Localization In Wireless Sensor Networks Research Articles

Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.

Wireless Sensor Network Localization using Artificial Intelligence and Simulated Annealing Optimization

In recent years localization of nodes in wireless sensor networks (WSNs) has become one the main features of applications. In fact, this issue has been widely explored by the scientific community that proposed many approaches in order to localize network nodes. However, artificial neural network (ANN) can be used as an operating method. Therefore, we aim in this paper to select the best suited structure of ANN to localize in WSN using a meta-heuristic technique. To optimize this procedure, we use the Simulated Annealing (SA) algorithm. We constituted a network of ESP8266 modules to create our WSN topology as well as the training and the testing data to evaluate the performances.

Singular Value Thresholding Algorithm for Wireless Sensor Network Localization

Wireless Sensor Networks (WSN) are of great current interest in the proliferation of technologies. Since the location of the sensors is one of the most interesting issues in WSN, the process of node localization is crucial for any WSN-based applications. Subsequently, WSN’s node estimation deals with a low-rank matrix which gives rise to the application of the Nuclear Norm Minimization (NNM) method. This paper will focus on the localization of 2-dimensional WSN with objects (obstacles). Recent studies introduce Nuclear Norm Minimization (NNM) for node estimation instead of formulating the rank minimization problem. Common way to tackle this problem is by implementing the Semidefinite Programming (SDP). However, SDP can only handle matrices with a size of less than 100 × 100. Therefore, we introduce the method of Singular Value Thresholding (SVT) which is an iterative algorithm to solve the NNM problem that produces a sequence of matrices { X k , Y k } and executes a soft-thresholding operation on the singular value of the matrix Y k . This algorithm is a user-friendly algorithm which produces a low computational cost with low storage capacity required to give the lowest-rank minimum nuclear norm solution.

Multilateration Localization for Wireless Sensor Networks

Objectives/methodology: Localization in wireless sensor networks (WSNs) has long been one of the most interesting areas that researchers continue to study. This study presents the methodology of mathematical model for multilateration localization in WSNs verified by a simulation model using the NS-2 simulator. Findings: The new modules added to NS-2 can be extended to various rangebased localization techniques, which help many researchers in this field. Applications: This work makes a comparative study of atomic and iterative multilateration localization according to different performance metrics. Keywords: Multilateration, Localization, NS-2, Simulation, WSN.

Differential Evolution With Opposition and Redirection for Source Localization Using RSS Measurements in Wireless Sensor Networks

To deal with the multimodality, nonlinearity, and nonconvexity of the maximum likelihood (ML) cost function when solving the localization problem with received signal strength (RSS) in wireless sensor networks, we propose a localization approach based on the differential evolution algorithm, opposition-based learning, and adaptive redirection in a unified way. In the proposed approach, we need neither to approximate the ML cost function nor to provide a good initial point while most conventional approaches, including those based on the semidefinite programming, second-order cone programming, and unscented transformation. The performance and computational complexity of the proposed approach are analyzed and compared with those of other approaches for three practical scenarios. Simulation results confirm that the proposed approach performs better than other approaches with relatively low computational complexity. Note to Practitioners —This article was motivated by the problem of localization in wireless sensor networks that can be found in different areas, such as intelligent farming, health care monitoring, and environmental monitoring. In this article, a technique is proposed, which exploits the RSS measurements to estimate the position of a target node. This technique is quite popular due to its simplicity and low cost. Most of the techniques based on the ML formulation approximate the ML cost function due to its high nonlinearity and nonconvexity. This article suggests a new approach using differential evolution and opposition-based learning a redirection, which does not require approximating the ML cost function to find the location of a target. In addition, in this article, we analyze the sensor nodes’ placement and its effect on localization accuracy. Numerical results in three practical scenarios are provided to demonstrate the effectiveness of the proposed approach and its superiority compared with state-of-the-art algorithms. In future research, we will address a more realistic scenario with multiple targets.

