Context-Aware Privacy Preserving in Edge Computing

The ever-increasing scale and more stringent latency requirements of mobile computing tasks have driven the recent development of distributed edge computing. In distributed edge computing, a large-scale computing task is partitioned into multiple small subtasks and executed in parallel on multiple edge nodes (ENs) to reduce computation delay. In early works of this area, the computation results of the subtasks are often transmitted back in a non-cooperative manner, which may lead to suboptimal downlink communication delay. Replicated edge computing can alleviate this issue by replicating the computing task over multiple ENs to enable cooperative transmission in the downlink. However, this will inevitably entail multi-fold increase of computation costs. To bridge the gap between the conventional distributed edge computing and the replicated edge computing, a novel partial replication based distributed edge computing scheme is proposed in this work. In particular, by judiciously determining the portion of task to be replicated at the ENs, the proposed scheme can harvest cooperative transmission gains while avoiding excessive computational replication costs. Accordingly, a partial replication based delay minimization problem is formulated. By leveraging the generic alternating optimization framework, this problem can be divided into two subproblems of power allocation and task partitioning. Through analysis, a semi-closed form solution is derived for the former non-convex subproblem, while the latter subproblem turns out to be linear. Simulation results are presented to corroborate the effectiveness of the proposed scheme.

Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

Due to the urgent emergence of computation-intensive intelligent applications on end devices, edge computing has been put forward as an extension of cloud computing, to satisfy the low-latency requirements of these applications. To process heterogenous computation tasks on an edge node, the corresponding services should be placed in advance, including installing softwares and caching databases/libraries. Considering the limited storage space and computation resources on the edge node, services should be elaborately selected and deployed on the edge node and its computation resources should be carefully allocated to placed services, according to the arrivals of computation workloads. The joint service placement and computation resource allocation problem is particularly complicated, in terms of considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the waiting time of unprocessed tasks. Benefiting from deep reinforcement learning, we propose a novel approach based on parameterized deep Q networks to make the joint service placement and computation resource allocation decisions, with the objective of minimizing the total latency of tasks in a long term. Extensive simulations are conducted to evaluate the convergence and performance achieved by our proposed approach.

The stable and efficient management and dispatching of power system depend on the accurate short term load forecasting of the following few minutes to a week. With the rapid development of the power Internet of Things, the number of network edge devices and data volume has increased exponentially. However, the traditional centralized method cannot accurately grasp load variation patterns of all area, which entails storage pressure and delays of data calculation and transmission. In addition, the centralized method has potential data security risk for its transmitting and storing all data in the data center. The present research proposes an ultra-short term load forecasting method for the power Internet of Things based on federated learning, which learns the model parameters from the data distributed in multiple edge nodes. Simulation results show that the method effectively generates accurate load forecasting and reduces the data security risk under the condition that the data of each edge node does not come out of its location.

The edge computing works as an interface between the real world data, sensor nodes and cloud devices and provides intended security and privacy features. Use of smart wearable devices embedded with modern chipsets and leveraged with edge computing provides near real time monitoring, display diagnostics of state of the patient health and prevents spread of disease, even if the doctor is distantly located. In this paper, a secure smart health care system is proposed that is supported by multiple physiological sensors, smart devices and edge nodes at patient and doctor end to collect, protect, process, analyze and leverages cloud facilities to manage health records of patients. The analyzed data and requisites data are made available to the patient's smart devices. The simulation results show that the proposed system demands low latency for patients under different medical condition to enable fast and real-time diagnosis from healthcare professionals.

Nowadays, with the advances in wireless communication, the mobile devices are becoming important due to various applications which provide mobile users with plentiful services in the devices. The mobile devices can hardly complete all the computing tasks as they have limitations on the battery capacity, physical size, etc. In order to release these limitations, in the fifth generation (5G), the computing tasks can be offloaded from the mobile devices to the central units (CUs) which are enhanced into edge nodes (ENs) for processing. However, it is still a problem to select the appropriate offloading destination, aiming to improve the load balance for all the ENs. In this paper, we first formulate an optimization problem to improve the load balance of all the ENs for 5G networks in edge computing, considering the time consumption and the privacy conflicts. Then, a load-aware computation offloading method with privacy preservation, named LCOP, is designed. Finally, experimental results and evaluations validate our proposed method is both effective and feasible.

