5G Network Data Migration Service Based on Edge Computing

With the development of mobile network technology, the continuous increase of mobile traffic has put forward higher requirements for quality of service (QoS) issues such as asymmetric transmission delay. The paper mainly studies the energy distribution problem on the migration data link from the terminal device to the edge node in the mobile edge network. Multiple data service packages are set up at each hop on the migration data link, and these data service packages compete with each other, and ultimately only one terminal provides and stores energy for this hop. The migration strategy of the data service package is affected by the edge node, and the edge node changes the migration strategy according to the migration strategy of the data service package. The paper is based on the formation of nodes between the data service packages of different nodes on the 5G network data link to jointly control the migration strategy, coordinate the migration strategy formulated, and better coordinate the migration strategy. In this competitive game model, the optimal migration strategy of nodes is found out according to the terminal equipment access requirements. Then according to the node stability rules, the composition of nodes when the nodes are stable is obtained, the migration strategy of stable nodes and the migration and spectrum strategies of operators are obtained, and the migration strategy of joint control provides energy for edge nodes.

Hyperledger blockchain based secure storage of electronic health record system in edge nodes

An Electronic Health Record (EHR) is a database for storing patients medical information collected from different sources such as smart wearable devices, smart sensors and diagnostic imaging equipment. An EHR contains sensitive private information for the patients and the treatment of their diseases. Furthermore, it’s often shared among different members consists of healthcare providers, insurance companies, medical researchers, and others. The main difficulty for EHR information management is the result of gathering, storing, and sharing patient healthcare without affecting privacy and security. Blockchain has recently proposed an efficient way to manage EHR data. This paper provides hybrid architecture for EHR data management by using both Hyperledger blockchain network in on-chain and edge node in off-chain. In this architecture is used to authenticate user without affecting sensitive patient’s information and also used to authenticate the encrypted EHR information in edge node. In the on-chain approach EHR activities and Patient authentication activities are recorded in the blockchain for the purpose of accountability and traceability. Edge nodes stored the encrypted EHR data in the off-chain method. So the combination of on-chain and off-chain approaches only allows the authorized data user who has to meet the EHR access activities and to decrypt the EHR data. If the EHR information is alter by an unauthorized user, the hash code is newly generated which is different from old hash code stored in the blockchain. As the result the user can easily detect that their EHR information has been hacked.

A Joint Model Provisioning and Request Dispatch Solution for Low-Latency Inference Services on Edge

With the advancement of machine learning, a growing number of mobile users rely on machine learning inference for making time-sensitive and safety-critical decisions. Therefore, the demand for high-quality and low-latency inference services at the network edge has become the key to modern intelligent society. This paper proposes a novel solution that jointly provisions machine learning models and dispatches inference requests to reduce inference latency on edge nodes. Existing solutions either direct inference requests to the nearest edge node to save network latency or balance edge nodes’ workload by reducing queuing and computing time. The proposed solution provisions each edge node with the optimal number and type of inference instances under a holistic consideration of networking, computing, and memory resources. Mobile users can thus be directed to utilize inference services on the edge nodes that offer minimal serving latency. The proposed solution has been implemented using TensorFlow Serving and Kubernetes on an edge cluster. Through simulation and testbed experiments under various system settings, the evaluation results showed that the joint strategy could consistently achieve lower latency than simply searching for the best edge node to serve inference requests.

An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles

In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.

A FPGA-Based Cloud-Edge Collaboration Platform in Cloud Manufacturing

To address the issue that many devices are connected to the cloud during the manufacturing process, which causes severe delays in analyzing massive manufacturing data in the cloud, an FPGA-based architecture of cloud edge collaboration is proposed. In this architecture, manufacturing equipment is connected to the cloud through an FPGA-based embedded edge node. The device data obtained by the edge node is processed by the FPGA module and the embedded system module according to the time-sensitivity. Considering the limited computing power of a single edge node, to realize cloud-edge collaborative computing, a communication-oriented task model and a computing model for edge nodes are designed. The task model learns cloud to edge and edge-edge communication, and the task model realizes the function of migrating computing tasks to other nodes. The edge node system’s design is realized based on the communication-oriented task model and the computing model for edge nodes. The cloud edge collaboration method is researched and explored based on this system. A series of comparative experiments, comparing the time delay of the FPGA module and embedded system module processing the same data, the framework’s usability and data processing ability can be verified.

Field Trial on 5G New Radio Over Satellite

5G New Radio (NR) is the 3rd Generation Partnership Project (3GPP) radio access technology for the next generation mobile communications network. A major evolution of 5G constitutes the integration of non-terrestrial networks including geostationary and low Earth orbit satellites. The seamless integration of satellites in the terrestrial mobile network requires significant adaptations within the radio access network and the development of new features in the core network to cope with the specific satellite channel characteristics. To date, the 5G control and data plane has been standardized to handle only continuous backhaul communication between the network components. However, a mobile satellite enabled next generation Node B (gNB) located in a vehicle or in a moving aerial platform needs to be able to handle frequent backhaul outages of various duration as well as longer signal delays as opposed to short terrestrial connections via fiber. In this paper, we report the results of an over-the-air (OTA) field trial comprising a mobile edge node connected to the 5G standalone core network components over a geostationary satellite. We analyze Transmission Control Protocol (TCP) acceleration and GPRS Tunneling Protocol (GTP)/TCP/Internet Protocol (IP) header compression features through the GTP. Moreover, the influence of short and long interruptions in the communication between the edge node and the central components on the entire system performance is investigated. The header compression and TCP acceleration modules were implemented on the satellite modems and are now part of the protocol stack of these devices. The results show up to 12% higher data rates for the 5G user equipment (UE), on a 1.5 MHz single carrier return link compared to deactivated TCP acceleration and header compression. We increased the data rate by 20% on the 4.5 MHz DVB-S2X forward link between the UE and 5G core. Moreover, our measurements reveal that even satellite-enabled gNB mobility is possible with the current Release 15 standard. After a short outage of the satellite connection due to shadowing, the UE can successfully re-establish the user plane connection to the core network. Our results will facilitate the full integration of satellite components in 5G through open and standard solutions.

An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

With large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.