SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience

Self Cite

Edge computing emerges as an alternative to cloud computing in the scenarios where the end devices require lower latency and faster access speeds. Edge nodes are deployed at the proximity of the end devices to reduce response time. On the other hand, the edge nodes are usually owned by small organizations that have limited operations and maintenance capabilities. Data on the edge may be easily damaged, due to external attacks or internal hardware failures. Therefore, it is essential to verify data integrity in edge computing. However, edge environment requires a different trust model compared with other computing and storage paradigm. Besides, compared with cloud storage, edge storage is decentralized and storage service participants may pose greater internal and external threats. This paper proposes a blockchain‐based intelligent crowdsourcing audit approach (Crowdauditing) to achieve on‐chain and off‐chain credibility of audit results. The model relies on an untrusted auditor committee from the crowd to audit data integrity and uses smart contracts as the core of the intelligent system to ensure the reliability of result submission, the accuracy of the result judgment, and reasonable punishments and rewards. Specifically, an unbiased selection algorithm is proposed to achieve fairness during the auditor committee construction. An innovative two‐stage submission strategy is proposed to ensure that the auditor committee can reach a consensus on the off‐chain audit results. An incentive mechanism is carefully designed to force auditors providing audit services honestly to maximize their own rewards. Moreover, we modeled that as a game of n players, which proves the reliability of the result. Finally, we implement a prototype of Crowdauditing based on smart contracts. The extensive experimental results demonstrate the effectiveness of Crowdauditing.

Section: Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain

Chen

Zhou

et al. 2021

Self Cite

“…Recently, blockchain is adopted as one of the most promising technologies to provide security support for IoT systems. 21 It was initially applied to provide digital payments, 22 and is now commonly used in smart contracts 23,24 and data storage. 25 Gueta et al 26 developed a scalable blockchain system based on optimized Practical Byzantine Fault Tolerance (PBFT).…”

Section: The Application Of Blockchain In Iotmentioning

confidence: 99%

EviChain: A scalable blockchain for accountable intelligent surveillance systems

Chen

et al. 2021

Self Cite

Smart cameras, as typical IoT devices, are widely adopted to provide surveillance on individuals, homes, and the environment. The unavoidably captured sensitive visuals via these cameras may raise significant security concerns, while the prevalent software defects and authentication misconfiguration issues aggravate the vulnerability of such devices. However, traditional cryptography techniques are inadequate to provide full protection of these devices due to the large computation overhead. In this context, realizing accountability for these surveillance systems shall be the last line of defense in the presence of fast-evolving and high-influential threats. We propose EviChain, a scalable blockchain-based solution to trace the operations on intelligent surveillance cameras and reserve the evidence for any misuse in tamper-proofing manipulation records. Building a blockchain over the distributed cameras is challenging due to the limited capacity of on-board memory.To tackle this challenge, we design a cooperative mechanism that enables cameras to adaptively join in groups and share storage for recording blocks. In addition, we present a computation efficiency and delay-aware block generation strategy to reduce the cost of the consensus process. We perform extensive

“…MEC is envisioned as a supplement to cloud computing. By deploying computing, storage, and other resources near the devices, MEC enables swift response to user service requests and reduces network traffic and cloud computing load 8–10 . Cloud computing is implemented in a fully centralized manner, with servers typically concentrated in one or several locations.…”

Section: Introductionmentioning

confidence: 99%

“…By deploying computing, storage, and other resources near the devices, MEC enables swift response to user service requests and reduces network traffic and cloud computing load. [8][9][10] Cloud computing is implemented in a fully centralized manner, with servers typically concentrated in one or several locations. In contrast, edge computing is implemented at the network's edge in a fully distributed manner.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning‐based multitask hybrid computing offloading for multiaccess edge computing

Cai

Liu

2022

By deploying computing units in edge servers, the device‐generated computation‐intensive tasks can be offloaded from the cloud, lessening the core network's traffic and reducing the tasks' completion latency. To mitigate the burden on edge server and improve user experience, this paper proposes a deep reinforcement learning (DRL)‐based multiuser multitask hybrid computing offloading model for offloading a set of computation‐intensive tasks generated by multiple users to edge server and adjacent devices. The proposed model makes global computing offloading decisions for multiple computation‐intensive tasks simultaneously rather than via one‐by‐one decision‐making, which takes the impact of users' offloading decisions on the system's overall performance in multitask offloading scenarios into account. The main goal of this study is to reduce the long‐term overall system delay. The model uses the recurrent neural network to extract the feature information of task and network state, improving the convergence speed and stability of the DRL model. The experimental results demonstrate that the global offloading decision‐making model outperforms other methods regarding long‐term overall system delay and device energy consumption.