Serving at the Edge: An Edge Computing Service Architecture Based on ICN

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

Detecting Malicious Switches for a Secure Software-defined Tactile Internet

The rapid development of the Internet of Things has led to demand for high-speed data transformation. Serving this purpose is the Tactile Internet, which facilitates data transfer in extra-low latency. In particular, a Tactile Internet based on software-defined networking (SDN) has been broadly deployed because of the proven benefits of SDN in flexible and programmable network management. However, the vulnerabilities of SDN also threaten the security of the Tactile Internet. Specifically, an SDN controller relies on the network status (provided by the underlying switches) to make network decisions, e.g., calculating a routing path to deliver data in the Tactile Internet. Hence, the attackers can compromise the switches to jeopardize the SDN and further attack Tactile Internet systems. For example, an attacker can compromise switches to launch distributed denial-of-service attacks to overwhelm the SDN controller, which will disrupt all the applications in the Tactile Internet. In pursuit of a more secure Tactile Internet, the problem of abnormal SDN switches in the Tactile Internet is analyzed in this article, including the cause of abnormal switches and their influences on different network layers. Then we propose an approach that leverages the messages sent by all switches to identify abnormal switches, which adopts a linear structure to store historical messages at a relatively low cost. By mapping each flow message to the flow establishment model, our method can effectively identify malicious SDN switches in the Tactile Internet and thus enhance its security.

Ties, status, and internationalization of Chinese private firms

Purpose This study aims to investigate the role of network in affecting private firms’ internationalization decision. Specifically, it investigates the way that business ties, political ties and status influence an internationalization decision. Design/methodology/approach On the basis of the survey data collected from Chinese private firms, this study distinguishes business ties from political ties and introduces network status. Binary logistic regression is used to test the hypotheses. Findings Results show that private firms that have business ties are more likely to internationalize, whereas private firms that have political ties are less likely to internationalize. High-status private firms are more likely to internationalize. Political ties negatively moderate the relationship between business ties and internationalization. High-status firms with political ties are more likely to internationalize. Originality/value This study provides theoretical and practical contributions. Results complement previous research on social networks in the context of Chinese private firms and have implications for managers who exert effort to internationalize their firms.

A New Three-Phase Smart Meter for Cloud Connection: Network Architecture and Performances

Efficient energy consumption is essential for the development of a smart grid in the power system. As a response, reliable monitoring and management of energy usage is a main priority for the smart grid. The existing energy meter system has a host of concerns, one of which is the lack of full-duplex transmission. To solve this problem, in this paper, we present a novel architecture for a cloud-connected Smart Energy Meter. The proposed meter is capable of real-time measurement of the energy network status and data transfer to a cloud architecture. The meter is suitable for single and polyphase measurement. Its performances are validated on a test field performed on the island of Lipari (Italy).

Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

Dynamic Content Enabled Microservice for Business Applications in Distributed Cloudlet Cloud Network

This study introduces Mob-Cloud, a mobility aware adaptiveoffloading system that incorporates a mobile device as a thick client, ad-hoc networking, cloudlet DC, and remote cloud to improve the performance and availability of microservices services. These cloudlet cloud has emerged as a popular model for bringingthe benefits of cloud computing to the proximity of mobile devices. the microservices preliminary goal is to improve service availability as well as performance and mobility features. The impact of dynamic changes in mobile content (e.g., network status, bandwidth, latency, and location) on the task offloading model is observed by proposing a mobility aware adaptive task offloading algorithm aware microservices, which makes a task offloading decision at runtime on selecting optimal wireless network channels and suitable offloading resources. The decision problem, which is well-known as an NP-hard issue, is the subject of this work. However, for the entire proposed microservices system has the following phases: I adaptive offloading decision based on real-time information, (ii) workflow task scheduling phase, (iii) mobility model phase to motivate end-users to invoke cloud services seamlessly while roaming, and (iv) faulttolerant phase to deal with failure (either network or node). We conduct real-world experiments on the built instruments to assess the online algorithm's overall performance. Compared to baseline task offloading solutions, the evaluation findings show that online algorithm incorporates dynamic adjustments on offloading decision during run-timeand achieves a massive reduction in overall response time with better service availability.

Association between aphasia severity and brain network alterations after stroke assessed using the electroencephalographic phase synchrony index

Electroencephalographic synchrony can help assess brain network status; however, its usefulness has not yet been fully proven. We developed a clinically feasible method that combines the phase synchrony index (PSI) with resting-state 19-channel electroencephalography (EEG) to evaluate post-stroke motor impairment. In this study, we investigated whether our method could be applied to aphasia, a common post-stroke cognitive impairment. This study included 31 patients with subacute aphasia and 24 healthy controls. We assessed the expressive function of patients and calculated the PSIs of three motor language-related regions: frontofrontal, left frontotemporal, and right frontotemporal. Then, we evaluated post-stroke network alterations by comparing PSIs of the patients and controls and by analyzing the correlations between PSIs and aphasia scores. The frontofrontal PSI (beta band) was lower in patients than in controls and positively correlated with aphasia scores, whereas the right frontotemporal PSI (delta band) was higher in patients than in controls and negatively correlated with aphasia scores. Evaluation of artifacts suggests that this association is attributed to true synchrony rather than spurious synchrony. These findings suggest that post-stroke aphasia is associated with alternations of two different networks and point to the usefulness of EEG PSI in understanding the pathophysiology of aphasia.

Traffic-aware adaptive server load balancing for software defined networks

Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms.