Beacon Nodes Research Articles

Weighted Centroid Amorphous Algorithm for Position Error Minimization in WSN

ABSTRACTWireless Sensor Networks (WSNs) have numerous applications, one of which is localization. Localization is crucial for determining the position of unknown sensor nodes in the deployment environment. Localization techniques are broadly classified into two categories: range based and range‐free. Range‐free localization techniques are gaining popularity as they are easy to implement and do not require external hardware. In this proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. Ten nearest beacon nodes are considered to determine the coordinates of a single unknown node. The hop value and hop size of that unknown node are calculated from the 10 nearest beacon nodes, and by using the hop value and hop size, the weight is calculated. After the weight estimation, the weighted sum is calculated, and using the weighted sum, the coordinates of an unknown node are determined. Simulation results indicate that the proposed Weighted Centroid Amorphous method achieves superior accuracy, with rates of 90.41%, 89.57%, and 86.10% compared to the traditional Amorphous, improved Amorphous, and Ensemble approach, respectively.

Multi-Source Information Fusion-Based Localization in Wireless Sensor Networks

In wireless sensor networks, accurate node localization is essential for ensuring the precision of data collection. The DV-Hop algorithm, a popular range-free localization method, estimates distances between nodes by multiplying hop counts with average hop distances obtained through distance vector routing. However, this algorithm often experiences localization errors in randomly distributed network environments due to considerable inaccuracies in average hop distance calculations and the approximation of actual paths by straight-line paths. This paper introduces an enhanced DV-Hop localization algorithm, which constructs a mathematical model to optimize the mean square error of the average hop distance for any anchor node. This optimization corrects the average hop distance across the network, bringing it closer to the actual value, thus reducing errors and enhancing accuracy. Simulation results indicate that with 150 nodes, a 30% beacon node ratio and a 100-m communication range, the localization error of the improved FuncDV-Hop model decreased from 0.3916 to 0.1705, and the Root Mean Square Error (RMSE) decreased from 24.78[Formula: see text]m to 14.39[Formula: see text]m, thereby improving localization accuracy by 56.46%.

INS/UWB multi-sensor integrated localization scheme in indoor LOS/NLOS environment based on MEF-GPDAF

Localization technology is crucial for indoor robot navigation. Wireless sensor network (WSN) serves as a significant part in indoor localization. However, the intricate indoor conditions make signal propagation susceptible to interference from obstructions, resulting in a decrease in positioning accuracy. Inertial Navigation System (INS) operates as a self-governing navigational setup undisturbed by indoor environment, and its location determinations remain unaltered by non-line-of-sight (NLOS) conditions. This research presents a combined positioning method of Ultra-wide band (UWB) and INS, which inherits advantages of the two subsystems and elevates the positioning system performance . On the basis of INS autonomous positioning, the maximum entropy fuzzy generalized probability data association filter (MEF-GPDAF) is used to modify the INS positioning results by combining the position of beacon nodes. When using UWB to locate the mobile node, a NLOS mitigation algorithm is applied to lessen the influence of NLOS propagation. Simulation experiment results demonstrate that, when compared to existing algorithm, MEF-GPDAF exhibits superior positioning precision.Meanwhile, in the real environment, the average error of the algorithm of MEF-GPDAF was measured to be 0.4961 m, which improved its accuracy by 32.88 % compared with the latest algorithm.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and the Weighted Centroid Method.

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b.

Localization in 3D Wireless Sensor Networks with Obstacle Consideration

Wireless sensor network (WSN) refers to the network formed by sensor nodes for communicating sensed data over the wireless medium. These sensor nodes are mostly deployed randomly in the target area, and hence estimating the coordinates of these sensor nodes through the localization process is an important activity. Majority of the localization algorithms existing in the literature assume the target area to be an obstacle-free 2D terrain. However, such algorithms are not suitable for real-life scenarios where WSNs are actually deployed on 3D terrains that may also have obstacles that hinder the radio signals from the deployed sensor nodes. Hence, factors such as the height of the terrain, the presence of obstacles, etc., require significant considerations while designing algorithms for WSN. The proposed research presents a localization scheme for 3D WSN where the sensor nodes are randomly deployed in a 3D area having large obstacles. The proposed scheme adopts a distributed approach for calculating the virtual coordinates of the sensors using four beacon nodes. The deployment area is divided into regions, and each node computes its virtual coordinates with respect to the obstacle. Simulation results indicate an average localization error of 6.93 m. The proposed scheme requires very less computational effort and can be easily adapted in different scenarios.

