In End-Edge-Cloud (EEC) computing environments, the diversity of devices often requires cloud-trained models to be adapted for end/edge devices, complicating decentralized project management. To address this, end/edge devices are increasingly using local model sharing instead of traditional cloud solutions. Popular platforms like GitHub and DockerHub lack the necessary data authenticity and security for high-stakes applications. While blockchain can ensure secure data sharing, permissioned blockchains struggle with the dynamic nature of EEC devices. To solve this, we propose BlockEdge, a hybrid blockchain architecture combining a permissioned blockchain with Practical Byzantine Fault Tolerance (PBFT) for cloud-based data management and a permissionless blockchain with Proof of Work (PoW) for decentralized model sharing at the end/edge. We enhance the PoW process with a dynamic mining algorithm and a lazy-loading Merkle tree structure, improving energy efficiency and computational performance. Experimental results show that BlockEdge reduces energy consumption by over 50% and cuts data update time by 91.73%, effectively addressing the energy and time inefficiencies of mainstream consensus mechanisms.

The proliferation of virtual reality (VR) content within social networks amplifies the importance of reducing rendering delay, as seamless interactions in shared virtual spaces are crucial for fostering social connections. Integrating VR streaming with social networks requires innovative solutions to address the unique challenges arising from the interaction between immersive experiences and social interactions. In this context, our research focuses on minimizing rendering delay for VR streaming in social networks, leveraging the synergistic benefits of edge computing. However, in scenarios where end-users experience poor channel quality, the rendering delay is prolonged due to lower data rates. To this end, a double reconfigurable intelligent surface (RIS) is employed to assist in improving the channel efficiency for end-user devices located in weak signal reception zones. We formulate an optimization problem related to the rendering resource allocation in edge servers and the distribution of downlink bandwidth for VR content from the edge server as a quadratically constrained quadratic problem. The non-convex optimization problem has been solved by dividing the problem into three sub-problems and solved using the block coordinate descent (BCD) method. In this study, we aim to enhance the overall quality of VR experiences in social settings, paving the way for more compelling and interactive virtual interactions where end-users have low wireless channel reception Quality.

In recent days, due to the emergence of communicable infectious diseases, healthcare, and medical technologies are expected to play a critical role. The advancement of communication and sensor network technologies has accelerated mass screening systems to combat the disease. Human temperature detection is one of the measurements for crowd screening in public places. Nevertheless, it is challenging to design a fast, lightweight, and easy-to-deploy contact-less crowd screening system in the outdoor environment due to several factors, such as environmental effect, background temperature, deployment cost, and remote operation. The state-of-the-art is mainly based on either hand-held devices or high-cost infrared cameras in only designated places. This article presents an end-to-end contactless assistive method for human body temperature screening systems, starting from collecting raw temperature data using a thermal camera to identify the suspected individual for combating communicable infectious diseases. We leverage the computing, storage, and communication resources offered by edge computing. In particular, we deploy a lightweight version of MobileNet v2 in resource-constrained Raspberry Pi 4B to detect the human’s head and body from the thermal image and use a classifier to determine the temperature from the raw temperature data. Moreover, we leverage a low-power and long-range wireless network for the exchange of model parameters between Raspberry Pi and the remote server. The experiments show that although the detection accuracy is not very high, we can reduce the bottleneck from screening time and reduce the exposure for the individuals because of the reduced bottleneck. Our proposed solution is implemented in Python and is available under the open-source MIT License athttps://github.com/mitunhub/HAWK-i.

End-users under poor wireless network coverage generally suffer from underutilization of bandwidth. This adversely affects the overall performance of task offloading to the edge server. In this work, we study a Reconfigurable Intelligent Surface (RIS)-assisted wireless network that enables end-user's devices under weak signal reception areas to enhance their offloading opportunities for delay minimization. It becomes a challenging task to allocate uploading bandwidth allocation for the offloaded tasks from end-user devices under different signal coverage areas. We formulate the optimization problem of bandwidth allocation for the offloaded tasks in the edge server and the offloading decisions as a quadratically constrained quadratic problem. We exploit a semi-definite relaxation (SDR) method to solve the problem. Moreover, during optimization, we minimize the adverse impact of bandwidth allocation for poor end-users on good end-users performance. From extensive simulation results, we show remarkably elevated improvement in delay reduction with RIS assistance compared to other baselines, increasing the number and ratio of end-user devices under good and poor signal reception areas.

Human-computer interaction provides pervasive services advocating several exciting interactive systems, such as remote automation, surgery, and rehabilitation. This paper studies a tight synchronization between human and robot hands to establish near to real-time maneuvering. We leverage the computing resources of the participating units of the master domain to determine the useful data by overserving and predicting the immediate reaction of the human hand movement. Moreover, we consider the unreliable wireless channels that lead to packet error during data transmission from the master domain to the controlled domain. In particular, by bringing the concept of edge computing while utilizing the Raspberry Pis's available yet limited computing resources, we aim to determine the balance between useful data and redundant packets without any significant performance degradation. Finally, we implement the proposed synchronization method of human-robot arm interactions in a real testbed and compare the performance with baselines.

