Location Of Mobile Node Research Articles

Energy-efficient mobile node localization using CVA technology and SAI algorithm

In the evolving landscape of the Internet of Things (IoT), Mobile Wireless Sensor Networks (MWSN) play a pivotal role, particularly in dynamic environments requiring mobile sensing capabilities. A primary challenge in MWSNs is achieving accurate node positioning with minimal energy consumption, as these networks often consist of battery-powered, mobile sensors where energy replenishment is difficult. This paper addresses the critical problem of energy-efficient node localization in MWSNs. We propose a novel positioning approach leveraging Cooperative Virtual Array (CVA) technology, which strategically utilizes the mobility of nodes to enhance positioning accuracy while conservatively using energy resources. The methodology revolves around optimizing the number of transceiver nodes, considering factors such as node moving speed, total energy consumption, and positioning errors. Central to our approach is the Signal Arrival and Interaction (SAI) algorithm, an innovative technique devised for efficient and precise mobile node localization, replacing traditional Time of Arrival (ToA) methods. Our simulations, conducted under various scenarios, demonstrate the significant advantages of the CVA-based positioning algorithm. Results show a marked reduction in energy consumption and robust performance in mobile node scenarios. Key findings include substantial improvements in localization accuracy and energy efficiency, highlighting the potential of our approach in enhancing the operational sustainability of MWSNs. The implications of this research are far-reaching for IoT applications, particularly those involving mobile sensors, such as in smart cities, industrial monitoring, and disaster management. By introducing a novel, energy-efficient positioning method, our work contributes to the advancement of MWSN technology, offering a sustainable solution to the challenge of mobile node localization.

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

I-OPC: An intelligent optimal path computation system using critical path prediction and deep learning for a time-sensitive network

Latency and energy are critical issues when working with time and power-constrained wireless sensor networks. To avoid wasting both time and energy, such systems require selecting optimal communication routes with minimum latency and energy. The energy and latency costs between sender and destination nodes are greatly affected by the occurrence of transmission holes (black hole and grey hole). Therefore, selecting the optimal path must consider the probability of transmission holes and investigate their impact on energy and latency costs. Based on these problems of silent failures, the current work, proposes i-OPC as an intelligent and effective system to address these problems by forecasting sources of such silent failures and resolving them before they occur. The proposed method uses a customized routing schedule and a multi-objective mathematical optimization approach to rank all candidate paths between the source and destination nodes. In addition, i-OPC implements a machine learning technique using deep learning to predict the energy and latency costs of the future location for mobile nodes (). Also, it determines if the future locations of mobile nodes can result in black holes. Experiments produces promising results in terms of delay, energy consumption, packet delivery rate and hole detection rate against existing methods.

Joint time synchronization and localization of underwater mobile node

Joint time synchronization and localization of underwater mobile node

FAULT TOLERANT ALGORITHM TO COPE WITH TOPOLOGY CHANGES DUE TO POSTURAL MOBILITY

The research involving in providing healthcare services to remote patients have led to the exploration of new solutions to provide effective Quality of Service (QoS) and fault tolerant network for Wireless Body Area Network (WBAN). With the growing number of diseases and percentage of elderly people, need for providing medicated assisted life to disabled or old age population has increased a lot. To deal with these conditions, there must exist a Fault Tolerant system. WBAN in the health care scenario is greatly impacted by issues related to efficient data transmission. In this paper, DV-Hop algorithm is utilized for the localization of mobile nodes in order to cope up with topology changes due to postural mobility.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network.

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Mobile Node Localization in Wireless Networks: Path-Loss Model, Trilateration, and Error Mitigation in a 5G Sub-6 GHz Scenario

In this paper, we proffer a novel technique designed for low-cost and computationally light localization of mobile nodes in an urban terrain, by leveraging the extended COST 231 Hata Path-Loss (PL) model and the Trilateration technique. Our approach accounts for the possibility of a Non-Line-of-Sight (NLoS) scenario in a medium-sized city, wherein one of the three reference nodes required for the trilateration approach encounters NLoS impediments. Our proposed method proceeds with localization by utilizing solely two Line-of-Sight (LoS) reference nodes, while integrating the localization system simulator with an Extended Kalman Filter (EKF). The simulation results presented herein demonstrate a marked enhancement in performance, surpassing that of trilateration in scenarios where three LoS nodes cannot be established.