Optimizing Sensor Localization and Cluster Performance in Wireless Sensor Networks through Internet of Thing (IoT) and Boosted Weight Centroid Algorithm

Localization is an extremely important component of applications that make use of wireless sensor networks. It has a substantial impact on academics as well as real-time sensor deployment applications in the aim of lowering the amount of energy that is used while simultaneously locating unknown nodes. The process of obtaining the coordinates along an axis that represent the locations of the sensor nodes is referred to as localization. The accuracy of locating the positions of the nodes varies depending on the environmental conditions, the type of nodes, the type of application, and the type of localization methods used. A standard localization method known as distance vector hop (DV-hop) localization will be able to determine the positions of unknown nodes with typical accuracy with the assistance of beacon nodes based on Internet of things. The DV-hop and improved weighted centroid localization algorithms, in addition to the suggested boosted weight centroid-based localization approach, are both addressed in this article. The suggested boosted weight centroid localization technique is utilized to find nodes in the remote area of the WSN while conserving energy. This is accomplished with the assistance of measurements involving both the nodes and the centroid. The modified weight metric is utilized in the process of carrying out the task of localisation of an unknown node. The performance of BWCLA is evaluated based on a number of different metrics, including accuracy in localization, average localization error, total packets utilized, and energy usage.

Improved DV-Hop model based on the application of variable scenarios

This work proposes an improved DV-Hop model based on function analysis and simulation parameter determination (named FuncDV-Hop) to address the problems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step function segmentation experiment, weight function strategy with equivalent points, and maximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distance estimation, and least square calculation of the DV-Hop model. The experiment is designed using the control variable method. The total number of nodes, the ratio of beacon nodes, the communication radius, the number of beacon nodes, and the number of unknown nodes are designed as variables to control the experiment. Finally, the experiments of two stages of simulation parameter determination and integration optimization are carried out. The simulation positioning error of all scenarios is reduced by 0.0630–0.2472, and the positioning accuracy is increased by 23.81%–75.85%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios compared with the other models, the localization error is reduced by more than 0.1, and the optimization rate is increased by more than 10% in the record parameters of the existing wireless sensor network system.

Research on the optimization of the innovation path of university education and teaching management based on the weighted function algorithm of RFID technology

Abstract College education and teaching management are experiencing a transition from traditional modes to informational modes. This paper constructs a management mode for university teaching informatization based on RFID technology and designs the workflow and functions for RFID. For the positioning of student RFID tags on campus, a time-gap ALOHA algorithm is proposed to reduce the collision rate by dividing the processing time of student tags into several time gaps. After obtaining the student tags, an improved K-weighted center-of-mass localization algorithm is proposed for multi-point localization, which makes the weight of multiple known nodes in the process of operation more reasonably distributed through the setting of the weight size. In the simulation test of campus student localization, when the number of beacon nodes is 40, the average error of the K-weighted prime is 6.5%, 4.25%, and 2.1% lower than that of the prime algorithm, weighted prime, and weighted differential prime, respectively.

Analysis of improved particle swarm algorithm in wireless sensor network localization

Abstract Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology, which is also a hot spot and focus of research at this stage. In this paper, firstly, we propose a classical ion swarm localization algorithm for wireless sensor network localization, randomly assigning appropriate velocity and position to each particle in the population in order to find the global optimum in the iterative process. Based on this, a network node localization model is established to convert the functional optimization problem with constraints into an unconstrained optimization problem to solve. At the same time, the space is searched using a chaotic search strategy, which greatly improves the search efficiency. Next, the particle swarm algorithm is further optimized, and simulation experiments are set up using MATLAB software to do comparison experiments on the PSOAPF algorithm and other algorithms. The experimental results show that when the density of beacon nodes is 40%, the average localization error of the PSOAPF algorithm is 14.26%, with the smoothest decreasing trend. When the percentage of anchor nodes is 10%, the localization error is reduced by 6.89%, and the localization accuracy of the PSOAPF algorithm is also higher than other models. This study shows that the improved particle swarm algorithm can effectively improve the localization accuracy, reduce error and accelerate the convergence speed in wireless sensing network localization.

Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

A positioning algorithm based on improved robust extended Kalman filter with NLOS identification and mitigation

With the development of the information age and the maturity of Internet of Things technology, wireless sensor network has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) propagation in complicated environment and the inherent noise of the sensor will introduce errors in the measurements, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on the mean reconstruction method is proposed, which reconstructs the distance measurements from all beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is not too large. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. To make the positioning results more accurate, hypothesis test is used as NLOS identification to classify the position estimates generated from all distance combinations by least-squares. Then, the residual weighting (RWGH) method is utilized to combine the position estimates that fall into the validation region. At last, we merge the results from RWGH and REKF. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.

Comparison between an RSSI- and an MCPD-Based BLE Indoor Localization System

IPS is a crucial technology that enables medical staff and hospital management to accurately locate and track persons or assets inside medical buildings. Among other technologies, readily available BLE can be exploited to achieve an energy-efficient and low-cost solution. This work presents the design, implementation and comparison of a RSSI-based and a MCPD-based indoor localization system. The implementation is based on a lightweight wkNN algorithm that processes RSSI and MCPD distance data from connection-less BLE Beacons. The designed hardware and firmware are implemented around the state-of-the-art SoC for BLE, the nRF5340 from Nordic Semiconductor. Experimental evaluation with real-time data processing has been evaluated and presented in a 7.3 m × 8.9 m room with furniture and six beacon nodes. The experimental results on randomly chosen validation points within the room show an average error of only 0.50 m for the MCPD approach, whereas the RSSI approach achieved an error of 1.39 m.

DV-Hop Location Algorithm Based on RSSI Correction

To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and the benchmark RSSI value, divides the hops continuously, calculates the average hop distance according to the Minimum Mean Square Error (MMSE) criterion of the best index based on the quantized hops, and then adds hop distance matching factor to the fitness function of each anchor node into the calculation of the hop distance fitness function to weight the fitness function. The change index value is introduced to obtain more accurate hop distance value, and then the estimation error of unknown node (UN) coordinate is modified by using the distance relationship between the UN and the nearest beacon node (BN), and the modified coordination position is further modified by using the triangle centroid to improve the accuracy of node positioning in the irregular network. The experimental results show that compared with the original DV-Hop, improved DV-Hop1, improved DV-Hop2 and improved DV-Hop3, the localization error of the improved algorithm in this paper is reduced by 58%, 45%, 34%, and 29%, respectively, on average, in the two network environments. Without increasing the hardware cost and energy consumption, the improved algorithm has excellent localization performance.

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, K -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.

Securing Age-of-Information (AoI)-Enabled 5G Smart Warehouse Using Access Control Scheme

Low-power wireless sensor networks (WSNs) and Internet of Things (IoT) have great impact for the real-time applications in future 5th generation (5G) mobile networks due to the wireless-powered communication technologies. The Age of Information (AoI) plays a crucial performance metric in an IoT-enabled real-time smart warehouse application, where the freshness of the aggregated data is very important. However, wireless medium communication among the beacon nodes and the user equipments (tracking nodes) gives an opportunity to an adversary not only to eavesdrop the data but also to corrupt the data by means of deleting, modifying or inserting malicious information during communication among the entities involved in the smart warehouse environment. To mitigate these issues, we design a security scheme for AoI-enabled 5G smart warehouse through an access control mechanism, where the secure communication among the beacon nodes and the tracking nodes will take place by mutual device authentication and key agreement process. The fresh data collected at the enterprise cloud is then used for big data analytics for better predictions and analysis, such as optimal device scheduling so that the data becomes very fresh. The rigorous security analysis and comparative study show that the proposed mechanism has significantly better security and comparable communication and computational costs as compared to the relevant schemes. In addition, through the real-time testbed experiments, we show that the proposed scheme is practical in 5G smart warehouse context.

