Pc Ng

While contact tracing is of paramount importance in preventing the spreading of infectious diseases, manual contact tracing is inefficient and time consuming as those in close contact with infected individuals are informed hours, if not days, later. This article proposes a smart contact tracing (SCT) system utilizing the smartphone’s Bluetooth low energy signals and machine learning classifiers to automatically detect those possible contacts to infectious individuals. SCT’s contribution is two-fold: a) classification of the user’s contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communication protocol. To protect the user’s privacy, both broadcasted and observed signatures are stored in the user’s smartphone locally and only disseminate the stored signatures through a secure database when a user is confirmed by public health authorities to be infected. Using received signal strength each smartphone estimates its distance from other user’s phones and issues real-time alerts when social distancing rules are violated. Extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers indicate that a decision tree classifier outperforms other state-of-the-art classification methods with an accuracy of about 90% when two users carry their smartphone in a similar manner. Finally, to facilitate research in this area while contributing to the timely development, the dataset of six experiments with about 123 000 data points is made publicly available.

This paper presents a novel Bluetooth Low Energy (BLE) protocol enabling a BLE node to perform RF fingerprint acquisition by measuring the received signal strength (RSS) from its neighboring nodes and simultaneously broadcast the acquired fingerprint via its advertising packet. However, the fingerprint acquisition and broadcast process in a dense BLE network is very challenging owing to 1) the increment of packet collision; and 2) the length-constrained packet. To this end, we exploit the compressive sensing (CS) framework such that each node only needs to acquire no more than M measurements from a very dense network, in which the number of nodes N is far greater than M. By aggregating the M-dimensional measurement vector from s<<N nodes, the receiver is able to reconstruct the fingerprints broadcast by all N nodes. Our proposed compressive RF fingerprinting (CRF) has been demonstrated with real and dense BLE network, consisting of 300 nodes randomly distributed around a confined area. We also conducted extensive simulations to verify the performance of three different reconstruction algorithms. The low reconstruction error at the receiving end indicates the feasibility of our proposed CRF in achieving efficient fingerprint acquisition for dense BLE networks.

Bluetooth low-energy (BLE) beacon is a key enabler for smart interaction between the user device and the physical thing, in which the physical thing can actively engage users for interaction via its advertising packet. However, reliability is always an issue for the beacon-based interaction since the beacon employs an unreliable broadcasting approach which provides no way to check if the user device has received the correct packet. We define the sparse observation to describe the phenomenon where the number of packets received by the user device within an arbitrarily small time duration is less than the number of deployed beacons. This article studies the sparse observation causing by the following two factors: 1) the unpredictable environmental variations and 2) the uncontrollable operating conditions of a beacon. An analysis is provided to investigate the interaction reliability in connection with the above two factors. Motivated by the above challenges, a novel solution, which exploits the ambient RF fingerprinting to address the sparse-observation issues, is proposed to enhance the interaction reliability. Our proposed solution is validated with extensive experiments consisting of real data collected from both indoor and outdoor environments. Finally, the feasibility of our proposed solution is demonstrated with a proof-of-concept prototype implemented over multiple physical things.

Device-free occupancy detection is very important for certain Internet of Things applications that do not require the user to carry a receiver. This paper achieves the device-free occupancy detection with RF fingerprinting, which labels each zone with a 2M-dimensional fingerprint vector. Specifically, the fingerprint vector consists of received signal strength (RSS) values measured from M Bluetooth low energy (BLE) beacons and also their corresponding temporal RSS variations. However, the unreliable RSS values caused two common issues with the fingerprint vector: 1) noise and 2) sparsity. To this end, we propose denoising-contractive autoencoder (DCAE) to jointly deal with these two issues, by learning a robust fingerprint prior to device-free occupancy detection. We validate the performance of our proposed DCAE with large-scale real-world datasets. The experimental results indicate the substantial performance gain of our proposed DCAE in comparison with state-of-the-art autoencoders. In particular, the classifier trained using the fingerprints learned by our proposed DCAE is able to maintain at least 90% accuracy when the noise factor or sparsity ratio increases to 0.6 and 0.5, respectively.

