More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

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Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: $Q$ -learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results.

Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks

Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environment and cannot achieve long-term performance. Moreover, the existing deep reinforcement learning-based offloading approaches suffer from the slow convergence caused by high-dimensional action space. In this paper, we propose a new model-free deep reinforcement learning-based online computation offloading approach for blockchain-empowered mobile edge computing in which both mining tasks and data processing tasks are considered. First, we formulate the online offloading problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Then, to maximize long-term offloading performance, we leverage deep reinforcement learning to accommodate highly dynamic environments and address the computational complexity. Furthermore, we introduce an adaptive genetic algorithm into the exploration of deep reinforcement learning to effectively avoid useless exploration and speed up the convergence without reducing performance. Finally, our experimental results demonstrate that our algorithm can converge quickly and outperform three benchmark policies.

Online Deep Reinforcement Learning for Computation Offloading in Blockchain-Empowered Mobile Edge Computing

Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial IoT (IIoT), and the high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this article, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on the state of the art for security and privacy and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging big data and Artificial Intelligence (AI) technologies show significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively.

A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles

With the advancement and emergence of diverse network services in Internet of Vehicles (IoV), large volume of data are collected and stored, making data important properties. Data will be one of the most important commodities in the future blockchain-based IoV systems. However, efficiency challenges have been commonly found in blockchain-based data markets, which is mainly caused by transaction confirmation delays and the cold-start problems for new users. To address the efficiency challenges, we propose a secure, decentralized IoV data-trading system by exploiting the blockchain technology, and design an efficient debt-credit mechamism to support efficient data-trading in IoV. In the debt-credit mechanism, a vehicle with loan demand could loan from multivehicles by promising to pay interest and reward. In particular, we encourage loaning among vehicles by a motivation-based investing and pricing mechanism. We formulate a two-stage Stackelberg game to maximize the profits of borrower vehicle and lender vehicles jointly. In the first stage, the borrower vehicle set the interest rate and reward for the loan as its pricing strategies. In the second stage, the lender vehicles decide on their investing strategies. We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and uniform pricing schemes. We also validate the existence and uniqueness of Stackelberg equilibrium. The numerical results illustrate the efficiency of the proposed pricing schemes.

A Novel Debt-Credit Mechanism for Blockchain-Based Data-Trading in Internet of Vehicles

The introduction of edge computing into blockchain-enabled Internet of Things (IoT) for offloading computational tasks is attracting increasing attention. Computing resource trading unavoidably happens in edge-assisted IoT. However, efficient computing resource trading cannot be achieved because of the “cold start” and “long return” problems. To address these challenges, we propose to use a credit-based payment for fast computing resource trading in edge-assisted blockchain-enabled IoT; therefore, the IoT nodes can finish fast payment and frequent trading by borrowing resource coins from other IoT nodes based on their credit values. In our resource-coin loan problem, we propose an iterative double-auction-based algorithm, where a broker is introduced to solve the loan allocation problem and to determine the size of the loan each lender would provide to each borrower. Furthermore, the broker enforces specific loan pricing rules to induce the borrowers and lenders to bid truthfully. Then, the hidden privacy information could be extracted to achieve the optimal resource-coin allocation and loan pricing. The proposed algorithm can maximize the economic benefits while protecting privacy. Simulations showed that the proposed algorithm can maximize social welfare. In addition, we compared the proposed algorithm with the credit-bank-based method in terms of the satisfaction function and payments. The experimental results demonstrated that the proposed algorithm was individually rational, truthful, and budget-balanced.

Credit-Based Payments for Fast Computing Resource Trading in Edge-Assisted Internet of Things

With the rapid development of the Internet of Things (IoT) in the era of big data, the amount of collected data has increased dramatically. Data are one of the most important commodities in IoT. To maximize the utility of the collected data, it is crucial to design an open IoT data market that enables data owners and consumers to carry out data trading securely and efficiently. To address the challenge of security presented by an untrusted and nontransparent data market, we propose an edge/cloud-computing-assisted, blockchain-enhanced data market framework to support secure and efficient IoT data trading, with a particular focus on an optimal pricing mechanism. In this mechanism, an authorized market-agency works as a scheduler, determining the win-owner and its pricing strategy to the consumer. We formulate a two-stage Stackelberg game to solve the pricing and purchasing problem of the data consumer and the market-agency. In the first stage of the game, the market-agency gives the win-owner and its pricing strategy. In the second stage, the data consumer decides on its purchasing quantity of data. We consider competition between data owners and propose a competition-enhanced pricing scheme (CPS). We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and CPSs. Lastly, we validate the existence and uniqueness of Stackelberg equilibrium, and the numerical results show the efficiency of the CPS.

Kang Liu

Papers

A Novel Debt-Credit Mechanism for Blockchain-Based Data-Trading in Internet of Vehicles

Credit-Based Payments for Fast Computing Resource Trading in Edge-Assisted Internet of Things

Optimal Pricing Mechanism for Data Market in Blockchain-Enhanced Internet of Things

Privacy-preserving incentive mechanism for multi-leader multi-follower IoT-edge computing market: A reinforcement learning approach