A comprehensive overview on secure offloading in mobile cloud computing

Shibin, D; Kathrine, G. Jaspher W.

doi:10.1109/ecs.2017.8067851

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Security issues of mobile IoT offloading have been discussed extensively in recent works [34], [35], [36]. Specially, access control, an efficient security mechanism, can ensure the high security capacity of offloading systems and preserve computation resources by offering numerous features such as enhanced device authentication, user authentication as well as improved data privacy and network security [37], [38], [39], [40].…”

Section: A Why Do We Need Blockchain Based Access Control For Mobile ...mentioning

confidence: 99%

Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning

Nguyen¹,

Pathirana²,

Ding³

et al. 2019

Preprint

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In current Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to cloud is vulnerable to security risks due to malicious mobile devices (MDs). How to implement offloading to solve computation problems of MDs while guaranteeing high security in mobile cloud is challenging. In this paper, we investigate simultaneously the security and offloading problems in a multi-user MECCO system on mobile cloud blockchain. First, to improve offloading security, we propose a trustworthy access control using blockchain, which protects clouds against illegal offloading behaviours. Then, to tackle the intensive computation issues of authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions and the allocation of computing resource and radio bandwidth. This optimization problem aims to minimize the long-term system costs of latency and energy consumption among all MDs. To solve the proposed offloading problem, we develop a novel deep reinforcement learning (DRL) algorithm by using advanced deep Q-network. We evaluate our framework towards access control and offloading performances by both real experiments and numerical simulations, showing significant advantages over existing schemes.

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Section: A Why Do We Need Blockchain Based Access Control For Mobile ...mentioning

confidence: 99%

Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning

Nguyen¹,

Pathirana²,

Ding³

et al. 2019

Preprint

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“…It is consisting of 2 sub-models as follow: Community Trust Extractor (CTE), to extract factors from social networks such as social distance or number of common neighbours, and Community Trust Inference (CTI), to infer the social trust between 2 users in the social networks, such as Google and FB. Similarly, the work in [13], listed some techniques that can be implemented to secure data before sharing it with cloud/edge servers. These are steganography, trusted computing techniques, hardware-based secure execution and homomorphic encryption.…”

Section: Litrature Review Of Edge Computing Solutionsmentioning

confidence: 99%

DEO: A Smart Dynamic Edge Offloading Scheme Using Processing Resources of Nearby Wireless Devices to Form an Edge Computing Engine

Lami

Al-ameri

2019

2019 IEEE Cloud Summit

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Edge computing reduces connectivity costs and network traffic congestion over cloud computing, by offering local resources (processing and storage) at one hop closer to the endusers. I.e. it reduces the Round-Trip Time (RTT) for offloading part of the processing workload from end-nodes/devices to servers at the edge. However, edge servers are normally pre-setup as part of the overall computing resource infrastructure, which is tough to predict for mobile/IoT deployments. This paper introduces a smart Dynamic Edge Offloading scheme, (we named it DEO), that forms the "edge computing resource" on-the-go, as needed from nearby available devices in a cooperative sharing environment. This is especially necessary for hosting mobile/IoT applications traffic at crowded/urban situations, and, for example, when executing a processing intensive Mobile Cloud Computing Service (MCCS) on a Smartphone (SP). DEO implementation is achieved by using a short-range wireless connectivity between available cooperative end-devices, that will form the edge computing resource. DEO includes an intelligent cloud-based engine, that will facilitate the engagement of the edge network devices. For example, if the end-device is a SP running an MCCS, DEO will partition the processing of the MCCS into sub-tasks, that will be run in parallel on the newly formed "edge resource network" of other nearby devices. Our experiments prove that DEO reduces the RTT and cost overhead by 62.8% and 75.5%, when compared to offloading to a local edge server or a cloud-based server.

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Blockchain-Based Secure Computation Offloading in Vehicular Networks

Zheng

Chen

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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A comprehensive overview on secure offloading in mobile cloud computing

Cited by 3 publications

References 19 publications

Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning

Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning

DEO: A Smart Dynamic Edge Offloading Scheme Using Processing Resources of Nearby Wireless Devices to Form an Edge Computing Engine

Blockchain-Based Secure Computation Offloading in Vehicular Networks

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