Beacon Deployment Research Articles

New Wireless Beacon Deployment for Maximum Coverage Efficiency in Indoor Localization

New Wireless Beacon Deployment for Maximum Coverage Efficiency in Indoor Localization

Optimal Beacon Deployment for Positioning in Cluttered Indoor Environments

Accurate indoor positioning is vital for many indoor autonomous systems. This article focuses on the influence of beacon placement on overall positioning quality in cluttered indoor environments. We first propose a metric based on the Fisher information matrix (FIM) to evaluate the performance of the positioning service provided by a specific beacon layout. After analyzing the characteristics of the FIM, we introduce an additional penalty term in the performance metric to handle singular cases. We then investigate the optimal beacon placement with respect to overall positioning quality. We derive the gradient of the optimization objective and propose an efficient optimization method called the quotient-space gradient-based particle swarm optimization (QGPSO), which combines the quotient-space PSO with the gradient ascent method, enabling the particle swarm to obtain more accurate optimization results locally while retaining the original global exploration capability. Numerical results validate our analysis and the superiority of the proposed optimization method over other baselines.

Pick the Most: An Algorithm for Optimum Beacon Deployment in WSN for Localization

Pick the Most: An Algorithm for Optimum Beacon Deployment in WSN for Localization

Efficient Adaptive Beacon Deployment Optimization for Indoor Crowd Monitoring Applications

The indoor crowd density monitoring system using BLE beacons is one of the effective ways to prevent overcrowded indoor situations. The indoor crowd density monitoring system consists of a mobile application at the user's side and the beacon sensor network as the infrastructure. Since the performance of crowd density monitoring highly depends on how BLE beacons are placed, BLE beacon placement optimization is fundamental research work. This research proposes a beacon deployment method EABeD to incrementally place the beacons adaptively to the latest signal propagation status. Also, EABeD reduces most walking and measurement labor costs by applying Bayesian optimization and the walking distance optimization algorithm. We conducted the placement optimization experiment in the wild environment and compared the results with placements derived by the simulation-based method and people. The result shows that our proposed method can achieve 26.4% higher detection coverage than the simulation-based approach, 23.2% and 5.2% higher detection coverage than the inexperienced person's solution and the expert's solution. As for the labor cost reduction, our proposed method can reduce 90.2% of the walking distance and 74.4% of the optimization time compared with optimization by the dense data gathering method.

BSSH: Bluetooth Indoor Positioning Strategy for Ship Cabin Based on Hierarchical Area

In the process of ship's ocean transportation, the monitoring of crew's work completion is a very important part in the ship safety management, and using indoor positioning technology can improve the construction of ship management system. However, the current indoor positioning technology is not ideal in the engine room environment in the face of the complex electromagnetic environment and spatial structure. In order to improve the positioning accuracy, this paper proposes a Bluetooth indoor positioning strategy for ship cabin based on hierarchical area (BSSH) for deploying and dividing the regions requiring indoor positioning and combines Bluetooth Low Energy (BLE) with Bluetooth fingerprint positioning technology. Processing the fingerprint library by using region hierarchy and proximity classification algorithms to obtain reliable results in the ship environment, the results show that better Beacon deployment and hierarchical area algorithms can lead to a localization accuracy of 80.38%.

Efficient Beacon Deployment for Large-scale Positioning

Instant and precise localization of indoor mobile users is fundamental for supporting various sophisticated location-aware services. Using Bluetooth low-power beacons for mobile user positioning has been reported as an effective approach, where the beacon deployment positioning (BDP) problem has been defined. The paper introduces a novel approach for solving large-scale BDP problems, aiming to significantly reduce beacon consumption from existing solutions with much less computation complexity. Extensive simulations are conducted to verify the proposed algorithm, whose beacon consumption is about 1.14 to 1.67 times and 0.2 to 0.48 times compared to those of the Mixed Integer Linear Program (ILP) and a naive iBeacon solution respectively. We have also observed that the running time scales well with the growth of the number of Test Positions and attenuation factors.

