associates physical locations to the nodes. However, searching a node of interest for servicing in a large-scale network with the knowledge of location map is a tedious task. In this paper, we propose a smartphone based acoustic navigation tool that can be used along with the location map to ease the search of a specific sensor node. We make use of the Doppler effect of acoustic wave for navigating the user towards the node of interest. Our approach is of lower complexity which can be implemented in a real-world scenario. In addition, the proposed algorithm takes into account the missing probability of the node of interest. Performance is investigated in terms of time required for searching the node of interest with respect to the network size. Experiments demonstrate that the proposed method helps the user to reach a node of interest with an average difference of 0.071% to ideal search time. [ABSTRACT FROM AUTHOR]

In this paper, localization error of the received signal strength (RSS)-based method in mixed LOS/NLOS conditions is analysed. In contrast to the time of arrival, time-difference of arrival and angle of arrival, RSS measurements are low-cost and ubiquitous in the indoor environment. The localization inaccuracy for the RSS-based method is first computed in the presence of NLOS positive bias using perturbation analysis. Subsequently, root-mean-square localization error is determined using Cramér–Rao lower bound analysis under the range error. Experimental results for mobile phone localization in two different indoor environments are shown to attain theoretical bound asymptotically. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a livestock tracking system, with the objective of transmitting the location and activity status of the animals, in real-time, to an end-user. The system comprises of tag, beacon and base station nodes, communicating wirelessly with each other. Tag nodes receive location information from neighbouring beacon nodes and through the process of trilateration, the location of a specific animal is determined. Motion sensors within the tags, are used to determine activity status of the animal. The base station node receives the identity, location and activity information, from the tag nodes and transfers the data to a web server, where a database stores the tag information in real-time. An Android application, serves as an interface between the end user and the web server, enabling the remote monitoring and tracking of the livestock. The performance of the system is evaluated in terms of its range, accuracy and the ability to detect and store information. The nodes in the system are able to communicate within the expected ranges. The tag data can be inserted into the database and be retrieved for end user needs. The results demonstrate that the system can successfully read, process, transmit and display the location and activity information. [ABSTRACT FROM AUTHOR]

The accurate and efficient localization of a person equipped with wireless body area network (WBAN) is still a major issue in surveillance systems. Localization mechanisms using signal strength suffer from inaccuracy but those especially based on fingerprints are likely more accurate for indoor environment and some related mechanisms have been proposed. However, a simple and accurate mechanism is still unavailable for indoor environment except those using ultra wideband technology which is not yet implemented in all existing devices. This paper proposes a new mechanism based on fingerprinting for WBAN indoor localization named Kriged Fingerprinting. From the recorded data, this proposed model generates a huge unsaved database used to estimate the mobile node. It is a hybridism of fingerprinting, particle swarm optimization (PSO) and kriging. The fingerprinting method operates into two phases: the first is the training phase that serves to build a radio map in a database and the second phase is a matching phase. PSO is used as a matching algorithm and when a particle gets to an unrecorded location, the kriging mechanism is used to interpolate its locations values. In comparison with some works done for indoor localization mechanisms and based on fingerprinting, our proposal outperforms all in terms of accuracy: the mobile node is localized with a mean error value of 0.906 m. Hence, the kriging method brings more improvement in localization based on fingerprinting. [ABSTRACT FROM AUTHOR]

Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field to perform sensing and acting task. The goal of localization is to assign geographical coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, the cuckoo search algorithm is implemented to estimate the sensor's position. The proposed approach has been compared in terms of localization error with particle swarm optimization (PSO) and various variants of biogeography based optimization (BBO). The results show that our method outperforms the PSO and BBO variants which are recently used in the literature. [ABSTRACT FROM AUTHOR]

