Impact of User Privacy and Mobility on Edge Offloading

Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure, and service providers must adapt to users' different mobility patterns, which can be challenging, especially for location-based services (LBS). Another issue that needs to be tackled is the increasing demand for user privacy protection. With less (accurate) information regarding user location, preferences, and usage patterns, forecasting the performance of offloading mechanisms becomes even more challenging. This work discusses the impacts of users' privacy and mobility when offloading to the edge. Different privacy and mobility scenarios are simulated and discussed to shed light on the trade-offs (e.g., privacy protection at the cost of increased latency) among privacy protection, mobility, and offloading performance.Comment: 2023 Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (IEEE PIMRC 2023

Offloading Decision Algorithm Based on Distance Weighted K-Nearest Neighbor in Power Internet of Things

With the widespread popularity of power Internet of Things (PIoT), the data collected from smart meters are growing explosively, which makes the calculation task of power data more and more complex. In order to improve computing power and maximize resource utilization, an offloading decision algorithm based on weighted K-nearest neighbor (WKNN) is proposed. It first collects the training set required by the WKNN-based algorithm, including the Received Signal Strength (RSS) required for offloading, the transmission rate, and the load balance of the Access Point (AP), and then the Euclidean distance between the training set and the sample is weighted by Gaussian function. Finally, the result with the largest K similarities in the training set is the offloading result. The simulation results show that the proposed algorithm reduces the offloading delay of the computing tasks and improves the resource utilization rate effectively when the number of meters increases in the network, which ensures that the resources of the mobile edge computing (MEC) servers in the system can be effectively and evenly utilized

Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy

Security Threats to 5G Networks for Social Robots in Public Spaces: A Survey

This paper surveys security threats to 5G-enabled wireless access networks for social robots in public spaces (SRPS). The use of social robots (SR) in public areas requires specific Quality of Service (QoS) planning to meet its unique requirements. Its 5G threat landscape entails more than cybersecurity threats that most previous studies focus on. This study examines the 5G wireless RAN for SRPS from three perspectives: SR and wireless access points, the ad hoc network link between SR and user devices, and threats to SR and users’ communication equipment. The paper analyses the security threats to confidentiality, integrity, availability, authentication, authorisation, and privacy from the SRPS security objectives perspective. We begin with an overview of SRPS use cases and access network requirements, followed by 5G security standards, requirements, and the need for a more representative threat landscape for SRPS. The findings confirm that the RAN of SRPS is most vulnerable to physical, side-channel, intrusion, injection, manipulation, and natural and malicious threats. The paper presents existing mitigation to the identified attacks and recommends including physical level security (PLS) and post-quantum cryptography in the early design of SRPS. The insights from this survey will provide valuable risk assessment and management input to researchers, industrial practitioners, policymakers, and other stakeholders of SRPS.publishedVersio

Local Differential Privacy based Federated Learning for Internet of Things

Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, which raises severe location privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The Three-Outputs mechanism is proposed which introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J

A Study on Intelligent Design and Control for Mobile Edge Computing Systems under the Wireless Environment

Tohoku University加藤寧課