Energy-aware localization algorithm for Ocean Internet of Things

PurposeLocalization is one of the critical issues in Ocean Internet of Things (OITs). The existing research results of localization in OITs are very limited. It poses many challenges due to the difficulty of deploy beacon accurately, the difficulty of transmission distance estimation in harsh ocean environment and the underwater node mobility. This paper aims to provide a novel localization algorithm to solve these problems.Design/methodology/approachThis paper takes the ship with accurate position as a beacon, analyzes the relationship between underwater energy attenuation and node distance and takes them into OITs localization algorithm design. Then, it studies the movement regulation of underwater nodes in the action of ocean current, and designs an Energy-aware Localization Algorithm (ELA) for OITs.FindingsProposing an ELA. ELA takes the ship with accurate position information as a beacon to solve the problem of beacon deployment. ELA does not need to calculate the information transmission distance which solves the problem of distance estimation. It takes underwater node movement regulation into computation to solve the problem of node mobility.Originality valueThis paper provides an ELA based on the relationship between propagation energy attenuation and node distance for OITs. It solves the problem of localization in dynamic underwater networks.

Beacon Deployment for Unambiguous Positioning

Instant and precise localization of a mobile user is fundamental for supporting various sophisticated indoor location-aware services. This paper focuses on achieving unambiguous user positioning using practical Bluetooth low energy (BLE) beacons with multiple discrete power levels. By receiving the beacon coverage status from a user’s device, the cloud server can unambiguously pinpoint the user’s location and react correspondingly. We first define the problem of beacon deployment for positioning (BDP) and provide several theoretic bounds on the number of required beacons to gain sufficient understanding on its performance behavior. The BDP problem is further formulated into an integer linear program (ILP) and solved in extensive case studies. We claim that this is the first systematic and in-depth research on beacon deployment for unambiguous user positioning. Our analysis and experiments show that the proposed solution takes ${O(\sqrt {N})}$ to ${O({N}/{2})}$ beacons for ${N}$ test positions, which is 2–8 times less beacons compared to that by the naive approach, while the analytical bounds are tight with the ILP results with 20% of gap.

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons.

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

Beacon deployment strategy for guaranteed localization in wireless sensor networks

In wireless sensor networks, beacons are always treated as infrastructures for localization. After beacons are deployed, non-beacon nodes can be located by simple schemes such as multilateration and multidimensional scaling (MDS). Deploying as many beacons as needed is an efficient way to improve localization accuracy where a global positioning system does not work well or a higher location accuracy is required. With more beacons to be deployed, the configuration of beacons' positions will have to be done manually. Therefore position auto-configuration using measured distances between these beacons can save a lot of efforts for the deployment. One challenge of this auto-configuration is that the positions should be uniquely determined based on the measured distances. In graph theory, it is a problem of unique realization in which the positions of vertices are determined by edges between them. Addressing this problem is one major aspect of this paper. To determine whether the topology of a network is a unique realization, this paper proposes a novel category of topology named Uniquely Determined Topology, with which edges in a d-dimensional space can be reduced from $$d+1$$d+1 to d in each extension, which is less strict and more suitable for beacon deployment. The other aspect of this paper is to improve localization accuracy of the deployed beacons. In MDS and curvilinear component analysis, a shortest-path algorithm is adopted to approximately reconstruct the distance matrix between each two nodes, and our proposed Uniquely Determined Topology has a feature that a distance calculation model can be adopted to replace the shortest-path algorithm, therefore that the local distance matrix can be reconstructed more accurately. Theoretical analysis shows that it has a low computational complexity to determine whether a deployment is a Uniquely Determined Topology. Simulations show the advantages of the improved localization scheme, in that they do not depend on the connectivity level of the networks, and they can provide accurate localization when the estimation accuracy of distances is high.