Sparse Feature Points Extraction-Based Localization With Partial Information Loss in UWSNs

This paper investigates the problem of localization of mobile nodes in the marine environment under conditions of information deficiency. To solve this problem, sparse feature points (SFPs) extraction-based time of flight (ToF) minimum residual localization algorithm and localization based on the prior information of the target under partial information loss are proposed. First, the SFPs extraction method is adopted to fit the sound speed profile (SSP). Based on this, the SFPs extraction-based ToF computational model is proposed. The coordinates of the nodes to be located are calculated by ToF measurement and the ToF model for minimum residual localization. Second, in the case of information loss, particle ranges are generated using the target history a prior information. This is combined with the received node localization information to locate the node by the proposed target prior information-based localization method. Finally, the results of the simulation experiments show that the proposed method achieves a more detailed description of the SSP characteristics. The localization error of the proposed method is reduced fivefold compared with other methods under the condition of information loss, which is more in line with the spatial characteristics of the underwater environment.

Research on Mobile Node Localization Optimization Algorithm in Wireless Sensor Networks Based on Monte Carlo Method

Mobile node localization is one of the key technologies in wireless sensor network applications. Aiming at the shortcomings of Monte Carlo algorithm (MCL) in mobile wireless sensor network node localization in nonideal environments, such as low accuracy and low sampling rate, in the prediction phase and filtering phase of MCL, the communication radius of the unknown node is determined according to the size of the irregularity of the node. Perform layering, assign adaptive weights to anchor nodes of different layers according to the area where they are located, and propose an adaptive improved Monte Carlo algorithm. After simulation analysis, the algorithm has an average localization error of nodes under different regularity conditions. It has dropped by about 12%, and the localization error has dropped by about 10% on average under different speed conditions. Aiming at the shortcomings of the MCL algorithm in mobile wireless sensor mobile networks such as low localization accuracy, large sample demand, and long localization time, the communication radius of the node is fuzzified to reduce the sampling area of the node, and an improved Monte Carlo localization algorithm based on fuzzy theory is proposed. The improved Monte Carlo localization algorithm, after simulation analysis, is about 50% shorter than the traditional MCL algorithm in localization time, and the localization accuracy is up to about 30% higher than the traditional MCL algorithm.

Three-dimensional localization algorithm of mobile nodes based on received signal strength indicator-angle of arrival and least-squares support-vector regression

Node localization is one of the key technologies in the wireless sensor network research field, which is crucial to the high-accuracy localization of mobile nodes, but the positioning error of traditional algorithms such as received signal strength indicator and angle of arrival is more than 4 m, which has almost no practical value. For example, the localization accuracy of the localization algorithm based on received signal strength indicator will be reduced sharply when affected by signal reflection, multipath propagation, and other interference factors. To solve the problem, a three-dimensional localization algorithm of mobile nodes was proposed in this article based on received signal strength indicator–angle of arrival and least-squares support-vector regression, which fused the ranging information of received signal strength indicator algorithm and the angle of arrival algorithm and optimized the estimated distance of unknown nodes. Next, the mobile node model and least-squares support-vector regression modeling mechanism were built according to the hop count of the shortest distance between nodes. Finally, the unknown mobile nodes were localized based on least-squares support-vector regression modeling. The experimental results showed that compared with the localization algorithms without optimized ranging information or least-squares support-vector regression modeling, the algorithm proposed in this study exhibited significantly improved stability, a reduced mean localization error by more than 50%, and increased localization accuracy.

A Human Location Prediction-Based Routing Protocol in Mobile Crowdsensing-Based Urban Sensor Networks

Mobile crowdsensing (MCS) has recently emerged as an urban-sensing paradigm that takes advantage of smartphone sensing capabilities and user mobility. A major challenge in mobile crowdsensing-based urban sensor networks is how to efficiently transfer data from sensors to the sink (e.g., the server center). Therefore, this study proposes a human location prediction-based routing protocol (HLPRP) in such networks. Specifically, a human location prediction (HLP) model is designed to estimate the location of mobile nodes. The proposed HLP model is based on a recurrent neural network with long short-term memory cells. The movement history of each person is used in the HLP model to predict their future locations. Experimental results on real traces are used to validate the proposed HLP model. Then, using predicted location information from the HLP model, packet delivery predictability is obtained. Packet delivery predictability represents the possibility that a node will deliver a packet to its destination and is used to select optimal relay nodes to maximize the packet delivery ratio, minimize the packet delivery cost, and reduce delivery latency. In addition, the proposed routing protocol considers social strength for relay selection. To evaluate the HLPRP, we conduct simulations and compare results with other routing protocols, showing that the HLPRP can outperform existing protocols.

