Beacon Nodes Research Articles

Indoor AR Navigation and Emergency Evacuation System Based on Machine Learning and IoT Technologies

In order to evacuate people safely and quickly in indoor disaster environments, it is necessary to estimate the current location of the individuals, detect disaster situations, predict disaster propagation, derive optimal individual escape paths, and implement a user-friendly and intuitive guidance system. In this study, we propose a machine-learning-based indoor augmented reality (AR) navigation and emergency evacuation system that can guide an optimal escape path for individual users. To detect emergency events and deliver sensing data, an Internet of Things (IoT)-enabled ad hoc network is considered. To deliver the sensing data safely and reliably to the server, we present a hybrid reinforcement-learning-based routing algorithm that combines direct and indirect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning methods. Prediction of disaster propagation at multiple time scales is important to prevent dangerous situations. We propose a simple disaster area prediction method that ensembles elementary component gradient boosting machine models. User location is estimated by a deep neural network using the received signal strength from beacon nodes. To derive the optimum evacuation path for each individual, we propose a novel model-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning method, in which we consider the building structural model and disaster context information. The performance of the proposed system is experimentally evaluated for various disaster scenarios.

An ensemble approach for improving localization accuracy in wireless sensor network

A wireless sensor network (WSN) consists of many small, low cost and less computational power sensor nodes. These nodes are uniformly or randomly deployed for gathering vital information from the environment. It is crucial to identify the exact and accurate position of the sensor node as it helps in efficient communication between unknown and known (beacon) nodes. Localization has many applications, such as rescue, traffic controlling and monitoring, underwater cultivation, surveillance, and target tracking. It is also used in day-to-day life in the form of GPS to guide people in their travel. Hence, to avail of the accurate service from localization, the exact position of the sensor is needed. In this work, an ensemble approach is proposed using both DV-Hop and a weighted amorphous algorithm to enhance localization accuracy. Two distance measurements are calculated to obtain the distance from an unknown node to the beacon node by considering hop value and size. Finally, the probabilistic distance estimation is applied to the obtained distances to get the actual distance. Proposed approach is compared with the traditional amorphous and three other improved amorphous algorithms and provides higher accuracy in terms of MAE, MSE, and RMSE.

Improved Wireless Sensor Network Localization Algorithm Based on Selective Opposition Class Topper Optimization (SOCTO)

One of the most important challenges in wireless sensor networks is the issue of localization. Furthermore, it is critical to monitor and evaluate the data gathered. With the aid of a collection of constructed nodes known as beacons, the localization technique can measure node location. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the distance vector-hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. Based on a proposed selective opposition class topper optimization (SOCTO), an enhanced DV-Hop localization algorithm is developed. It also focuses on an optimised formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error within the estimated distance between the beacon and the dumb node, due to improved localization accuracy. The range-free localization algorithm in uniform multi-hop wireless sensor networks is the subject of a recent study. The results show that our proposed method outperforms the DV-Hop technique and related techniques in terms of average localization error (\(0.05\%\)) versus random beacon node deployment, average localization error (\(0.04\%\)) versus circular beacon node deployment, and average localization error (\(0.08\%\)) versus spiral beacon node deployment.

Light-Emitting Diode-Based Optical Localization of a Robot in Continuous Motion Using Dynamic Prediction

Abstract For mobile robots, localization is essential for navigation and spatial correlation of its collected data. However, localization in Global Positioning System-denied environments such as underwater has been challenging. Light-emitting diode (LED)-based optical localization has been proposed in the literature, where the bearing angles extracted from the line-of-sight of the robot viewed from a pair of base nodes (also known as beacon nodes) are used to triangulate the position of the robot. The state-of-the-art in this approach uses a stop-and-go motion for the robot in order to ensure an accurate position measurement, which severely limits the mobility of the robot. This work presents an LED-based optical localization scheme for a mobile robot undergoing continuous motion, despite the two angles in each measurement cycle being captured at different locations of the robot. In particular, the bearing angle measurements are captured by the robot one at a time and are properly correlated with respect to the base nodes by utilizing the velocity prediction from Kalman filtering. The proposed system is evaluated in simulation and experiments, with its performance compared to the traditional state-of-the-art approach where the two angle measurements in each cycle are used directly to compute the position of the robot. In particular, the experimental results show that the average position and velocity estimation errors are reduced by 55% and 38%, respectively, when comparing the proposed method to the state-of-the-art.