In this demonstration, we suggest lightweight haptic communications from the context of remote neurorehabilitation. In particular, we collect tactile data from pressure sensors attached to the human hand and design a classifier to determine the objects that an individual holds or grasps. Finally, we have implemented the proposed system on Raspberry Pi and demonstrated a personalized classification while rendering the haptic feedback in a virtual perception.

In this demonstration, we present a wearable haptic system to realize the perceptual illusion in a virtual environment. We collect the pressure data from sensors attached with the fingertip, and after passing through a classifier, we render the sensation of pressure. We mainly focus on designing a lightweight and wearable haptic interface with fast rendering. At last, we implement the proposed interface on Raspberry Pi and present preliminary results with various test objects.

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.

In this demonstration, we present an end-to-end assistive method for human body temperature screening system starting from collecting raw data using a thermal camera to identify the suspected individual for combating communicable infectious diseases. We deploy a lightweight MobileNet v2 in resource-constrained Raspberry Pi 4B to detect the human's head and body from the thermal image and use a classifier to determine the temperature from the raw temperature data. The experiments show that although the detection accuracy is not very high, we can reduce the bottleneck from screening time and reduce the exposure for the individuals because of the reduced bottleneck.

Industrial applications, such as real-time manufacturing, fault classification and inference, autonomous cars, etc., are data-driven applications that require machine learning with a wealth of data generated from industrial Internet of Things (IoT) devices. However, conventional approaches of transmitting this rich data to a remote data center to learn may be undesired due to the non-negligible network transmission delay and the sensitiveness of data privacy. By deploying a number of computing-capable devices at the network edge, edge computing supports the implementation of machine learning close to the industrial environment. Considering the heterogeneous computing capability as well as network location of edge devices, there are two types of feasible edge computing based machine learning models, including the centralized learning and federated learning models. In centralized learning, a resource-rich edge server aggregates the data from different IoT devices and performs machine learning. In federated learning, distributed edge devices and a federated server collaborate to perform machine learning. The features that data should be offloaded in centralized learning while it is locally trained in federated learning make centralized learning and federated learning quite different. We study the computation offloading problem for edge computing based machine learning in an industrial environment, considering the abovementioned machine learning models. We formulate a machine learning-based offloading problem with the goal of minimizing the training delay. Then, an energy-constrained delay-greedy (ECDG) algorithm is designed to solve the problem. Finally, simulation studies based on the MNIST dataset have been conducted to illustrate the efficiency of the proposal.

Quality of Service (QoS) provisioning for wireless multimedia applications has attracted increasing research attentions. Due to limited bandwidth of time-varying wireless links and various QoS requirements of bursty multimedia applications, an effective call admission control (CAC) policy that efficiently utilizes the bandwidth is desired. We have proposed a Differentiated Queueing Service (DQS) to support per-packet differentiated services for admitted traffic, which has been illustrated feasibility for mixed streams with various QoS requirements in multimedia applications. This paper furthers the research by exploring a DQS-based CAC policy to maximize the bandwidth utilization of wireless networks while provisioning call-level as well as packet-level QoSs for multimedia applications. To this end, we novelly use delay bound violation probability as admission control threshold and a basic DQS-based CAC policy, namely Delay Bound violation probability Guard (DBG) policy is proposed. Then we optimize the policy by formulating it as a Semi-Markov Decision Process (SMDP). The optimal DBG policy is found by solving the linear programming formulation problem. Numerical results show that, the DBG policy over SMDP achieves its goals of maximizing the bandwidth utilization while provisioning both call-level and packet-level differentiated QoSs for each class of multimedia calls in comparison with bandwidth guard CAC policies.

Quality of Service (QoS) provisioning especially delay guarantee is particularly important to the increasing popularity of mobile wireless multimedia applications. Traditional QoS schemes include the per-flow Integrated Service (IntServ) and per-class Differentiated Service (DiffServ) as well as their variants. To overcome the scalability problem of IntServ and the coarse QoS granularity of DiffServ, the per-packet Differentiated Queueing Service (DQS) was proposed. This paper focuses on analytically modeling of the QoS provisioning performance of DQS for mobile wireless multimedia applications. In the analysis, arriving packets of a session are classified into different streams according to their delay requirements. Each stream is described by a modified Fractional Brownian Motion (FBM)based traffic model, where both the traffic self-similarity of multimedia streams and heavy-tailed distribution of packet sizes are considered. We develop a stochastic service capability model for mobile wireless links. Then a delay guarantee model for DQS is further developed by deriving the delay bound violation probability and packet loss rate. Computer simulations are conducted to validate the proposed analytical models.