Mobile Beacon Research Articles

Underwater vehicle navigation using bearing measurements from a mobile beacon

Autonomous underwater vehicles enable efficient underwater operations such as mapping, surveillance, and inspection. However, due to the lack of global navigation satellite systems (GNSS) signals, drift in pose estimates will occur over time. Common solutions to this issue are using either stationary or mobile beacons to provide range and possibly bearing measurements to bound the navigation error over time. However, this requires occupying communication bandwidth to get reliable round-trip range measurements. In this paper, we propose a system where a mobile beacon passively listens to communications from the underwater vehicle to estimate the position of the vehicle. We show that the estimation error is bounded over time through strategic movement of the mobile beacon.

Preliminary Mobile Beacon Application Framework for Higher Education Co-Curricular Engagement

Mobile beacon application leverages the beacon technology as location-based or proximity sensors to notify or interact with user via mobile apps. There are many instances of these applications especially as an engagement platform across many domains like marketing, retails, exhibitions, or public services. Mobile beacon application holds potential as an engagement tool for empowering co-curricular engagement in higher education. Integrated with personalized mobile notification, users can be notified and interact with co-curricular events within their proximity in the campus. However, literature indicates limited insights on the design applicability and empirical studies of mobile beacon application for higher education context. This new application context poses its own unique challenges, but imperative in helping researchers to discover the extents of mobile beacon potentials. This concept paper introduces a preliminary mobile beacon application framework for supporting co-curricular engagement in higher education. The framework aims to enable design applicability of a mobile beacon application integrated with personalized notification for education engagement purposes. Characterization of mobile beacon application components along with the deliberation of mobile notification features are highlighted in the framework. The presented conceptual framework offers the foundation for application designers in building a mobile beacon application for higher education engagement.

Optimal Maneuvering for Autonomous Vehicle Self-Localization.

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.

Node topology location method of SDN social network based on ant colony algorithm

Due to the convergence defect of the existing SDN social network node topology location methods, there is a large node topology location error problem. In order to solve the above problems, this paper proposes a topology location method of SDN social network nodes based on ant colony algorithm. Firstly, the ant colony algorithm is introduced, and then the biological principle of ant colony algorithm is analyzed to determine the basic steps of ant colony algorithm. According to the number of mobile beacon launch positions and specific launch coordinates in ROI, ant colony algorithm is introduced into the mobile beacon path acquisition program to realize SDN node topology location based on ant colony algorithm. The experimental results show that under the background of node communication radius of 2.0 and 5.0, the node topology positioning errors of this method are small, and the minimum errors are 9.10% and 5.15% respectively. It is fully proved that this method has good node topology positioning effect.

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.

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.

An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT

In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.

An Improved Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight

Increasing the location accuracy of the objects in the Indoor Positioning System (IPS) has grasped great attention lately. With the recent developments in the fields of smartphones and mobile beacons, the accuracy of the IPS has achieved accuracy with less than a meter of accuracy. In this paper, we proposed and developed a Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator using Kalman filter. The developed algorithm takes the distance and the smoothed RSSI values between beacon nodes into consideration. We employ Kalman filtering on the RSSI measurements of the Beacon signals before applying FRBW algorithm. The developed FRBW algorithm was applied and validated in indoor environment using Bluetooth Low Energy beacons and the experimental results achieves accuracy of a few centimeters localization using cost-effective and easy to deploy beacons.

Localization in Wireless Sensor Networks with Mobile Anchor Node Path Planning Mechanism

Determining the accurate locations of randomly deployed sensors in a given geographical region is a critical problem in the field of Wireless Sensor Networks. Various approaches have been proposed in the past to tackle this problem. Most of the existing techniques rely on the use of a beacon node which traverses throughout the region with limited power sources. Sensors use the beacon node positions for self localization. The trajectory of the beacon node has an influence on the accuracy, time and efficiency of the localization algorithm. In this paper, we propose Cosine Rule based Localization (CRL) algorithm for static trajectories of the mobile beacon node. Existing schemes use trilateration for position estimation of sensor nodes. The proposed method uses the Cosine rule on received beacon positions and distances obtained from RSSI in such a way that lines representing distances intersect at one point for the application of the Cosine rule. Simulation results reveal that CRL enables the unknown sensors to locate themselves with better accuracy in comparison with the existing trilateration technique for all the trajectories.