Multi-access/Mobile Edge Computing (MEC) and fog computing are promising techniques to satisfy low latency requirements for emerging next generation applications. Moving computation entities closer to a user could reduce the overall serving latency. In terms of green communications, given the latency constraint, how to minimize the power consumption at the user equipment (UE) and the edge node (EN) sides is important. Considering several edge nodes, partially offloading a user's task to one or more edge nodes is key. In this paper, a multi-node partial task offloading MEC scenario is discussed, in which UEs locally compute the task and share the remainder with other edge nodes. In addition, a green task distribution algorithm which minimizes the system power consumption is proposed, considering queueing, transmitting and computing delay. The simulation results show that the proposed algorithm minimizes the power consumption while meeting the latency requirements, and the power saving efficiency outperforms a binary offloading strategy. Moreover, the coupling effects between the latency requirements, offloading signal strength within the edge nodes, computation capability of edge nodes and the number of subcarriers used to transmit the offloading task are discussed.

With the development of intelligent transportation, vehicular edge computing (VEC) has enhanced the automaticity, efficiency, and coordination of the traffic system. Due to communication channel openness and traffic entity unreliability, identity authentication for privacy preservation, and trust management based on VEC become increasingly important in vehicular networks. However, existing authentication schemes mainly focus on the communication between an edge node and multiple vehicles, which may depend on the single edge computing node excessively and render it vulnerable. In this paper, we designed a blockchain-enabled authentication scheme for vehicles with decentralized identification based on traceable driving route data and the dynamic proxy mechanism. In the dynamic proxy edge computing (DPEC) mode, cooperative authentication based on secret sharing along with data tracking and trust management based blockchain is designed to enhance privacy preservation. Typical attacks are considered in this scheme to perform security analysis, and our proposed scheme is able to achieve cooperative privacy preservation for vehicles in VEC with low communication overhead and computation cost.

With the deep integration of communication technology and Internet of Things technology, the edge network structure is becoming increasingly dense and heterogeneous. At the same time, in the edge network environment, characteristics such as wide-area differentiated services, decentralized deployment of computing and network resources, and highly dynamic network environment lead to the deployment of redundant or insufficient edge cache nodes, which restricts the efficiency of network service caching and resource allocation. In response to the above problems, research on the joint optimization of service caching and resources in the decentralized edge network scenario is carried out. Therefore, we have conducted research on the collaborative caching of training data among multiple edge nodes and optimized the number of collaborative caching nodes. Firstly, we use a multi-queue model to model the collaborative caching process. This model can be used to simulate the in-network cache replacement process on collaborative caching nodes. In this way, we can describe the data flow and storage changes during the caching process more clearly. Secondly, considering the limitation of storage space of edge nodes and the demand for training data within a training epoch, we propose a stochastic gradient descent algorithm to obtain the optimal number of caching nodes. This algorithm entirely takes into account the resource constraints in practical applications and provides an effective way to optimize the number of caching nodes. Finally, the simulation results clearly show that the optimized number of caching nodes can significantly improve the adequacy rate and hit rate of the training data, with the adequacy rate reaching 84% and the hit rate reaching 100%.

