Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid

Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods.

Privacy-preserving and Efficient Aggregation based on Blockchain for Power Grid Communications in Smart Communities

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided. This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions. 

When blockchain meets smart grids: A comprehensive survey

Benefitingfrom the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment. 

RPPTD: Robust Privacy-Preserving Truth Discovery Scheme

Cloud-edge-end collaboration enables harmonious and efficient resource allocation for the Power Internet of Things (PIoT). However, the security and complexity issues of computation offloading evolve into the main obstacles. In this article, we first propose a blockchain and AI-based secure cloud-edge-end collaboration PIoT (BASE-PIoT) architecture to ensure data security and intelligent computation offloading. Its advantages in flexible resource allocation, secure data sharing, and differentiated service guarantee are elaborated. Then the adaptability of three typical blockchains with PIoT is analyzed, and some typical application scenes of BASE-PIoT including computation offloading, energy scheduling, and access authentication are illustrated. Finally, we propose a blockchain-empowered federated deep actor-critic-based task offloading algorithm to address the secure and low-latency computation offloading problem. The coupling between the long-term security constraint and short-term queuing delay optimization is decoupled by using Lyapunov optimization. Numerical results verify its excellent performance in total queuing delay and consensus delay.

Blockchain and Federated Deep Reinforcement Learning Based Secure Cloud-Edge-End Collaboration in Power IoT

Peer-to-Peer (P2P) resource sharing promotes local resource-hungry task offloading to other mobile devices and balances the resource consumption between mobile devices. Most of existing P2P task offloading systems aims to solve the resource sharing between one pair exclusively without considering the cost of resource supply and the strategic behaviors of mobile users. In this paper, we propose two user models for the P2P task offloading system: honest user model and strategy user model. For the honest user model, we formulate the resource allocation maximization problem with latency and energy consumption constraints as an Integer Linear Programming. We show that the solution for honest user model can output 189% resource transactions of that for the strategic users. For the strategy user model, we propose a double auction-based P2P task offloading system, and design a truthful multi-resource transaction mechanism to maximize the number of resource transactions. We first group the mobile users based on the connected components to improve the efficiency of double auction. Then we utilize the McAfee Double Auction to price the resource transactions. Finally, we split each winning mobile user of double auction into multiple virtual mobile users, and use the matching approach to calculate the resource allocation. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed multi-resource transaction mechanism satisfies the desirable properties of computational efficiency, individual rationality, budget balance, truthfulness for resource request/supply, and general truthfulness for bid/ask price.

Truthful Multi-Resource Transaction Mechanism for P2P Task Offloading Based on Edge Computing

Bitcoin is one of the most popular cryptocurrency in the world. Miners in the Bitcoin network reduce their risks through participating in mining pool. Existing mining pool systems do not consider the cost and strategy of miners. In this paper, we study two mining models: public cost model and private cost model. For the public cost model, we design an incentive mechanism, called Mining game, using a Stackelberg game. We show that Mining game is individually rational, profitable, and has the unique Stackelberg Equilibrium. For the private cost model, we formulate the Budget Feasible Reward Optimization (BFRO) problem to maximize the reward function under the budget constraint, and design a budget feasible reverse auction to solve the BFRO problem, which is computationally efficient, individually rational, truthful, budget feasible, and constant approximate. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Incentive Mechanism for Rational Miners in Bitcoin Mining Pool

Auction design for cross-edge task offloading in heterogeneous mobile edge clouds

Device-to-device (D2D) communication is a core technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem of D2D underlay communication in a base station cell area with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D User Equipment and Cellular User Equipment are taken into consideration under the control of Efficiency Functions. The optimization problems are formulated as a game model series of utility functions built on the unit price of jamming power and the amount of jamming service. We extracting the system into a price bargain game with a buyer and a seller both desiring maximum profits, a bargaining game approach is adopted to solve this problem by reaching an agreement of unit price. The step number of bargain process is also a restriction under consideration. For the non-steps model, an Evaluation Function and a Comprehensive Utility Function are demonstrated to analyze the bargain process. For steps-contained model, the step number of iteration is involved and an attenuation function is introduced to modify the bargaining game. The algorithms of two models are proposed to derive the equilibrium point for reaching an agreement. Finally, extensive simulations are illustrated for verifying proposed theory.

Weifeng Lu

Papers

Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid

Truthful Multi-Resource Transaction Mechanism for P2P Task Offloading Based on Edge Computing

Incentive Mechanism for Rational Miners in Bitcoin Mining Pool

Auction design for cross-edge task offloading in heterogeneous mobile edge clouds

Improving physical layer security and efficiency in D2D underlay communication