Bluetooth low energy (BLE) beacons have been widely deployed to deliver proximity-based services (PBSs) to user’s smartphones when users are in the proximity of a beacon. Conventional proximity detection simply uses the received signal strength (RSS) to infer the proximity, and then retrieves the PBS by mapping the beacon ID with the corresponding service in the cloud database. Such an approach suffers two major issues: 1) the severe RSS fluctuation might confuse the smartphone during the detection and 2) a malicious PBS can be delivered by manipulating the same beacon ID. This paper proposes RF fingerprinting to label a beacon with an N-dimensional fingerprint vector, which consists of N RSS values from N deployed beacons. The contribution of our proposed method is twofold: 1) we infer the proximity based on the fingerprint vector instead of relying solely on the single RSS value and 2) we retrieve the PBS by mapping the fingerprint vector instead of the hard-coded beacon ID. The challenge with our proposed approach is the incomplete fingerprint observation during real-time detection, resulting in an underdetermined proximity detection problem. To this end, we exploit the compressive sensing (CS) approach based on the differential evolutional algorithm to address such an underdetermined problem. Extensive simulations with realworld datasets show that our proposed approach outperforms the legacy machine learning techniques with substantial performance gains.

While the Internet of Things (IoT) is driving a transformation of current society toward a smarter one, new challenges and opportunities have arisen to accommodate the demands of IoT development. Low power wireless devices are, undoubtedly, the most viable solution for diverse IoT use cases. Among such devices, Bluetooth low energy (BLE) beacons have emerged as one of the most promising due to the ubiquity of Bluetooth-compatible devices, such as iPhones and Android smartphones. However, for BLE beacons to continue penetrating the IoT ecosystem in a holistic manner, interdisciplinary research is needed to ensure seamless integration. This paper consolidates the information on the state-of-the-art BLE beacon, from its application and deployment cases, hardware requirements, and casing design to its software and protocol design, and it delivers a timely review of the related research challenges. In particular, the BLE beacon’s cutting-edge applications, the interoperability between packet profiles, the reliability of its signal detection and distance estimation methods, the sustainability of its low energy, and its deployment constraints are discussed to identify research opportunities and directions.

The emergence of Bluetooth low energy (BLE) beacons has promoted the development of proximity-based service (PBS), which is a context-aware application delivered subject to the Proximity of Interest (PoI). Most commercial applications use the sequential proximity detection with a fixed scanning mechanism to identify the target PoI. Such sequential execution, though is able to produce reliable detection, suffers severe performance degradation especially when the number of deployed beacons in the vicinity increases. To understand the effects of dense deployment, we conduct an empirical investigation and derive the statistical properties of both received signal strength (RSS) and signal inter-arrival time. In light of the statistical insights, this paper proposes a high resolution proximity detection using an adaptive scanning mechanism fusion with a spontaneous Differential Evolution (AS+sDE). This novel approach enables the receiver to adapt its scanning duration conditioned on the deployment density and make an almost spontaneous detection in parallel with the scanning. The feasibility of the proposed approach is verified by both simulations and real-world implementations. For a density of ≤ 5 beacons = m2, AS+sDE achieves a superior performance with a high accuracy rate, i.e., on average <; 1s is spent to guarantee at least 90 percent accuracy.

The meeting of pervasive screens and smart devices has witnessed the birth of screen-smart device interaction (SSI), a key enabler to many novel interactive use cases. Most current surveys focus on direct human-screen interaction, and to the best of our knowledge, none have studied state-of-the-art SSI. This survey identifies three core elements of SSI and delivers a timely discussion on SSI oriented around the screen, the smart device, and the interaction modality. Two evaluation metrics (i.e., interaction latency and accuracy) have been adopted and refined to match the evaluation criterion of SSI. The bottlenecks that hinder the further advancement of the current SSI in connection with this metrics are studied. Last, future research challenges and opportunities are highlighted in the hope of inspiring continuous research efforts to realize the next generation of SSI.