Multiple objective optimization-based DV-Hop localization for spiral deployed wireless sensor networks using Non-inertial Opposition-based Class Topper Optimization (NOCTO)

The problem of localization is one of most important issues in wireless sensor networks. Furthermore, it is critical to monitor and evaluate the data gathered. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the Distance Vector-Hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. A new Non-inertial Opposition based Class Topper Optimization (NOCTO) based enhanced DV-Hop localization algorithm is proposed. It also focuses through an optimized formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error with estimated distance between the beacon and the dumb node, due to improved localization accuracy. For spiral deployed 2D wireless sensor networks, this paper proposes a multi-objective NOCTO-based DV-Hop localization. The simulation results indicate that our suggested multi-objective function outperforms some existing techniques.

Hybrid optimization for fault‐tolerant and accurate localization in mobility assisted underwater wireless sensor networks

SummaryMobility prediction and fault tolerance are extremely difficult due to underwater characteristics. Energy drain is one of the major causes for node faults. Hence, in this research article, a hybrid optimization algorithm is developed for fault‐tolerant and accurate localization in UWSN. In this technique, Artificial Butterfly Optimization (ABO) algorithm is applied for finding the distance between the anchors and the sensors. Each non‐localized node runs ABO algorithm for finding the distance amid the anchor or beacon nodes. Then, applying Quaternion‐based Backtracking Search Optimization (QBSA) algorithm, non‐localized sensor nodes are localized and to decrease the localization error based on the Received Signal Strength Indicator (RSSI), battery energy, and distance parameters. Aqua‐Sim a tool kit of NS2 is an open‐source software developed for Underwater Wireless Sensor Network research, and this proposed algorithm will be implemented using this software. By simulation results, it is shown that the proposed optimization algorithm reduces the localization error, latency, and cost and energy consumption.

Performance analysis and optimization of energy harvesting cognitive multi-hop relay network over mixed Rayleigh and double-Rayleigh fading channels

In this paper, we investigate the outage performance of an energy harvesting underlay cognitive multi-hop relay network. In the network, energy-constrained secondary network (SN) nodes harvest energy from radio frequency signal of a power beacon (PB) node. The source node transmits information to destination node via multiple intermediate relay nodes by using a time division broadcast protocol, and the hardware impairments of SN nodes’ transceiver are modeled. The outage performance of SN is analyzed and evaluated by accurately approximate and asymptotic closed-form expressions of outage probability (OP) over quasi-static mixed Rayleigh and double-Rayleigh fading channels. Additionally, due to the complexity of OP expression, a chaotic dragonfly algorithm (CDA) is proposed to jointly optimize energy harvesting ratio and PB node’s abscissa, which can achieve OP minimization of SN. Extensive simulations demonstrate the correctness of theoretical analysis and the effectiveness of the proposed CDA.

Improved DV-Hop Algorithm Based on Swarm Intelligence for AI and IoT-Federated Applications in Industry 4.0

In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm’s hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles’ velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles’ movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0.

An evolved algorithm for underwater acoustic sensor node localization enhancement using reference node

Accurate localization of underwater acoustic sensor networks is one of the essential operations. The range-free DV-Hop technique is more acceptable due to its low hardware cost and simplicity than the range-based approach. But the main drawback of this basic DV-Hop method is low accuracy and link failure due to maintenance or short battery life during the continuous localization calculation in the underwater scenario. Most algorithms in this field do not pay enough attention to the mobility of the nodes. By examining the movement patterns of water, this manuscript uses a technique for UWSN localization based on a mobility prediction algorithm. This manuscript proposed a reference node-based multi-hop improved DV-Hop​ technique using School Topper Optimization (STO) and mobility prediction algorithms. This proposed scheme’s highlighted advantage is to reduce the localization error to identify the unknown target nodes using a reference node-based localization scheme using a mobility prediction algorithm without increasing the beacon nodes, and the multi-hop improved DV-Hop​ algorithm using STO significantly improved the link failure problem by iteratively selecting the different sets of the beacon nodes in the same scenario The simulation report has proved that our framework takes better computational time, has a better convergence rate by introducing the STO technique, provides 23.76% and 37.18% better avg. localization error and localization error variance, and 16.12% better coverage from PSODV-Hop and GSODV-Hop techniques.