A BIM centered indoor localization algorithm to support building fire emergency response operations

Abstract The ability to locate people quickly and accurately in buildings is critical to the success of building fire emergency response operations, and can potentially contribute to the reduction of various building fire-caused casualties and injuries. This paper introduces an environment aware beacon deployment algorithm designed by the authors to support a sequence based localization schema for locating first responders and trapped occupants at building fire emergency scenes. The algorithm is designed to achieve dual objectives of improving room-level localization accuracy and reducing the effort required to deploy an ad-hoc sensor network, as the required sensing infrastructure is presumably unavailable at most emergency scenes. The deployment effort is measured by the number of beacons to deploy, and the location accessibility to deploy the beacons. The proposed algorithm is building information modeling (BIM) centered, where BIM is integrated to provide the geometric information of the sensing area as input to the algorithm for computing space division quality, a metric that measures the likelihood of correct room-level estimations and associated deployment effort. BIM also provides a graphical interface for user interaction. Metaheuristics are integrated to efficiently search for a satisfactory solution in order to reduce the computational time, which is crucial for the success of emergency response operations. The algorithm was evaluated by simulation, where two building fire emergency scenarios were simulated. The tabu search, which employs dynamically generated constraints to guide the search for optimum solutions, was found to be the most efficient among three tuned tested metaheuristics. The algorithm yielded an average room-level accuracy of 87.1% and 32.1% less deployment effort on average compared with random beacon placements. The robustness of the algorithm was also examined as the deployed ad-hoc sensor network is subject to various hazards at emergency scenes. Results showed that the room-level accuracy could remain above 80% when up to 54% of all deployed nodes were damaged. The tradeoff between the space division quality and deployment effort was also examined, which revealed the relationship between the total deployment effort and localization accuracy.

Lean implementation of electronic health records (EHR).

211 Background: The Palo Alto Medical Foundation (PAMF), an integrated multispecialty physician group and one of five regions of Sutter Health in Northern California, is converting from an electronic chemotherapy ordering and administration system, Intellidose, to Epic Beacon to achieve full EHR integration. As chemotherapy workflows are complex with multiple interactions between providers and high potential for patient harm, we adopted a novel approach of incorporating Lean process improvement principles in advance of our Beacon deployment to improve the design of our software build and increase operational safety and efficiency. Methods: Two multi-disciplinary teams including oncologists, pharmacists, nurses, administrative and technical support staff were deployed; a Sutter Beacon team responsible for technical design and build of the Sutter Beacon standard and a second PAMF team to evaluate workflow variation among PAMF chemotherapy infusion centers, identify best practices, engage the Sutter Beacon Build team around system design and institute rapid cycle process improvement in partnership with the PAMF Lean Promotion Office. Results: Visio maps of current state workflows were constructed to reflect oncologist, pharmacy and nursing workflows from medication ordering to chemotherapy administration and billing submission. Two Rapid Process Improvement Workshops designed future state workflows and identified key areas requiring re-engineering. Lean rapid cycle improvement strategies will be leveraged to improve clinical workflows prior to Beacon implementation. To support the 40% of infusion treatments that are for non-oncology patients, a second process was designed as an important patient safety initiative to avoid discordant nursing administration workflows. Conclusions: Successful design, build and implementation of EHR require achieving consensus-driven work processes that maximize patient safety, eliminate workflow redundancies and maximizes the value-added component. It requires coordination between the EHR technical build team who design the system and the implementation team who integrate it with re-engineered workflows. Lean methodology provides the conceptual framework to achieve a successful EHR deployment.

Insights toward robot-assisted evacuation

This paper considers the problem of human evacuation assistance. We discuss how this kind of task differs from the more prevalent search and rescue tasks, and the resulting implications for the design of assistive evacuation systems. We describe the implementation and evaluation of an algorithm for deploying audio navigational cues throughout an office building with a team of mobile robots. A review of evacuation dynamics methods is presented and particular methods are applied on-line during deployment. We use a pedestrian simulation and a simple model of audio–evacuee interaction to show the effects of beacon deployment; the results indicate that even a small number of beacons can significantly decrease the mean and variance of egress time and distance. We also advance uses for human motion-based measures of environment complexity for general mobile robotics.