While many technologies (e.g., RFID, QR code, etc.) have been developed for items identification, they fail to provide continuous monitoring for items in transit. This paper introduces a Bluetooth Low Energy (BLE) beacon-based system, which can be deployed easily with any off-the-shelf smartphone without modification on the existing infrastructures. However, it is an elusive challenge to achieve a fast item identification and counting involving massive items stacked up inside a confined space (e.g., a container), resulting in an ultra-dense beacon network (UDBN). To this end, we propose a novel beaconing solution capable of informing the receiver about their own presence as well as the presence of their neighboring beacons for identification purpose. Specifically, our proposed solution provides a well-designed yet innovative protocol data unit (PDU) which allows the beacon to encapsulate its neighboring information into its own advertising packet. A prototype consisting of 300 beacons is implemented to demonstrate the feasibility of our proposed solution for real-world applications. The extensive experiment confirm the superiority of our proposed solution in delivering a fast item identification and counting in UDBN.

While Bluetooth Low Energy (BLE) beacons have been massively deployed to broadcast their advertising packets to any receivers in their vicinity, it is relatively difficult, if not impossible, for a beacon to report the packet back to the server in the absence of a receiver. This paper proposes a novel BLE-based overlay mesh (BOM) that enables the mesh functionality to existing beacon networks without introducing new infrastructure. However, it is an elusive challenge to jointly manage the beaconing and flooding events. To this end, BOM employs 1) best-effort scheduling (BES) to minimize the packet collision rate (PCR) while scheduling the time slots for beaconing events, and 2) received signal strength (RSS)-based bounded flooding (RBF) to maximize the packet delivery ratio (PDR) for the advertising packet while forwarding the relaying packet across the BOM network. Extensive simulations indicate the substantial performance gain of our proposed approach in comparison to the legacy approaches. Specifically, BES reduces the PCR to 66.67%, whereas RBF improves the PDR for the advertising packet to 52% while maintaining approximately the same PDR for the relaying packet. The practical experiment with a real network testbed further demonstrates the feasibility of BOM.

Bluetooth Low Energy (BLE) beacons can be powered up with a small coin cell battery. The problem with battery-powered beacon is that frequent battery replacement is required. Such a battery replacement process can be very tedious considering the massive amount of already deployed beacons. While solar-powered beacons have emerged as an alternative to the battery-powered beacon, beacon deployment is challenging considering the very low ambient light energy available in indoor environments. This paper presents an innovate solar-powered beacon with an adjustable solar panel. In particular, we employ Markov Decision Process (MDP) to model the angle adjusting problem. The contribution of this paper is two-fold: 1) the MDP formulation is based on the insight obtained from a series of preliminary experiments which unveil the relationship between the incident angle and the harvested power; 2) our experiment shows that the legacy Policy Iteration (PI) and Value Iteration (VI) algorithms achieve similar optimized decision-making by adjusting the angle of solar panels such that to quickly charge up the beacon when it is low in energy. This rapid charging time guarantees the sustainable operation of solar-powered beacons in indoor environments.

Lately, there are many works exploited the radio frequency (RF) fingerprint for occupancy detection. However, most works suffer severe performance variations owing to the unreliable received signal strength (RSS). In this paper, we propose a deep learning approach to occupancy detection: 1) an unsupervised denoising-contractive autoencoder (DCAE) is built to learn a robust fingerprint representation from the raw RSS measurements, and 2) a supervised softmax function is added at the last layer for classification. A real testbed with Bluetooth Low Energy (BLE) beacons was built such that we can collect real-world RSS data for experiments. The data were collected via different devices at different times to better reflect environmental variations. The experimental results show that our proposed approach achieves a substantial performance gain in comparison to the conventional machine learning approaches. Specifically, our proposed DCAE is able to reconstruct the noisy and always changing data with less than 0.047 mean square error. Overall, our occupancy detection combining DCAE and softmax classifier achieves sub-room level accuracy for at least 99.3% of the time.

Lately, Bluetooth Low Energy (BLE) beacon has attracted a lot of interests for its capabilities in enhancing the interaction between smart things in the Internet of Things (IoT) ecosystem via proximity approach. Even though Proximity sensing is capable of delivering a correct interaction, it might have a problem for explicit interaction when exact distance estimation is required. Considering those interactive applications which are distance-dependent, this paper proposed an optimized support vector machine (O-SVM) on the cloud for distance estimation and a Kalman filter (KF) on the edge to obtain a near true RSS value from a list of RSS measurements. Four benchmark functions (i.e., two from Industries and two Machine Learning Techniques) have been used for performance evaluation. Simulation with real signal samples was conducted to verify the performance of our proposed algorithm. Besides examining the performance gain of our proposed solution over the four benchmark functions, we also implemented the proposed solution on a smartphone for practical testing to demonstrate its feasibility. The proposed solution not only outperforms the rest with significant performance gain, i.e., > 50% error reduction compared to the benchmark functions. Furthermore, practical implementation verified that our proposed approach is able to return the estimate distance in less than 1s, such real-time response is desirable for many delay- sensitive applications.

