Recent years have witnessed considerable efforts in repurposing ubiquitous wireless communication signals for non-contact sensing.$\bf{NearLink}$is a new-generation short-range wireless communication protocol, which is designed to address the high-quality network connectivity requirements of low power, low latency and high reliability. Given these notable advantages, NearLink has great potential for widespread application in various Internet of Things (IoT) areas. However, NearLink-based wireless sensing has not yet been explored. To bridge this gap, this work explores for the first time the sensing potential and opportunities of NearLink. Specifically, we systematically investigate the sensing capability of NearLink through answering two key questions: (1) How can NearLink's communication-oriented signals be adapted for sensing tasks? (2) How can sensing performance be enhanced under multipath interference and low-power constraints? We prototype the NearLink sensing system-NearSense, and take the respiration detection as a case study to demonstrate its effectiveness. Extensive experiments demonstrate that NearSense can achieve an average detection rate of 98% and a false alarm rate below 1.5% in case of various real-life challenging interference. We believe this work opens up new directions for the new-generation wireless sensing towards high-quality network connections.

Device-free or off-body sensing methods, such as Lidar, can be used for location-driven Activities during Daily Life (ADL) recognition without the need for a mobile host such as a human or robot to use on-body location sensors. Because if such an attachment fails, or is not operational (powered up), when such mobile hosts are device free, it still works. Hence, this paper proposes an innovative method for recognizing ADLs using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a low-cost, 360°rotating 2D Lidar device. We researched, developed, deployed and validated the system. The results indicate that it can provide a centimeter-level localization accuracy of 88% when recognizing 17 targeted location-related daily activities.

In this paper, we propose an access control solution for exchanging Blockchain-based Electronic Medical Records (EMRs) called BBACS (Block-based Access Control Scheme) that includes an access model and an access scheme. Unlike the existing Blockchain-oriented access schemes for EMRs, our access model can omit the agent layer (gateway) in order to authorise users' access with block level granularity, whilst maintaining compatibility with the underlying Blockchain data structure. Furthermore, the authorisation, encryption, and decryption algorithms presented in BBACS dispense with the need to use a public key infrastructure (PKI) and hence cut down the cost of network construction and improve the computational performance. We validated the efficiency (time cost) of local computation and data transmission (over Wi-Fi) for BBACS using a simulation of BBACS against another Blockchain-oriented access control scheme for EMRs called HDG as our baseline. To the best of our knowledge, BBACS is the first Blockchain-oriented access control solution without the need for an agent (or gateway) design, supporting granular authorisation (block level), that has been proposed for secure EMRs management in eHealth.

The use of magnetic field (MF) measurements, unlike typical Wi-Fi or Bluetooth positioning measurements, are unaffected by moving humans, providing more time-invariant location information. We present a novel Fast Path Matching algorithm for MF and Inertial sensor measurements, FPM-MI, to localise a person using MF measurements fused with inertial sensor measurements. Our novelty is twofold: it has a reduced computational cost compared to a particle filter algorithm; it has a fast convergence performance, i.e., a person can walk a much shorter distance, about 3 m, to have an arm-span location accuracy. We validated our system in a library, a retail-like building, with multiple metal shelves and pillars, and determined the positioning error to be 1.8 m (90% confidence).

Deploying trusted (private) cloud computing to exchange, store and analyse data from IoT networks has become mainstream. In this paper, we describe a data privacy transmission scheme for IoT data collection, which supports one-way identity authentication and a dual data integrity validation for low resource devices called the Lo-A&DI (Low-resource for IoT 1-way Authentication and Dual Integrity) scheme. Unlike other schemes that use trusted clouds, the Lo-A&DI can be applied to untrusted public clouds while protecting data security and privacy. Unlike other untrusted cloud security schemes, the Lo-A&DI scheme can be used to support end-to-end data security and privacy from low-resource ICT IoT devices. An experimental validation shows that the performance of the Lo-A&DI is much more adaptable for use in resource-constrained IoT devices when compared to a baseline trusted cloud scheme such as one based upon an interactive (2-way) certificate authentication scheme for IoT data exchange.

In recent years, Virtual Reality (VR) applications have become widely available. An increase in popular interest raises questions about the use of the new medium for communication. While there is a wide variety of literature regarding scene transitions in films, novels and computer games, transitions in VR are not yet widely understood. As a medium that requires a high level of immersion [2], transitions are a desirable tool. This poster delineates an experiment studying the impact of transitions on user experience of presence in VR.