Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

A Survey on the Edge Computing for the Internet of Things

With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

A Review of False Data Injection Attacks Against Modern Power Systems

A survey on security control and attack detection for industrial cyber-physical systems

With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. In this paper, we study and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: 1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; 2) from the CPS components perspective, we focus on cyber, physical, and cyber-physical components; and 3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS, and smart cars). The model can be both abstract to show general interactions of components in a CPS application, and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection. With this intensive literature review, we attempt to summarize the state-of-the-art on CPS security, provide researchers with a comprehensive list of references, and also encourage the audience to further explore this emerging field.

https://ieeexplore.ieee.org/ielaam/6488907/8172502/7924372-aam.pdf

Cyber-Physical Systems Security—A Survey

It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.

On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures

Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.

A Survey on Big Data Market: Pricing, Trading and Protection

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. Nonetheless, leveraging optimal phasor measurement unit (PMU) placement to defend against these attacks, while simultaneously ensuring the system observability, has yet to be addressed without incurring significant overhead. In this paper, we enhance the least-effort attack model, which computes the minimum number of sensors that must be compromised to manipulate a given number of states, and develop an effective greedy algorithm for optimal PMU placement to defend against data integrity attacks. Regarding the least-effort attack model, we prove the existence of smallest set of sensors to compromise and propose a feasible reduced row echelon form (RRE)-based method to efficiently compute the optimal attack vector. Based on the IEEE standard systems, we validate the efficiency of the RRE algorithm, in terms of a low computation complexity. Regarding the defense strategy, we propose an effective PMU-based greedy algorithm, which cannot only defend against data integrity attacks, but also ensure the system observability with low overhead. The experimental results obtained based on various IEEE standard systems show the effectiveness of the proposed defense scheme against data integrity attacks.

On Optimal PMU Placement-Based Defense Against Data Integrity Attacks in Smart Grid

Electric vehicle (EV) has become one of the most critical components in the smart grid with the applications of the Internet-of-Things (IoT) technologies. Real-time charging control is pivotal to ensure the efficient operation of EVs. However, the charging control performance is limited by the uncertainty of the environment. On the other hand, it is challenging to determine a charging control strategy that is able to optimize multiple objectives simultaneously. In this article, we formulate the EV charging control model as a Markov decision process (MDP) by constructing state, action, transition function, and reward. Then, we propose a deep-reinforcement-learning-based approach: charging control deep deterministic policy gradient (CDDPG) to learn the optimal charging control strategy for satisfying the user’s requirement of battery energy while minimizing the user’s charging expense. We utilize the long short-term memory (LSTM) network that extracts the information of previous energy price to determine the current charging control strategy. Moreover, Gaussian noise is added to the output of the actor network to prevent the agent from sticking into the nonoptimal strategy. In addition, we address the limitation of sparse rewards by using two replay buffers, of which one is used to store the rewards during the charging phase and another is used to store the rewards after charging is completed. The simulation results prove that the CDDPG-based approach outperforms the deep- $Q$ -learning-based approach (DQL) and the deep-deterministic-policy-gradient-based approach (DDPG) in satisfying the user’s requirement for the battery energy and reducing the charging cost.

CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs’ sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and $\varepsilon $ -differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.

Dou An

Papers

On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures

A Survey on Big Data Market: Pricing, Trading and Protection

On Optimal PMU Placement-Based Defense Against Data Integrity Attacks in Smart Grid

CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control

Towards Differential Privacy-Based Online Double Auction for Smart Grid