In operating and managing wireless sensor networks (WSNs) and their applications, the high accuracy of localization and the low operating costs are considered the substantial and key issues. The literature is rich in algorithms for localized WSN devices in hostile and unreachable outdoor environment. Majority of the literature considered mobile anchor as one of the solutions in locating sensor nodes. In this situation, the critical issue is the trajectory planning. All algorithms supposed that the mobile anchor should travel following the shortest path to determine the positions of sensor nodes with minimum localization error. A localization algorithm, which is called efficient localization algorithm based path planning for mobile anchors (ELPMA), is proposed. ELPMA is based on a one-mobile anchor moving in adjustable circular trajectory to scan the target area. It considers that the received signal strength indicator is the ranging function to determine the distance between mobile anchor and sensor nodes. ELPMA supposed the mobile anchor starts the motion from the center of the target area. The travelling paths are planned in advance by ELPMA based on the distance measurements between the mobile anchor and the sensor nodes. Simulation results demonstrate that ELPMA has better performance compared to other algorithms based on static path. This performance was evident in the localization accuracy and trajectory planning. [ABSTRACT FROM AUTHOR]

The original DV-hop algorithm performs pretty well in isotropic Wireless Sensor Networks in which nodes distribute uniformly. However, the localization accuracy degrades severely in anisotropic networks caused by uneven nodal distribution or irregularity of deployment region. In this paper, we propose a novel DV-hop algorithm based on Locally Weighted Linear Regression (LWLR-DV-hop), in which kernel method is adopted to improve the localization accuracy by raising the weight of neighboring anchor nodes. In the simulation section, algorithms are evaluated within two deployments and three topologies: the regular and random deployments, the L-shaped, O-shaped and X-shaped topologies. As performance metrics, the Average Localization Error and the Cumulative Distribution Function are used. The results of simulation and experiment reveal that LWLR-DV-hop performs better than original DV-Hop in anisotropic networks of different topologies, in which localization accuracy is improved by about 40% on average. [ABSTRACT FROM AUTHOR]

Connectivity in vehicular traffic environment has witnessed significant attention due to the direct impact on the performance of most of the traffic safety applications of intelligent transport system. Various parameters such as density, speed, direction, link quality and inter vehicle distance (IVD) have been utilized for measuring connectivity. IVD has greater impact on connectivity and controls the impact of other parameters. Usage of real time IVD for measuring connectivity has not received sufficient attention in VANETs. This paper proposes IVD based connectivity aware routing (Ivd-CAR) for enhancing connectivity aware data dissemination. IVD calculation is robust and can effectively handle instantaneous GPS failure. Two localization techniques; namely, cooperative localization and Geometry based Localization are developed. Standard deviation of real time IVDs of a forwarding path is derived. Distribution of IVDs of a forwarding path is employed for estimating connectivity. Segment vehicle based next hop vehicle selection is utilized for incorporating network load, link quality and direction into consideration while selecting forwarding path. Simulations are carried out in ns2 to evaluate the performance of Ivd-CAR in realistic traffic environment. Comparative analysis of simulation results attests the superiority of Ivd-CAR to the state-of-the-art techniques: CSR and A-CAR. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is consisted of enormous amount of miniature devices recognized as sensor nodes empowered with sensing, processing, and computing abilities. The main role of WSN is sensing, gathering, and transmission of data to the destination. In several applications, the location of collected data is of much significance. Such type of information is acquired by a localization technique. So, localization is a vital issue in various applications of WSNs. On account of range estimates, the localization techniques can be divided into range based and range free. The range based schemes require added device, thus costly to implement in practice. Range free schemes reduce the expenditure because they do not need additional ranging device. Therefore, the range free methods are practiced as worthwhile alternatives to range based schemes. However, these schemes comprised more localization error as compared to range based schemes. Hence, to overcome the existing drawback of conventional DV-Hop, a PSO based improved localization algorithm is offered in this paper. Simulation results confirm that our proposed algorithm reduces the error and error variance of localization whereas improves the accuracy in comparison with the existing localization algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network is one of the new technologies that have gotten more attention in the past few years. The localization problem is one of the most important topics in these types of the networks. The traditional positioning techniques cannot be used in these networks due to the hardware restrictions of the sensor nodes. Lately, some positioning methods which use soft computing approaches such as neural networks, are proposed for solving the localization problem. In this paper, we propose a new range-free localization algorithm which uses the neural networks for this purpose. This method utilizes Particle swarm optimization (PSO) algorithm to optimize the number of neurons of hidden layers of neural networks. The objective function considers both localization accuracy and storage overhead, simultaneously. The proposed algorithm is implemented and simulated in isotropic networks with and without coverage hole, and anisotropic networks. The obtained result show, in the different environmental conditions, the proposed algorithm has a less localization error rate and less storage requirement than the analogous methods. [ABSTRACT FROM AUTHOR]

Designing a scheme for facilitating sensors in a WSN to compute their own locations is a challenging task. In this paper, we propose a scheme that equips sensors to estimate their own locations. In the proposed scheme, a sensor adds a set of virtual points for each location of a moving anchor. Using sets of virtual points together with actual locations of the moving anchor, it computes a convex hull. The points lying on the convex hull are used to construct an optimized circle whose center and radius is used to compute the location of the sensor iteratively. We have shown that the proposed scheme converges to the actual location of an unlocalized sensor. We observe that the localization error for the proposed scheme decreases with an increase in the number of iterations. The proposed scheme performs significantly better as compared to the existing schemes. Further, the proposed scheme provides a tradeoff between the accuracy of localization and the delay incurred during the process of localization. [ABSTRACT FROM AUTHOR]

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach. [ABSTRACT FROM AUTHOR]

The applications of wireless senor networks (WSN) vary in diversified field, at different geographic locations. Location aware computing is the key for the success of such applications. Henceforth, there is a need of efficiently estimating the location of individual WSN nodes deployed in the remote geographic locations. Manually estimating the locations of these densely deployed nodes is impossible, therefore the WSN nodes must be able to localize themselves collecting local information from its neighboring nodes, which is called as the localization technique. The information used for localization is generally distance and bearing information obtained from the ranging techniques which are not accurate and are prone to error. Therefore there is a need for techniques which can cope up with this perturbed distance information. Rigid graphs have the property of sustaining various kind of deformations due to translation, rotation and reflection. Hence, it is more fruitful using the concepts of rigid graphs, for estimating accurate location coordinates from error prone distance measurements. In this paper we will scrutinize the sound theoretical background which defines the need of rigid graph based localization and different localization techniques, with associated algorithms which uses the concepts of rigid graphs. [ABSTRACT FROM AUTHOR]

Localization based on the received signal strength (RSS) is by far the cheapest and simplest option. By constructing the appropriate path-loss model, the RSS information can be converted to the distance estimates which can determine the position of the target node with Linear Least Square (LLS) algorithm. In this paper, the LLS algorithm can be directly applied by subtracting a reference node equation from the remaining ones rather than defining a new variable, which selects the reference node with the maximal RSS among all the RSS measurements. Furthermore, based on the latest research results about the estimates of the squared distance, we investigate the unbiased and linear minimum mean square error (MMSE) estimates of the squared distance, and the covariance matrixes about the two estimates with the proposed LLS algorithm are derived, respectively. Then the weighted LLS localization algorithms are presented by utilizing the covariance matrixes. Simulation results demonstrate that the localization accuracy of the linear MMSE estimate with the proposed LLS algorithm is superior to the unbiased estimate. Moreover, the proposed weighted LLS algorithms outperform the traditional LLS localization algorithms in terms of localization accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the indoor positioning technologies have been recognized as core technologies for realizing smart space, a ubiquitous society, context awareness, and various location-based services. There are several approaches for positioning with radio signals, but the received signal strength (RSS)-based technology is considered a promising scheme because of its simplicity and practicality in implementation. In this paper, the positioning performance of the RSS value-based scheme is analyzed with respect to the location of access points (APs) and the number of APs in an indoor environment. An adaptive AP selection scheme and a base AP changing scheme are then proposed to enhance the positioning accuracy. In order to estimate the RSS characteristics, RSS values are measured as the distance between the AP and the receiver increases. The positioning performance is evaluated with differing AP numbers, which form a triangle or a quadrilateral. The performance of the proposed schemes is evaluated via experiments using wireless local area network APs. Results show that the performance of proposed schemes is enhanced compared to that of conventional scheme. [ABSTRACT FROM AUTHOR]