Monitoring and Locating Oceanic Sailors Through Wireless Sensor Network

A systematic oceanographic monitoring and management requires an effective marine tracking system. It helps the localization of different marine assets, their border control and in encountering emergency situations. Thus, a reliable marine tracking system is always required to ensure a safe marine environment for shippers, fishers, sailors and other marine assets. Nowadays due to the evolution in wireless sensor network (WSN) technology, their easy and inexpensive deployment can be seen in different fields such as agriculture, medical, industries and IoT. The implementation of WSN in the marine environment comes with various issues due to the complex behavior of the marine environment and thus affects the reliability of the system. In this paper, a WSN-based marine tracking protocol sailing is presented, which identifies the location of marine assets reliably through the genetic firefly (GEFIR) algorithm. The system comprises sensor nodes, sink nodes, server-client terminals and a base station. The protocol uses the application and contact layers to create a group of beacon nodes (boats) based on the geographical location to conserve energy and increase data reliability. GEFIR algorithm is implemented to give a precise and more convenient data to the WSN for the mariners. The results show a better performance for latency calculation through the WSN- and GEFIR-based sailing protocol. The system generates the alert to nodes (sailors) in case they are moving out from a specified region and sends their location for further communication.

An Enhanced DV-Hop Localization Scheme Based on Weighted Iteration and Optimal Beacon Set

Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning performance of the standard DV-Hop, an enhanced DV-Hop based on weighted iteration and optimal beacon set is presented in this paper. Firstly, different weights are assigned to beacons based on the per-hop error, and the weighted minimum mean square error (MMSE) is performed iteratively to find the optimal average hop size (AHS) of beacon nodes. After that, the approach of estimating the distance between unknown nodes and beacons is redefined. Finally, considering the influence of beacon nodes with different distances to the unknown node, the nearest beacon nodes are given priority to compute the node position. The optimal coordinates of the unknown nodes are determined by the best beacon set derived from a grouping strategy, rather than all beacons directly participating in localization. Simulation results demonstrate that the average localization error of our proposed DV-Hop reaches about 3.96 m, which is significantly lower than the 9.05 m, 7.25 m, and 5.62 m of the standard DV-Hop, PSO DV-Hop, and Selective 3-Anchor DV-Hop.

Node localization algorithm for detecting malicious nodes to prevent connection failures and improve end-to-end delay

In sensor network applications, the measured data makes sense only if the location is known. The location of the node is known, and the wireless communication line and transmission method between the two nodes can be an unknown collaboration process where a sensor with a known location is used to locate the node in an unknown location. Various localization algorithms are available that use linear and nonlinear methods to find nodes. Security is a major issue in localization. After the data is sent from the beacon node to the binding node, an error occurs because the virus-safe or unsafe mobile beacon acts like the original mobile beacon, sending the wrong message. One of the basic problems of wireless sensor networks is the localization and security of nodes. This article analyses the performance of optimization algorithms like Ant Colony Optimization algorithms, opportunity routing algorithms, and the proposed Buffalo optimization algorithm. The performance of the buffalo algorithm is compared with these optimization algorithms. Performance metrics such as throughput, residual energy, packet forwarding rate, packet loss rate, delay, and cost were measured. Then the algorithm is implemented as a hardware system for measuring system performance in real-time. Based on a detailed analysis of the performance of optimization algorithms in wireless sensor networks, the buffalo algorithm provides higher efficiency and performance in wireless sensor network applications.