Location detection of the mobile sensor with fingerprinting‐based cascade artificial neural network model using received signal strength indicator in 3D indoor environment

SummaryThe problem of localization of mobile sensor nodes has been extensively studied in the literature in recent years. In any localization technique, the aim is to determine the location of the sensor node of unknown location with low error. In this article, a cascading artificial neural network (ANN)‐based location detection algorithm is proposed, which detects the location of a mobile sensor node in 3D indoor environment. In a 3D indoor environment of 6 × 20 × 3 m3, received signal strength indicator (RSSI) signals were collected using a mobile node with XBee sensor, and a fingerprint database was created. Cascade ANN system was trained using this database. Then, while a mobile node is in any location, RSSIs measured by anchor nodes are given as an input to the cascade ANN system, and the location of the mobile node is determined. Fingerprint steps 1 and 0.5 m were taken, and two applications were carried out in the article. According to the RSSI values taken from 100 different coordinates for the test, the total error was 3216 and 2838 cm, respectively. The average error is 32.16 and 28.38 cm.

Multiple-Model Mobile Localization Based on Unscented Kalman Filter in Mixed Indoor

In this paper, we present an Interactive multiple mode-Unscented Kalman filter (IMM-UKF) algorithm to achieve mobile node location under wireless sensor networks environments. In the IMM structure, UKF and Variational Bayesian Adaptive Method based on UKF are adapted in parallel, which can improve positioning accuracy in the process of line-of sight (LOS) and non-line-of-sight (NLOS) signal state switching. The estimated values by filtering are fused according to the weighting factors to get the estimated positions. Moreover, when NLOS measurement noise covariance change, we propose Variational Bayesian Adaptive Method based on UKF to improve robustness. Both Simulation and experiments illustrate that the propose algorithm performs can achieve competitive localization accuracy.

A Cloud-based Asset Tracking System for Hospitals Using Ultra-wideband Localization

Nosocomial infections are fatal diseases caused by microorganisms in the hospital environment. It has mortality rates of up to 70% in intensive care units. The most important way to fight this infection is to pay attention to the hygiene of the assets in the hospital. Within the scope of this paper, an IoT-based real-time advanced asset tracking system has been implemented. This system consists of three main components: Hand Hygiene Surveillance System, Device Tracking System, and Infection Control System. Providing hand hygiene in hospitals is critical for the prevention of infections. The proposed Hygiene Surveillance System provides hand hygiene control in intensive care units and neonatal services, which are the areas with the highest risk of infections. Received signals from ultrawide-band (UWB) modules are processed by a designed positioning algorithm. While competitors are insufficient in many scenarios because of the 1-2 m precision positioning, the proposed system provides a solution in these scenarios with 20-25 cm precision. Antibiotic-resistant nosocomial infection is difficult to treat, contagious, and fatal. These patients are kept in quarantine. During the transportation of these patients, end-to-end hygiene control is required. By using the developed Infection Control System, these patients will be followed end-to-end in real-time. Our system consists 3 main layers. In the first layer, the distributed IoT devices in the environment collect the broadcasted UWB and Bluetooth Low Energy (BLE) signals from the mobile nodes. The strength of the received signals is sent to the second layer which is responsible for calculating the location of mobile nodes based on the received signals from different anchor nodes. The detected locations are sent to the thir layer which provide the real time tracking system for the web and mobile applications. In this layer different technologies such as JSON, Ajax, Server Sent Events, WebSockets, and RestFul technologies are used to deliver the real time location data to the users.

Inertial optimization MCL deep mine localization algorithm based on grey prediction and artificial bee colony

To solve the problem that the existing Monte Carlo Localization (MCL) algorithm has long localization time and large localization error in the real-time localization of downhole personnel and mobile equipment, an inertial optimization MCL deep mine localization algorithm based on gray prediction and artificial bee colony (IMCL-GABC) is proposed. Firstly, the movement speed and direction of the personnel or equipment to be located at the current moment are estimated by the grey prediction model, and the sampling area is determined by combining with the structural characteristics of the deep mine roadway. Secondly, the artificial bee colony algorithm is introduced to optimize the filtering to eliminate the less likely position points and obtain the approximate optimal estimated position sampling set. Finally, the weight of the sample is optimized by motion inertia, so as to complete the localization of the personnel or mobile equipment to be located. The simulation results show that the average localization error of the IMCL-GABC algorithm is 0.46 m and the average localization time required for the node to move one step is 0.21 s. Compared with the other two mobile node localization algorithms MCL and Monte Carlo localization Boxed, the localization error of IMCL-GABC algorithm is reduced by 50% and 37.84% respectively, and the localization time is reduced by 4.6 s and 0.93 s respectively, which proves that IMCL-GABC algorithm effectively improves the localization accuracy and efficiency of downhole personnel and mobile equipment.

Positioning and Ranging Process of RSSI Algorithm Based on ZigBee Technology

Communication and positioning systems in wireless network are merging and promoting each other. Using wireless communication and parameter measurement to determine the location of mobile nodes, node location information can be used to support location services and optimize network management, improve the location service quality and network performance. In various wireless networks, the location algorithm, location technology and location system to obtain the location information of mobile nodes quickly, accurately and in real time have become the current research hotspot. Based on ZigBee technology, RSSI algorithm is used to complete the positioning and ranging from anchor point to label point, and the positioning and ranging method is applied to practical engineering.

V2X-Based Mobile Localization in 3D Wireless Sensor Network

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies.

Monte Carlo Node Localization Based on Improved QUARTE Optimization

Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi‐Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high‐quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

Investigating Mobility-aware Strategies for IoT Services Placement in the Fog under Energy and QoS Constraints

Mobility of Internet of Things (IoT) objects is a key characteristic of IoT environments. It brings dynamicity, uncertainty and raises many challenges when it is associated with computation and network resources management for IoT applications. The resources management problem under objects mobility consideration is even more sensitive if we consider that various IoT applications have stringent Quality of Service (QoS) needs. Fog Computing is a distributed computation paradigm that increases data centers computation and storage abilities with nodes between end-users and the Cloud. Fog computing offers a large distributed infrastructure to support IoT applications needs by bringing services closer to end users. However, Fog infrastructures inherit the energy greediness characteristics of both data centers and network infrastructures. This work investigates the IoT services placement problem in the Fog as an optimization problem to minimize energy consumption and enhance QoS while considering mobility of IoT objects. We model the placement problem as a multi-objective optimization problem and we propose a location history based mobility model (HTM) to estimate future locations of IoT mobile nodes. We propose a framework composed of online strategies for IoT services placement and a Mobility-aware Genetic Algorithm (MGA) for services migrations. We evaluate our strategies through iFogSim simulator and compare the proposed framework to migrations and placement strategies from the literature based on Shortest Access Point migration strategy (SAP) and with Penguins Search Optimization Algorithm (PeSOA). Experiments show that the proposed framework outperforms literature approaches for the considered objectives and for various configurations of the mobile environment.

Time-Difference-of-Arrival-Based Localization Methods of Underwater Mobile Nodes Using Multiple Surface Beacons

In this paper, we investigate the mobile node localization problem in an underwater positioning system using multiple surface beacons, where time synchronization between the surface beacons and underwater mobile nodes is not required. First, we propose a Time-Difference-of-Arrival-based (TDoA-based) node localization method. In the proposed method, as the underwater node receives the positioning messages from more than or equal to three surface beacons belonging to the same grid at one epoch, it calculates the location by itself using the information of received positioning messages and the corresponding arrival times. To deal with the problem that the number of received positioning messages at one epoch by the underwater node is not enough for the location calculation, we present the modified TDoA- based node localization method with buffered messages and the modified TDoA-based node localization method with motion information. The effectiveness of proposed TDoA-based node localization method and two modified methods is validated by simulation results and sea experiment data analytical results. The robustness of the TDoA-based node localization method is improved by two modified methods. Moreover, using the proposed TDoA-based node localization method and two modified methods, the underwater node can localize itself in low energy consumption and passive mode.