In this paper, a novel localization algorithm is proposed by utilizing the time difference of arrival (TDOA) with asynchronous base stations. Generally, a TDOA-based localization algorithm requires synchronization between base stations. Hence, correlations using wideband signals or wire connections between base stations have been used to synchronize the base stations; however, these approaches result in localization error by synchronization error between base stations. Thus, the proposed algorithm does not require synchronization between base stations. The TDOA equations of proposed algorithm are derived by continuously varying the locations of the mobile station and the location of a base station. Localization procedure is also reduced. The localization performance of the proposed algorithm is verified with Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

LTE overlay cognitive femtocells system can provide a satisfied throughput for the macro and the femto users. On the other hand, the femto users can assign only the vacated subcarriers from the macro users. This assignment results in a reduced throughput for the femto users. Moreover, their throughput can have a zero value when the macro users operate on all subcarriers. In this paper, an underlay cognitive femtocells operation is proposed. The femtocells can share the subcarriers with the existing macrocells in such a way that their transmission cannot affect the macro users' performance. The macro users' positions can be determined, during their uplink transmission, using the received signal strength at the femto users. Then, the femtocells adapt their emitted power to guarantee a satisfied macro users' performance. The performance of the proposed LTE underlay cognitive femtocells system is mathematically analyzed, simulated, and compared with the previous one which applies overlay cognitive femtocells. It can be observed that, the femto users' throughput is existent and has a high value in comparison with the previous LTE overlay cognitive femtocells system. Furthermore, the proposed system has a higher macro and femto users' throughput. [ABSTRACT FROM AUTHOR]

The location determination technique using time difference of arrival (TDOA) measurements has been widely used in the military and observation industry. The accuracy of geolocation estimation is a very significant problem because measurement data are affected by environmental noise. Environmental noise occurs due to measurement error and the non-line of sight (NLOS) problem. This paper presents a Kalman filter-based NLOS section identification method and an iterative estimation of emitter location using the recursive weighted least square (RWLS) algorithm. Using fixed receivers with known locations, we obtain TDOA data that contain environmental noise. We identify the NLOS section of each receiver using a Kalman filter. Using the identified line of sight (LOS) TDOA measurements, we accurately derive the estimated location of an emitter with a fast calculation speed using the proposed RWLS algorithm when we receive additional TDOA data. In order to confirm the performance of the RWLS algorithm, the presented simulation results show that the proposed technique achieves improved accuracy and speed for estimating the emitter location. [ABSTRACT FROM AUTHOR]

The current restricted area based range free localization schemes use various geometric shapes such as triangle, rectangle, and circle to determine the residence area of the sensor node. Then this residence area is used to localize the sensor node. However, the existing work requires higher density of anchor deployment to gain the acceptable accuracy of position estimation. In this paper, we overcome the density dependent accuracy of localization by proposing a new localization scheme called geometric anchor beacon (GAB). The proposed scheme is based on analytical geometry of an arc. In the proposed scheme, the $$Cramers \ rule$$ is used, where the intersecting point of two perpendicular bisectors of the chords is taken as the estimated position of the sensor node. Simulation results show that, GAB shows less estimation error than any other range-free localization schemes such as CAB, Centroid, Convex, and APIT. [ABSTRACT FROM AUTHOR]

This paper proposes a progressive Isomap algorithm for node localization. The algorithm is an extension of centralized Isomap and is capable of effectively localizing new nodes progressively, without neglecting preceding computational results. In the initial startup phase, sensor nodes within two hop range of three non-collinear anchor nodes are localized using range ( $$\epsilon$$ )-Isomap. Afterwards, in the progressive phase, the remaining nodes are localized in a sequential manner, using recently localized nodes. The localization process initiated at different nodes in the network give rise to the overlapping substructures of localized nodes. The overlapped substructures are stitched to form a single coordinate system. Finally, Helmert Transformation is used to obtain the global coordinates of the nodes. The effects of varying various parameters on the accuracy and scalability of the proposed algorithm are studied through the simulation. Results indicate that the proposed algorithm has good positioning accuracy and noise-robustness. [ABSTRACT FROM AUTHOR]

Ultra-wide bandwidth (UWB) transmission is a promising technology for localization systems, due to its power efficiency, strong penetrability, and robust operation in severe multipath environments. As we know, the large time stamp errors result in significant localization errors. The time stamp errors will be large if the direct path (DP) signal that is between the transmitting node and the receiving node is not detected correctly at the receiving node. It always happens in non-line-of-sight (NLOS) conditions. So it is very important to identify NLOS, because we want to mitigate the significant localization errors in NLOS conditions. The existing techniques based on machine learning techniques have not discussed the problem that the features used to represent the propagation conditions are affected by the transmission distance of the signal, the transmitted power, the transmitted preamble length and other factors (related-factors-of-features, RFOF), which seriously affect the accuracy of the classifier that is trained by the features. So a practical NLOS identification (PNI) technique is proposed in this paper. PNI includes reduce-effect-of-related-factors (REORF) for reducing the serious effect and a set of appropriate features for improving speed of NLOS identification. Experimental results show that PNI have better performance in various system environment and real-world environment than the existing techniques. [ABSTRACT FROM AUTHOR]

In recent times although there is lot of research in field of localization application but very few aims at localization module with optimal energy consumption. Localization, being inevitable for a range of applications, is also a basis for many network operations such as data packet forwarding i.e. routing and sensing coverage in wireless sensor networks. With a range of application scenario, it is a challenging task to design a localization module for a wireless sensor network consisting of sensor nodes with limited capacity in terms of power, processing and storage etc. This paper presents a simple model of a range based localization system that employs distributed computing in a collaborative and cooperative manner. The aim of this work is to modify a range based localization algorithm to utilize scarce resources optimally along with improved performance by changing the inherent methodologies of position estimation. In this research work basically the role of a media access control scheme (MAC) in the localization module is investigated. A MAC is basically used to save the energy by reducing collisions and retransmission of range packets. Further, energy is conserved by restricting the coverage to a smaller part containing the neighbours up to certain hops and that is formed by dividing the whole network area into clusters. To analyse the performance spectrum, we have simulated the localization method under a variety of application scenarios and lead to the conclusion that this approach increases the efficiency of localization in wireless sensor networks. In the simulation process, every minute aspect of sensor network localization model including the topology, range, mobility, channel, propagation schemes etc. were considered and the performance was finally analyzed. [ABSTRACT FROM AUTHOR]

In presence of obstacles, localization is a real challenge since multi-path effect takes place due to reflections and/or scattering of signals. There are works in the literature which discard multiple-bound signals received by sensors using statistical methods and then localize sensors using remaining signals. In literature, to the best of our knowledge, there is no deterministic way to know the number of reflections/bounds of a particular signal, so researchers have to rely on statistical methods for localization. In this paper, we analyze multiple-bound signals based on the information gathered by different ranging techniques viz. AOA, TOA, RSSI and make use of a multiple-bound signal where number of reflections are not known, to localize sensor deterministically. [ABSTRACT FROM AUTHOR]