Semi-supervised manifold learning based on polynomial mapping for localization in wireless sensor networks

Localization of sensor nodes is becoming an interesting research area in wireless sensor networks(WSNs). In recent years, some localization algorithms are presented based on manifold learning which can achieve good performance. In this paper, based on the need of practical application and the development of manifold learning theory in WSNs, the localization problem is considered for fast determining the locations of unknown nodes in large scale WSNs. For this purpose, a new semi-supervised manifold learning algorithm is proposed based on polynomial mapping which is the combination of Gaussian kernel embedding and polynomial kernel embedding algorithms. This method is to compute a polynomial mapping function between the high dimensional location data space and the low dimensional physical space, which can obtain an explicit nonlinear feature mapping and has a high discriminative ability. At last, comparing with some related localization approaches, the performance of the proposed algorithm is evaluated and analyzed under different signal noise, anchor nodes, and communication range, respectively. In terms of the root-mean-square error and computational complexity, the experiment results demonstrate that the proposed algorithm has faster speed for large scale sensor nodes and higher accuracy with a small amount of known nodes.

E-DTDOA Based Localization for Wireless Sensor Networks With Clock Drift Compensation

A high time resolution localization scheme, using ultra-wide band ranging signal with bandwidth of 2GHz, is proposed for a fully asynchronous wireless sensor network (WSN). The proposed scheme is specifically useful for sensor nodes which are designed to operate at very low ADC sampling rate, in the order of 2–3 MHz, but still achieves the sampling resolution in the order of sub-nanoseconds. To achieve low sampling rate, equivalent time sampling (ETS) technique is used at the sensor nodes. Reconstructed signal obtained by ETS technique, that require periodic transmission of the same signal repetitively, is severely affected by the variation in transmitter and receiver clock drift as against the real time sampling where the variations are due to receiver node clock drift only. Thus, it requires a protocol to precisely estimate the transmitter and receiver clock parameters. A scheme, which uses a novel mathematical equivalent time of arrival (E-TOA) model for ETS based system, for clock drift estimation is presented. Based on clock drift estimation parameters, receiver nodes are tuned to the same frequency. E-TOA measurements are further used to propose an equivalent differential time difference of arrival (E-DTDOA) based ranging algorithm, which relaxed the time synchronization requirement between the wireless nodes, and still achieving high time resolution. The E-DTDOA range measurements are subsequently used to obtain precise localization of the target node/s. The feasibility of the algorithm proposed is demonstrated experimentally using in house designed wireless sensor nodes.

Trilateration based localization method using mobile anchor in wireless sensor networks

<p><span>Localization in wireless sensor networks (WSNs) is essential in many applications like target tracking, military applications and environmental monitoring. Anchors which are equipped with global positioning system (GPS) facility are useful for finding the location information of nodes. These anchor nodes may be static or dynamic in nature. In this paper, we propose mobile anchors assisted localization algorithm based on regular hexagons in two-dimensional WSNs. We draw a conclusion that the number of anchor nodes greatly affect the performance of localization in a WSN. An optimal number of anchor nodes significantly reduces the localization error of unknown nodes and also guarantees that unknown nodes can obtain high localization accuracy. Because of the mobility of anchor nodes high volume of sensing region is covered with less period of time and hence the coverage ratio of the proposed algorithm increases. Number of communications also decreases for the reason that the system contains log<sub>e</sub> (n) number of anchor nodes which leads to less energy consumption at nodes. Simulation results show that our LUMAT algorithm significantly outperforms the localization method containing single anchor node in the network. Movement trajectories of mobile anchors should be designed dynamically or partially according to the observable environment or deployment situations to make full use of real-time information during localization. This is the future research issue in the area of mobile anchor assisted localization algorithm.</span></p>

Special Issue: Localization in Wireless Sensor Networks

Recent and continuous development of mobile computing and user-centric applications has led to a requirement for accurate and low-cost localization and tracking systems [...]