Recent advances in machine learning and deep learning have enabled many existing applications in smart cities, autonomous systems, and wearable devices. These applications often demand scalable real-time cognitive intelligence and on-the-spot decision making. Current computer systems have been customized for a cloud computing paradigm which often does not meet latency constraints and scalability requirements. To address the limitations of the cloud computing paradigm, the general trend is toward shifting the computation next to data producers at the edge. However, the edge computing paradigm is in the very early stages. Many system-level aspects of edge computing, including algorithms mapping and partitioning across edge computing resources (edge server, and edge nodes) are unknown. New research is required to understand and quantify design dimensions for edge computing.This paper presents a novel edge computing infrastructure for distributed real-time video analytics. This paper presents a holistic solution for co-designing application and edge infrastructure, including edge nodes and edge servers, to enable scalable real-time Artificial Intelligence (AI)/Deep Learning (DL) video analytics across many cameras. For experimental results and evaluation, we focus on the case study of object re-identification across many cameras, which is composed of object detection/classification (TinyYOLOv3), feature extraction, local re-identification, and global re-identification kernels. We evaluate the edge system under three different task mapping and resource allocation configurations. The results present that with the edge nodes (video cameras) more than 32, the only scalable solution is to perform detection/classification (TinyYOLOv3), feature extraction, local re-identification on the edge nodes next to cameras, and execute global re-identification on edge server.

Computing nodes in Edge Computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

Edge computing has been widely used in recent years for bringing services closer to end users, resulting in faster response for applications. However, the sensitive information that leaves the data owner is at risk of being disclosed because the service provider is generally honest-but-curious. Federated learning (FL) is a popular method for preserving privacy by transferring the model from the edge node to local devices and training on the local data set. Nonetheless, the training parameter that communicates between local mobile devices and the edge node may contain the original data and be guessed by adversaries. In order to address the privacy threats, we propose the PL-FedIPEC scheme in this article, which is a privacy-preserving and low-latency FL method that transmits parameters encrypted with the improved Paillier, a homomorphic encryption algorithm, to protect the privacy of end devices without transmitting data to the edge node. Our method introduces the improved Paillier encryption, which brings a new hyperparameter and previously computes multiple random intermediate values in the key generation phase so that the time for the encryption phase has a significant reduction. With this new algorithm, the time for model training is decreased, and the sensitive information is in ciphertext format and cannot be analyzed. To evaluate the efficiency of our proposed scheme, we conduct extensive experiments and the results validate and demonstrate that our scheme with the improved Paillier algorithm can achieve the same accuracy as the original Paillier algorithm and the baseline FedAVG algorithm. At the same time, our method can save a massive amount of time when training the learning model with various settings.

A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment

The ever-increasing scale and complexity of the computing tasks arising from various mobile applications has fostered wide research interests in distributed edge computing. Coded edge computing is among the recent advancements in this area, which can effectively mitigate the task processing delay caused by straggling edge nodes (ENs). In edge computing, as the mobile user usually tends to offload to closer ENs to save transmit power, the adversary may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on location privacy-aware offloading, they mainly focused on the single EN scenarios and may not be directly applicable to coded edge computing that involves multiple ENs. To the best of our knowledge, the location privacy issue in coded edge computing still remains largely unexplored. With this consideration, a sequential hypothesis testing based location inference attack is identified in this work to reveal the potential vulnerability of existing coded edge computing methods. Besides, a countermeasure based on dynamic EN selection is proposed, together with a location privacy-aware coded offloading scheme based on the generic Lyapunov optimization framework. In addition to analysis, simulation results are provided to justify the effectiveness of the proposed scheme.

Deadly diseases and terrorist attacks are greatly threatening human safety, which challenges global security. To address this issue, urban surveillance systems are being applied at a rapid pace with mature but inefficient solutions in large scale networks. When a surveillance network is managing the data generated from multiple edge nodes, it is easy to create congestions due to concentrated data traffic and inefficient data delivery mechanism. In parallel, 5G technology, cope with explosive mobile data traffic growth and massive device connections, can realize a true “Internet of Everything” and build the social and economical digital transformation. In this paper, in the context of 5G technology, we propose an Information-Centric Networking (ICN) surveillance system based on our designed Suspicious Object Network System (SONS) over the concept of next-generation networking. In this solution, the edge nodes in the network distribute the computing and data storage requirements. We first describe the current surveillance issues and our proposed system architecture. Then we use simulation to verify and evaluate the system performance between legacy all-to-one centralized surveillance system and ICN based decentralized surveillance system.