Trust-Based Beacon Node Localization Algorithm for Underwater Networks by Exploiting Nature Inspired Meta-Heuristic Strategies

Conventional underwater technologies were not able to provide authentication and proper visualization of unexplored ocean areas to accommodate a wide range of applications. The aforesaid technologies face several challenges including decentralization, beacon node localization (for identification of nodes), authentication of Internet of Underwater Things (IoUTs) objects and unreliable beacon node communication between purpose oriented IoT-enabled networks. Recently, new technologies such as blockchain (BC) and the IoUTs have been used to reduce the issues but there are still some research gaps; for example, unreliable beacon messages for node acquisition have significant impacts on node identification and localization and many constrained node resources, etc. Further, the uncertainty of acoustic communication and the environment itself become problems when designing a trust-based framework for the IoUTs. In this research, a trust-based hybrid BC-enabled beacon node localization (THBNL) framework is proposed to employ a secure strategy for beacon node localization (BNL) to mine the underwater localized nodes via the hybrid blockchain enabled beacon node localization (HB2NL) algorithm. This framework helps to merge two disciplines; it is hybrid because it follows the nature and bio inspired meta heuristics algorithms for scheduling the beacon nodes. The performance of the proposed approach is also evaluated for different factors such as node losses, packet delivery ratios, residual and energy consumption and waiting time analysis, etc. These findings show that the work done so far has been successful in achieving the required goals while remaining within the system parameters.

EMBN-MANET: A method to Eliminating Malicious Beacon Nodes in Ultra-Wideband (UWB) based Mobile Ad-Hoc Network

Recent breakthroughs in microelectromechanical systems, microsystem technologies, and wireless communications have made energy-efficient, inexpensive, off-the-shelf devices that unambiguously carry data without communication overhead practical. UWB radio technology employs minimal energy for short-range, high-bandwidth communications throughout a large section of the radio. Mobile computing organizations now explore mobile ad-hoc networks. Decentralized networks communicate. Peer nodes enable wireless device-to-device communication. Location-based and location-aided routing improve ad hoc routing. Physical or involuntary configuration places mobile nodes during deployment. Large networks need too much manual setup. GPS receivers for each sensor unit would significantly increase network installation costs. It inspired non-GPS and non-physical configuration localization. Most localization systems use beacon nodes. Beacon nodes are unique. Standard security approaches block secure beacon nodes, which might compromise localization. This research investigates beacon node localization issues and attacks. We constructed a statistical model to detect rogue beacon nodes in the ad hoc network and studied how various attacks affect localization. Bogus beacon nodes find mobile nodes. We enabled malicious beacon node communication. Trilateration and Received Signal Strength Indicator found unknown nodes. Trilateration detects compromised beacon nodes. The algorithm finds malicious beacon nodes. Our experiments removed 90% of compromised beacon nodes. Our flexible localization technique allows several algorithms and range methods without time complexity. Location identification affected fake beacon nodes.

Improvement on the DV-hop localization algorithm based on average hop distance optimization

Wireless sensor networks, which have numerous uses in numerous industries, have attracted more attention over the past few years. Node localization is one of the gordian technology to locate in it. This research sought to address the low precision of the classic DV-hop algorithm in the context of wireless sensor networks. This novel DV-hop localization algorithm is given some practical solutions in this paper. The results of the simulations demonstrate that the new method can improve accuracy and increase communication range. First of all, the least squares method is applied to correct the average length of each hop between beacon nodes. In addition, an error distance matrix is defined as an intermediate variable to reckon the average length between the unknown node and the beacon node for each jump and select the minor error distance matrix. Finally, this improved algorithm chooses the minimum error distance matrix from the previous steps and modifies it. Simulation consequences reveal that this modified algorithm can better enhance the accuracy and extend the communication range.

An Indoor Tracking Algorithm Based on Particle Filter and Nearest Neighbor Data Fusion for Wireless Sensor Networks

Wireless indoor localization technology is a hot research field at present. Its basic principle is to estimate the geometric position of the mobile node by measuring the characteristic parameters of the propagation signal between the mobile node and the beacon node. However, in the process of position estimation, there are non-line-of-sight errors such as multipath propagation, which greatly reduces the localization accuracy. This paper proposes an enhanced closest neighbor data association approach based on ultra-wide band (UWB) measurement. First, the measured values were grouped to obtain a series of undetermined prediction position points, and the undetermined points were put into our set verification gate for screening. Then, the particle filter was introduced to weight and redistribute the position estimation after screening, removing the NLOS-contaminated location estimation from consideration. The position estimation group with low error was finally confirmed and weighted again by the nearest neighbor association algorithm. Simulation results showed that the average localization accuracy of the proposed method was about 1 m. Compared with the existing localization algorithms, the proposed method can successfully reduce the influence of NLOS error and obtain higher localization accuracy.