A parallel WOA with two communication strategies applied in DV-Hop localization method

Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.

Analysis of error for anchor-based localization in wireless sensor networks

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.

Improved DV-Hop Localization Scheme for Randomly Deployed WSNs

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

Swarm intelligence based centralized clustering: a novel solution

Recent evolutions of MEMS technology, digital electronics, and wireless communication technologies have made smart environments possible; especially through the apparition or incorporation of sensors. Despite being small-sized, the sensors have paved the way for data collection in the environments where they are applied; including luminosity, gas presence, water content, humidity, pressure, and temperature. This research aims to pragmatically optimize Wireless Sensor Network Localization and Network Coverage issues using nature-inspired algorithms. The specific objective is to establish an optimal nature-inspired algorithm and comparing it with other algorithms regarding the capacity to achieve manufacturing optimization in large WSNs, especially in relation to the parameters of high scalability, data delivery rate, and low-energy consumption. Also, the study seeks to determine the extent to which swarm intelligence (SI)-based centralized clustering solutions (optimal nature-inspired algorithms), compared to other approaches, might optimize the WSN features of localization and network coverage. To determine the solutions’ performance, the study involved three scenarios. In scenario 1, the network operational time and the stability of SI-based algorithms were investigated for large WSNs that had the minimum heterogeneity. Imperative to note is that the WSNs on focus had different numbers of nodes, which included 500, 300, and 100. In scenario 2, the motivation was to investigate the SI-based WSN protocols in relation to the parameters of packet delivery, energy conception, and network lifetime for large WSNs. In scenario 3, the performance of SI-based solutions over large WSNs was compared to that which had been reported previously for other algorithms; with the target parameters of comparison being attributes such as packet delivery, energy conception, and network lifetime. From the findings, this study established that SI-based centralized clustering solutions are not only more recent but also exhibit superior performance compared to other algorithms; with the parameters of the amount of data delivered to the BS, energy consumption and scalability on the focus.

A Security Localization Algorithm Based on DV-Hop Against Sybil Attack in Wireless Sensor Networks

Localization technology is crucial in wireless sensor networks (WSN) by forming the basis of various WSN applications. With the advancements of WSN, WSN attacks for node localization have increasingly become an important security issue. The Sybil attack is an example of such attacks where a reputation system is subverted by creating multiple pseudonymous identities. In this paper, a detection and defense algorithm against Sybil attacks based on the distance vector-hop (DV-Hop) method is presented. Experimental results show that the algorithm can effectively improve the security of the node localization in WSN. When the number of beacon nodes is 50, our proposed algorithm reduces the average localization error by 3% than the traditional DV-Hop.

Location of three-dimensional movement for a human using a wearable multi-node instrument implemented by wireless body area networks

A wireless human body local area network is a wireless network composed of lightweight, wearable or implantable sensor nodes that sense various physiological parameters of the human body. This paper introduces the key technologies and characteristics of wireless sensor networks and the research status of node localization, analyzes the wireless sensor network localization algorithms and their performance evaluation indicators, and conducts in-depth research on wearable 3D node localization algorithms. The 3D positioning algorithm uses static beacon nodes to achieve the 3D positioning of public nodes. The positioning accuracy and positioning rate depend on the number of beacon nodes, and the number of beacon nodes increases, which increases network costs and energy consumption. Aiming at the spatiotemporal correlation between motion data collected by multiple sensors in a wireless human local area network, an RSSI-based multi-node 3D motion monitoring auxiliary algorithm is used, including the planning of wearable device motion trajectories. Throughout the positioning process, the workflow belongs to the public node and involves the format of the relevant information packet. The 3D wearable multi-node positioning algorithm based on RSSI is a distance-dependent distributed positioning algorithm. The public node uses the RSSI ranging method to obtain the distance during the two-stage positioning process, and then uses the three-dimensional multilateral positioning method to perform its own calculation.

GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

Localization is one of the issue in Wireless Sensor Network (WSN). The problem is the estimation error in the localization of the node. In this paper, we propose an indoor localization in WSN based on Global best Local Neighbourhood Particle Swarm Optimization (GbLN-PSO) algorithm. Three anchors are used as the distance measurement for each unknown node to be localized. A comparison of the performances of PSO and GbLN-PSO in terms of localization error (accuracy) and computation time is presenting using simulation in Matlab. The result shows that GbLN-PSO improved the accuracy approximately by 0.01% and reduced the computational time by 86.02% compared to PSO technique.

On Base Station Localization in Wireless Sensor Networks

Wireless sensor networks (WSN) has been a prominent topic for the past decade. WSN consist of multiple sensor nodes, which collect and convey data to the base station(s). Sensor nodes are expected to run on batteries, and it makes energy the scarce resource for sensor nodes. Energy expenditure of a sensor node mainly depends on data transmission, which is exponentially affected by transmission distance. Consequently, if sensor nodes forward their data to the base station directly, distant sensor nodes will exhaust quickly. On contrary, minimization of transmission distance for each sensor node, i.e., each node transmits its data to the closest sensor node on its path to the base station, depletes the energy of sensor nodes that are closer to the base station fast. As a result, the flow balance in the network must be optimized. In this study, we investigate the effect of optimization of the base station location along with flow balance optimization. For this purpose, we compare five different localization methods on different topologies; three statically located linear programming approaches, a dynamically located nonlinear programming approach and a heuristic based hybrid approach. Experimental results indicate that lifetime improvement of up to 42% is possible in selected scenarios.

Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks

The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.

Multi-node topology location model of smart city based on Internet of Things

Smart City is a new model and new form of urban development based on the Internet of Things, the advanced information technology, intelligent technology and multi-network integration. In most cases, wireless sensor networks need to be monitored in complex environments or in areas that humans cannot reach. Most of the nodes will adopt aircraft throwing and other methods, resulting in uncontrollable node positions. Such research cannot be carried out without knowing the specific source of the information. Therefore, the problem of node location in wireless sensor networks has become one of the key issues that must be solved. In this paper, a wireless sensor network topology based on correlation neighbor graph is constructed. The DV-Hop (Distance Vector-Hop) localization algorithm with hop number correction and average hop weighting is proposed. The signal strength value received by the node is converted into the distance between the nodes by the shadowing model; and the ratio of the distance value to the communication radius between the nodes is used to correct the hop value. In order to further improve the positioning performance of the algorithm, the MDV-Hop (Modified Distance Vector-Hop) algorithm is proposed. The MDV-Hop algorithm mainly improves the error caused by the number of hops between anchor nodes, the average hop distance and the network topology in the DV-Hop algorithm. Aiming at the problems of the maximum likelihood coordinate calculation method, the localization problem of nodes is transformed into the minimum problem of solving nonlinear equations, and the improved Bat algorithm is used to replace the maximum likelihood algorithm to obtain the coordinates of unknown nodes. The simulation shows that the unknown node has achieved better positioning results in the IoT positioning model based on wireless sensor network topology and has higher positioning accuracy.

A Lattice Signcrypted Secured Localization in Wireless Sensor Networks

Confidentiality, authentication, and the integrity services of wireless sensor networks (WSNs) are the concerns at present in an era of Internet of Things (IoT) or Internet of Everything (IoE). In this context, a secured location estimation technique is developed for advanced WSN with lightweight lattice signcryption. The method inherently uses range-free DV-hop algorithm to ensure no extra burden on the hardware cost with low power consumption, low complexity (time and space), and desirable accuracy. The anchor nodes and unknown sensor nodes of the WSNs are considered mobile in nature and use the random mobility model. The evaluation and comparison of the developed “SECure LAttice based Signcryption for LOcalization (SECLASLO)” have been done in terms of location error analysis, memory, time, and energy consumption.