Considering an incomplete signals acquisition due to a sparse beacon deployment, this paper proposes a generalized similarity filter to improve the performance of proximity detection and thus guarantee the quality of proximity-based service (PBS). In particular, this paper leverages Bluetooth Low Energy (BLE) Beacons to realize a PBS system which comprises a number of Proximities of Interest (PoIs). We define a PoI as an object or area which is associated with a beacon such that each PoI can announce their presence implicitly through the beacon’s signal. However, under a sparse beacon network condition in which some beacons associated with some PoIs are malfunction or their batteries die before the scheduled maintenance, a receiver (e.g., smartphone) might fail to return the target PoI correctly. In view of the quality degradation in consequence to the sparse condition, we refine the performance of classical compressive sensing based algorithm with a generalized similarity filter. The effects of different similarity measures on proximity detection performance are also investigated. Simulation results indicate that the proposed algorithm improves the detection accuracy as compared to the conventional compressive sensing based algorithm. Specifically, Chordal-based similarity filter achieves substantial improvement in comparison with Mahalanobis and Euclidean-based similarity computation.

Existing interactive systems suffer from low user engagement due to their passiveness and steep learning curve. To address these issues, this paper presents an interactive framework, Notify-and-Interact, which leverages the Bluetooth low energy (BLE) beacon infrastructure to notify and a smart-phone to interact, such that it transforms a passive interactive system into an active one. The proposed framework is demonstrated in the Ping Yuan and Kinmay W Tang Gallery, where a series of wildlife artworks are exhibited. Engagement conversion rate is measured, and users’ quality of experience (QoE) is surveyed through likert assessment. Artworks with Notify-and-Interact outperforms the QR code with a high engagement conversion rate at the interaction stage, i.e., 86% over 53%, and an average engagement time of 55.67s over 28.69s, respectively. The mean opinion score (MOS) shows that around 80% of the users expressed high satisfaction with the installed Notify-and-Interact framework in the gallery.

A proximity-based service (PBS) leverages the estimated proximity to provide users the accessibility to object or location restricted service. This paper exploits the interaction between Bluetooth Low Energy (BLE) Beacon and smartphone to set forth the fundamental building block of a beacon-based PBS system. In real-world scenarios, a beacon-based PBS system might suffer from sparse conditions when some beacons malfunction or beacons can only be deployed in a few specific positions. Motivated by such limitations, a similarity filter extended with compressive sampling matching pursuit (SF-CoSaMP) is proposed to ensure the reliability of proximity detection under such sparse conditions before smartphone proceed to retrieve the corresponding PBS. An extensive simulation with large volume of collected data has been conducted and the results prove the reliability of the proposed algorithm with high detection accuracy in an environment with sparse deployment.

To look for the specific vacation rental that suits ones personal preferences can be time consuming due to the wealth of information available on the Internet. Often collaborative filtering is used to help people narrow down their search results. However, most of the methods are solely based on the textual data which might insufficient to capture comprehensive details about the accommodation that suits individuals’ preferences. The visual effects of the images, on the other hands, might reveal hidden users’ preferences which cannot be told through the text. In this paper, an images-textual hybrid recommender system is proposed to enhance the preferable vacation accommodation prediction by leveraging the strength of both data collected from users’ traveling histories. The proposed recommender system is demonstrated on the Airbnb dataset for all the advertised accommodation offers in Hong Kong. Around 1 million images features are extracted from a total of 110572 accommodations’ images for similarity calculation. Rooms description and review scores are collected through a custom built web crawler program, the review scores are used as a reference to filter out the low quality accommodation prior to the implementation of the proposed recommender system. The proposed hybrid recommender system achieves better recommendations with an average precision score of 36.23%, which shows a 26.44% improvement compared to the baseline, which has a mean precision score of 9.79%.