An Enhanced DV-Hop Positioning Scheme Based on Spring Model and Reliable Beacon Node Set

How to get the node position, as a hotter topic in the field of wireless communication, is the primary problem to be solved in wireless sensor networks (WSNs). The DV-Hop positioning scheme is a kind of range-free positioning scheme, which has been favored by researchers in recent years. However, the original DV-Hop scheme faces the shortcoming of low positioning precision and stability. To ameliorate the positioning capability of the DV-Hop, an enhanced DV-Hop scheme based on the spring model and reliable beacon node set has been presented in this paper. Firstly, the relationship between nodes in the network is abstracted as a spring model, and the distance between unknown nodes and beacon nodes is corrected by comprehensively considering the influence of each beacon node from the physical phenomenon of force vector decomposition. After that, based on the random sample consensus (RANSAC) algorithm, the reliable beacon nodes with smaller distance deviation to the unknown node are picked out to compute the unknown node coordinates, rather than all beacon nodes participating in the calculation. Finally, extensive experiments have been conducted on distance accuracy and positioning performance. Simulation results demonstrate that our proposed positioning scheme outperforms better than original DV-Hop and other existing improved schemes under different scenarios.

Path planning mechanism for mobile anchor-assisted localization in wireless sensor networks

The detection of a deployed sensor node location plays a vital role in all domains of Wireless Sensor Networks since its location provides the source of the information. Many approaches have been proposed in recent years. One of these approaches uses a single location-aware node, known as the Anchor or Beacon, that exchanges information with other nodes to help in their position estimation. Although this approach provides good accuracy, it creates new challenges in the picture. The key issue is to design an optimal trajectory for the anchor node in order to achieve maximum localization accuracy in the shortest amount of time. In this paper, we propose a static trajectory for a mobile beacon node for localization in WSNs. This trajectory ensures that the localization success ratio can be improved for lower communication range of an anchor node with higher localization precision and minimum localization time as compared to other static models. The proposed trajectory also overcomes the collinearity problem and ensures that all the unknown sensor nodes receive good quality beacon positions for position estimation.

Improvement of triangle centroid localization algorithm based on PIT criterion (ITCL-PIT) for WSNs

One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error.

A Comprehensive Study on Security in Wireless Sensor Networks (WSNs).

Wireless Sensor Networks(WSNs) are an important component of today's ubiquitous and pervasive computing. Without WSNs, the applications aren't as clever as they could be. Almost every scenario involving WSNs necessitates a quick and precise localization process. Existing frameworks and algorithms, on the other hand suffer from a significant disadvantage when it comes to beacon node trust, which is a critical component in Wireless Sensor Network. For localization this has to be ensured.. This issue is addressed in our current solution. In the harsh environment of WSN operations, malicious nodes are inescapable. As a consequence, A technique has been proposed to find out the problem while simultaneously offering a safe trust based localization system. It focuses on the algorithm for assessing trust and the creation of blockchains. Every beacon node’s truth value(trust value) are determined using various trust criteria with the corresponding weights being dynamically changed during localization process. After that the most reliable beacon nodes are chosen for mining. This two-step process ensures that the blockchain is kept up to current, and that beacon nodes have consistent Tvalues(Trust values). We conducted a series of simulations to test the suggested algorithm’s performance and effectiveness. The accuracy of localization, harmful activity detection, the confusion matrixes are used to compare results.

A hybrid Harrison Hawk optimization based on differential evolution for the node localization problem in IoT networks

SummaryDespite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.

A TOPSIS-Based Relocalization Algorithm in Wireless Sensor Networks

Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network. When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor. However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed. This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes. The algorithm is divided into four steps. First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing. Second, the entropy method is used to weight the contributions of three indicators. Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model. Finally, using the reliability evaluation model select reliable beacon nodes. Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.

Low beacon node localisation algorithm in sensor networks based on multi-space key distribution algorithm

In the traditional sensor network low beacon node localisation method, it is easy to be attacked by false routing, which seriously affects the localisation accuracy. A location algorithm based on multi - space key distribution algorithm is proposed. By using the key information of multiple key Spaces brought by sensor nodes, the paired keys in the information are accurately calculated. Routing messages are encrypted and authenticated to effectively prevent spurious routing attacks. The hopping number information and distance information between beacon nodes are used as training datasets. The least square method is learned to realise the localisation of sensor network low beacon nodes. Simulation results show that the algorithm can locate low beacon nodes quickly and accurately.

Low beacon node localization algorithm in sensor networks based on multi-space key distribution algorithm

Low beacon node localization algorithm in sensor networks based on multi-space key distribution algorithm