Time-Synchronization Free Localization Scheme with Mobility Prediction for UAWSNs

Background: Underwater Acoustic Wireless Sensor Network supports a lot of civil and military applications. It has come out as an effective tool to explore the ocean area of the earth. Sensor deployed underwater can help to relate the events occurring underwater with the rest of the world. To achieve the goal, the information gained from the sensors needs to be tagged with their real-time locations. Objective: To study the effect of the mobility of the sensor nodes on the accuracy of the location estimation during the localization period and develop a localization scheme which can give an accurate result by predicting the mobility behavior of the sensors. Methods: In this paper, a time-synchronization free localization scheme for underwater networks has been presented. The scheme employs a mobile beacon in the network to move vertically and broadcast the beacon messages. Results: The performance evaluation shows that the scheme reduces the error in location estimation caused by the mobility of the sensors by predicting their further location according to the mobility pattern of the sensor node. In an existing localization scheme, nodes are localized without time-synchronization but the scheme does not consider the mobility of the senor between the reception of two messages. The result of shows that the proposed localization scheme has achieved success in reducing the localization error by introducing the mobility behavior of the sensors in the existing localization scheme. Conclusion: localization scheme without a need of time-synchronization is presented. The main reason of the inaccuracy of a localization scheme is the mobility of the sensor node during the range estimation. The accuracy of a localization scheme can be improved by prediction the mobility pattern of the sensor during a localization period.

Geometric least square curve fitting method for localization of wireless sensor network

In a wireless sensor network, the localized sensors are essential for efficient management of the network. In recent years, we have seen the implementation of various localization schemes. Few schemes presume to use mobile beacon because it has many potentialities. Despite many potentialities, the mobile beacon-based localization schemes suffer from two significant problems. First, the shorter beaconing interval increases the communication overhead and degrades the network lifetime. Second, the radio propagation irregularity minimizes the uniform coverage. For a better network lifetime, we need to reduce the communication overhead. Similarly, consistent coverage is improved through better path planning and enhanced beaconing range. However, most of the proposed geometric schemes in literature fails to address both the problems with better localization accuracy. In this paper, we address the underlying impact of shorter beaconing interval and the radio propagation irregularity. The proposed scheme utilizes the geometric least square curve fitting method for localization (GLSCFL). The results show that the proposed method provides better localization accuracy than other geometric schemes.

Transmission Power Control for Anchor-Assisted Localization in Wireless Sensor Networks

Energy-efficiency and localization play significant roles in Wireless Sensor Networks. Localization provides geographical information about the sensors deployed in a certain geographical region. Single mobile anchor-assisted localization is considered as an energy-efficient approach where numerous GPS-equipped sensors are removed to reduce the consumption of energy in the network. In recent years, various path planning mechanisms for mobile anchor have been proposed with single transmission power assigned to the anchor. Preservation of power is critical while performing mobile beacon-assisted localization so as to increase the lifetime. Providing the ability to dynamically adjust transmission power for the anchor node does not only reduce the energy consumption but also improves the overall performance of the localization. In this article, we propose a new trajectory, Anchor-power, that provides transmission power control for the mobile beacon. Anchor-power efficiently adjusts the transmission power of the beacon node for the reliable transmission of beacon messages. Experimental results reveal that our proposed approach enables unknown sensors to locate themselves with better accuracy in comparison with the existing models. Anchor-power improves performance in terms of accuracy and energy efficiency along with the localization success ratio and localization time.

Deep learning, machine learning and internet of things in geophysical engineering applications: An overview

The earthquakes in Eastern Mediterranean are mostly tectonic. The earthquakes that are 60 km deep in the ground are called Shallow earthquakes. The earthquakes in the eastern Mediterranean are generally shallow-orientated, and their depth often varies between 0 and 30 km. When there is a sudden plate movement within the earth's crust, increasing friction between two plates can result in earthquakes, which are harmful to human lives, buildings and the economy. With the technological developments in the world over the years, more information has been accessible and reliable. The Internet of things (IoT) and crowdsourcing have proven to be effective in predicting and preparing for the future natural hazards when combined with Machine Learning or Deep Learning. Machine learning does not require human interaction, as the machines automatically gets information and distributes in real-time to prevent major or severe damages, which are low-cost, has lower power consumption with reliable information. Short Message Service and Global Positioning System are still functional, even though powerlines and mobile beacons, were cut during recent massive earthquakes in some countries of Asia. The Seismic Alert System (SAS) is another design to prevent and reduce earthquake damages. The SAS was designed in 1996, to be effective with real time data from seismograms. The Earth cloud uses geophones which uses MEMS (micro electro-mechanical system). Geophones have an accelerometer and seismometers to detect the earth movement. Therefore, usage of sensors and the Internet of Things (IoT) to monitor earthquakes and send early warning signals to prevent destruction of buildings and loss of life play a significant role.

A Mobile-Beacon-Based Iterative Localization Mechanism in Large-Scale Underwater Acoustic Sensor Networks

The accurate localization of nodes is one of the most basic tasks in underwater acoustic sensor networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this article, we propose a mobile-beacon-based iterative localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multihop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism first uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then, the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network’s lifetime.

