Topic
Edge computing
About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.
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TL;DR: This article proposes a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers, and proposes a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit.
Abstract: Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users’ data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors’ cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.
85 citations
TL;DR: Wang et al. as discussed by the authors proposed a hierarchical computation architecture by inserting an edge layer between the conventional user layer and cloud layer, and further developed an innovative operation mechanism to improve the performance of mobile AR applications.
Abstract: In order to mitigate the long processing delay and high energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising means to deliver better Quality of Experience (QoE) for AR consumers. In this article, we first present a comprehensive AR overview, including the indispensable components of general AR applications, fashionable AR devices, and several existing techniques for overcoming the thorny latency and energy consumption problems. Then we propose a novel hierarchical computation architecture by inserting an edge layer between the conventional user layer and cloud layer. Based on the proposed architecture, we further develop an innovative operation mechanism to improve the performance of mobile AR applications. Three key technologies are also discussed to further assist the proposed AR architecture. Simulation results are finally provided to verify that our proposals can significantly improve latency and energy performance as compared to existing baseline schemes.
85 citations
TL;DR: The hardware architectures of typical IoT devices are presented and many of the low power techniques which make them appealing for a large scale of applications are summed up.
Abstract: In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.
85 citations
TL;DR: This paper considers a setting, where MUs can offload their computations to the MEC server through a small cell base station, the SBS connects to the macro BS through a wireless backhaul, and computation resource at the M EC server is shared among offloading MUs.
Abstract: Considered as a key technology in 5G networks, mobile edge computing (MEC) can support intensive computation for energy-constrained and computation-limited mobile users (MUs) through offloading various computation and service functions to the edge of mobile networks. In addition to MEC, wireless heterogeneous networks will play an important role in providing high transmission capacity for MUs in 5G, where wireless backhaul is a cost-effective and viable solution to solve the expensive backhaul deployment issue. In this paper, we consider a setting, where MUs can offload their computations to the MEC server through a small cell base station (SBS), the SBS connects to the macro BS through a wireless backhaul, and computation resource at the MEC server is shared among offloading MUs. First, we formulate a joint optimization problem with the goal of minimizing the system-wide computation overhead. This is a mixed-integer problem and hard to derive the optimal solution. To solve this problem, we propose to decompose it into two subproblems, namely the offloading decision subproblem and the joint backhaul bandwidth and computation resource allocation subproblem. An algorithm, namely JOBCA, is proposed to obtain a feasible solution to the original problem by solving two subproblems iteratively. Finally, numerical results are conducted to verify the performance improvement of the proposed algorithm over two baseline algorithms and the close performance of the proposed algorithm compared with the centralized exhaustive search.
85 citations
TL;DR: A fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust is proposed.
Abstract: The development of mobile cloud computing technology has made location-based service (LBS) increasingly more popular. Given the continuous requests to cloud LBS servers, the amounts of location and trajectory information collected by LBS servers are continuously increasing. Privacy awareness for LBS has been extensively studied in recent years. Among the privacy concerns about LBS, trajectory privacy preservation is particularly important. Based on privacy preservation models, previous work have mainly focused on peer-to-peer and centralized architectures. However, the burden on users is heavy in peer-to-peer architectures, because user devices need to communicate with LBS servers directly. In centralized architectures, a trusted third party (TTP) is introduced, and acts as a bridge between users and the LBS server. Anonymity technologies, such as k-anonymity, mix-zone, and dummy technologies, are usually implemented by the TTP to ensure safety. There are certain drawbacks in TTP architectures: Users have no physical control of the TTP. Moreover, the TTP is more attractive to adversaries, because substantially more sensitive information is stored by the TTP. To solve the above-mentioned problems, in this paper, we propose a fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust. Compared with cloud computing, fog computing is a promising technique that extends the cloud computing to the edge of a network. Moreover, fog computing provides local computation and storage abilities, wide geo-distribution, and support for mobility. Therefore, mobile users’ partial important information can be stored on a fog server to ensure better management. We take the principles of similarity, intersection, practicability, and correlation into consideration and design a dummy rotation algorithm with several properties. The effectiveness of the proposed method is validated through extensive simulations, which show that the proposed method can provide enhanced privacy preservation.
85 citations