Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning
TL;DR: Wang et al. as mentioned in this paper investigated simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain, and proposed a trustworthy access control using blockchain, which can protect cloud resources against illegal offloading behaviours.
Abstract: For current and future 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 issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over existing approaches.
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TL;DR: In this paper , the authors investigated computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical layer security (PLS).
Abstract: This article investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical-layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile-edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization (ECM) or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process cannot be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this article, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes and ensures that the node paying higher Gas gets stronger computational resources.
12 citations
TL;DR: This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task, and the mining task is offloaded to the edge networking.
Abstract: Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.
12 citations
TL;DR: In this paper , a cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system was proposed, where each edge device not only handles data tasks but also deals with block mining for improving the system utility.
Abstract: The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.
12 citations
TL;DR: In this paper , a convolutional neural network was used to accelerate learning and put it on the edge platform to solve the task offloading problem in the Markov decision process.
Abstract: Offloading is one of the critical enablers of the Internet of Things (IoT) as it helps overcome the resource limitations of individual objects. Offering enough computational power for IoT applications at the edge has become a severe problem. An intelligent edge is a potential approach for pushing intelligence to network edges, which has played the role of intelligent decision-making in many elements of edge, notably task offloading. By leveraging the edge, IoT devices with limited battery capacity can offload a portion of the tasks that can dramatically reduce latency and improve battery life. Because IoT devices have limited battery capacity, employing deep learning approaches in such devices results in higher energy consumption. As a result, several studies used energy harvester modules, which are not available to IoT devices in real-world scenarios because many IoT devices lack such modules. This article proposes the offloading problem by leveraging the Markov decision process. Furthermore, we built a lightweight version of the reinforcement learning technique to decrease complexity and deployed it in IoT devices. Then, we used a convolutional neural network to accelerate learning and put it on the edge platform. Throughout the entire working duration of the system, these two methods collaborate to provide the optimal offloading strategy. Also, transfer learning was used to initialize Q-table values to improve the system’s efficiency. The results showed that the proposed method outperforms five benchmarks in terms of delay by 3.3%, IoT device efficiency by 3.2%, energy use by 4.2%, and task failure rate by 2.9% on average.
5 citations
TL;DR: In this paper , the authors provide a detailed description of the critical technologies, challenges, and applications of federated learning in cloud-edge collaborative architecture, and provide guidance on future research directions.
Abstract: Abstract In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative architecture has been proposed to compensate for the lack of data processing power of traditional cloud computing. On the other hand, on account of the increasing demand of the public for data privacy, federated learning has been proposed to compensate for the lack of security of traditional centralized machine learning. Deploying federated learning in cloud-edge collaborative architecture is widely considered to be a promising cyber infrastructure in the future. Although each cloud-edge collaboration and federated learning is hot research topic respectively at present, the discussion of deploying federated learning in cloud-edge collaborative architecture is still in its infancy and little research has been conducted. This article aims to fill the gap by providing a detailed description of the critical technologies, challenges, and applications of deploying federated learning in cloud-edge collaborative architecture, and providing guidance on future research directions.
4 citations