Range-Free Localization With a Mobile Beacon via Motion Compensation in Underwater Sensor Networks

Localization accuracy is crucial for achieving reliable location tagging for underwater missions. However, the accuracy of conventional range-free localization schemes with a mobile beacon is distorted by ship dynamics ( i.e. , pitch, roll, and heave) over oceanic conditions, and the schemes suffer from two uncertainties: beacon point and geometric shape uncertainties. To reduce the effects of these uncertainties, we herein propose LMB-MC, a novel range-free localization scheme. LMB-MC compensates beacon point errors and employs an accurate geometric model instead of an unconditional circle-shaped model. Furthermore, we adopted real-world motion measurements from an operating ship and used motion data to support diverse simulation settings ( i.e. , Douglas sea scales). Result of extensive simulation studies verified that LMB-MC provided improved accuracy and reliability compared to the existing solutions in the considered oceanic environments.

Design and Implement Beacon based scheme for Node Localization in Underwater Acoustic Network

A network that can sense the surroundings and collected all the information from the sensor nodes and passed it to the base station is known as a wireless sensor network. The underwater acoustic networks are the type of network deployed under the oceans and passed information to the base station. Due to the dynamic nature of the network, nodes change their location at any time. To maximum aggregate information from the sensor nodes, to estimate exact node location is very important. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node, localization has a high delay, which reduces its efficiency. The technique needs to be designed, which localizes more nodes in less amount of time. This research is based on the advancement of the range based scheme for node localization. In the proposed scheme, mobile beacons are responsible for node localization. The beacon nodes send beacon messages in the network, and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node, the sensor nodes which are already localized will not respond back to the beacon messages, which reduce delay in the network for node localization.

A context-aware system using mobile applications and beacons for on-premise security environments

In this paper, we describe a mobile solution for on-premise security monitoring using Android devices and Bluetooth Low Energy beacons. The system incorporates two mobile based applications, beacons and supporting cloud services. One mobile application was developed for security managers to easily configure beacons while in close proximity to a beacon for setting up a route, while the other is for security guards as they perform their rounds. We developed this system in collaboration with a security firm and an industry partner located in Waterloo, Ontario, Canada. We present the relevant background of work in this area, the architectural framework that we designed and developed to support the system, the applications themselves and a report on the findings of real use test case scenarios involving security managers and guards. Our system was tested in a variety of conditions and performed at an accuracy of 98%, achieved SUS usability scores of 85% and 82% for Security Managers and Security Guards respectively. It provides a low-cost, easily deployed scalable solution for security monitoring in a range of environment configurations.

Enhanced cooperative single-range underwater navigation based on optimal trajectories

This work addresses the observability analysis for a cooperative range-based navigation system based on the optimization of an index. A nonlinear model is first defined in order to describe the motion of the vehicle and a mobile beacon. Then, the Fisher Information Matrix is introduced to explain how it is related with the observability problem. A unconstrained optimization problem is formulated in order to find the best sequence of actions for the beacon to ensure observability in the system; the unconstrained problem does not take into account physical limitation of the vehicle and beacon. Then, four different scenarios are solved using different constraints; we show that, when the beacon is rotating with variable angular velocity we get a better strategy than rotating with constant velocity, despite that in both scenarios the system is observable. Finally, we show that increasing the energy provided to rotate the beacon does not improve further the observability of the system. These results are important from a theoretical and practical point of view, since they represent a strategy to plan the motion of the beacon to guarantee observability in the system.

$\Sigma$ -Scan: A Mobile Beacon-Assisted Localization Path-Planning Algorithm for Wireless Sensor Networks

Deploying static beacons to help sensor localization is a common approach in wireless sensor network. Alternatively, adopting a mobile beacon to travel along a specific trajectory and to broadcast its locations at specific points is more efficient. Therefore, path planning of the mobile beacon originally focused on finding shortest path length and minimum broadcast points, such as Scan and Hilbert, to decrease power consumption. More recently, path planning increasingly attempted to achieve higher accuracy, such as Double-scan and Z-scan. However, optimal trade-off between power efficiency and localization accuracy is yet to be achieved. In this paper, we designed $\Sigma $ -Scan which aims to reach high accuracy and coverage with short path length by combining the advantages of Scan and Z-scan. $\Sigma $ -Scan provides three kinds of unit to constitute arbitrary rectangles and minimize the minimum bounding rectangle. Compared to them, $\Sigma $ -Scan is more applicable, accessible and easy to implement. The simulation results show that compared to other previous approaches, $\Sigma $ -Scan has the highest ratio of accuracy and coverage to path length and also decreases the incidence of collinearity in time-priority trilateration (TPT).

Geometric sensitivity of beacon placement using airborne mobile anchors

Locating fixed sensing devices with a mobile anchor is attractive for covering larger deployment areas. However, the performance sensitivity to the geometric arrangement of anchor beacon positions remains unexplored. Therefore, localization using new RSSI-based localization algorithm, which uses a volumetric probability distribution function is proposed to find the most likely position of a node by information fusion from several mobile beacon radio packets to reduce error over deterministic approaches. This paper presents the guidelines of beacon selection that leads to design the most suitable trajectory, as a trade-off between the energy costs of travelling and transmitting the beacons versus the localization accuracy.