The smart grid is a critical infrastructure that must function reliably in a geographically decentralized structure. However, this structure renders the smart grid vulnerable to stealthy cyberattacks, and data silos further limit the sharing of datasets needed to train an effective attack-detection model. Moreover, most existing methods rely on the availability of enormous amounts of labeled data, which is scarce due to the need for domain knowledge. To address these issues, we propose FedCLD, a federated contrastive learning approach for detecting stealthy attacks using unlabeled data. FedCLD leverages Bootstrap Your Own Latent (BYOL), a contrastive learning model, to enhance its ability to learn robust representations from unlabeled data. With the federated learning paradigm, FedCLD enables local centers in different areas to collaboratively train local models without sharing raw datasets. Although the global representation is enhanced, the regional characteristics should be preserved. Therefore, a strategic local update scheme based on the exponential moving average is proposed. Furthermore, we theoretically prove the convergence of FedCLD with this modified update strategy. Experiments are conducted in the industry-level PowerWorld simulator to evaluate the performance of FedCLD.

Recently reported attacks against Programmable Logic Controllers (PLCs) have shown that the exploitation of Industrial Control Protocols (ICPs), i.e., ICP-based attacks, poses significant threats to industrial control systems. ICP-based attacks include two essential steps: generating tailored attack payloads and breaking through the session-ID-based PLC defenses. Traditional approaches to performing the two steps rely on laborious manual analysis. To analyze the threats posed by ICP-based attacks to commercial-off-the-shelf PLCs, we propose AIAF, an Automated ICP-based Attack Framework leveraging proprietary binary protocols, which operates automatically through an offline construction of effective attack payloads and an online ICP-based attack test. We have evaluated AIAF with 9 mainstream PLCs, covering 9 protocols, showing that AIAF can reverse engineer 12 kinds of session-ID negotiation (6 value-changed and 6 value-same), generate attack payloads, and execute 35 ICP-based attacks with a 94.29% success rate. Our further Internet-wide evaluation reveals that over 28K PLCs exposed to the Internet are vulnerable to ICP-based attacks.

Programmable logic controllers (PLCs) play a crucial role in various industrial manufacturing processes. Recent attack events show that attackers have a strong interest in controller variables of PLCs, including the device status and internal program logic. Detecting anomalous messages targeting PLC controller variables, which relies on the analysis of controller variable semantics, has proven to be an effective method for identifying such attacks. However, the proprietary nature of industrial control protocols (ICPs) poses a challenge to extracting the required semantics. In this paper, we propose an automated framework namedSePannerto extract the semantics of controller variables from proprietary ICPs based on network traffic. Specifically, we first collect multiple groups of interaction traffic of PLCs and perform the starting-aligned comparisons on them to locate the semantic fields directly. Then, we identify and investigate a new problem in semantic extraction — interference resulting from misordered messages — and propose a set of filtering criteria to eliminate it effectively. We evaluate SePanner using the S7COMM protocol, and the results indicate that SePanner can successfully extract the semantics of controller variables with 100% accuracy. Additionally, we employ SePanner to analyze 7 proprietary ICPs, successfully extracting the semantics of 63 controller variables and their 134 states. Additionally, we demonstrate the extensive applications of SePanner in multiple ICS security scenarios and present its better performance compared with existing ICP semantic analyzing tools.

Coordinated cyber-physical attacks (CCPAs) pose a critical threat to the secure operation of smart power grids. While existing studies often assume simultaneous or sequence-agnostic attack strategies, this paper proposes a sequence-aware CCPA framework that explicitly models the temporal coupling between cyber manipulation and physical sabotage. We enhance the classical load redistribution attack (LRA) by addressing four key limitations: detectability due to infeasible power flows, violation of power balance, omission of post-attack system response, and insensitivity to attack sequencing. Specifically, we formulate two distinct bilevel attack models, namely Cyber-to-Physical (C→P) and Physical-to-Cyber (P→C), and solve them via an exact KKT-based Mixed-Integer Linear Programming (MILP) reformulation and a scalable Benders decomposition (BD) framework. Experiments on IEEE 14-, 57-, and 118-bus systems demonstrate that C→P attacks induce significantly more line overloads than P→C, validating the heightened risk of cyber-initiated cascades. Moreover, our BD approach accurately identifies spatial vulnerability hotspots with high fidelity, even when the physical attack budget is extended fromRp= 1 toRp= 2, confirming the framework’s scalability and practical relevance. The results provide actionable insights for adaptive grid protection against sophisticated, sequential threats.

With the increasing sophistication of malware, enhanced Attributed Control Flow Graphs (ACFGs) have become a fundamental representation and are widely applied in malware detection. However, existing CFG-based detection techniques primarily extract shallow features of malware, neglecting deeper structural and semantic characteristics. Additionally, retaining all basic blocks in CFGs significantly increases the memory overhead of detection models. To address these issues, we propose MCLPF, collaborative malware detection with interpretable pruning, to improve the overall performance of existing malware detection systems that rely on fine-grained control flow features. MCLPF first introduces a novel Attributed Interpretable Flow Graph (AIFG) to extract functional attributes, integrating node-level features, edge-level features, and assembly language embedding features derived from Large Language Models (LLMs). Subsequently, it proposes an efficient and reliable detection scheme by alternately updating the graph structure and language learning modules through L-Step and G-Step, rather than synchronously training Language Models (LMs) with Graph Neural Networks (GNNs) on large-scale graphs. We conduct experiments using public datasets involving four different architectures (i.e., PE-32, PE-64, ELF-32, and ELF-64) and demonstrate that our model achieves an exceptionally high detection accuracy (i.e., 99.30%). After pruning 100% of noncritical nodes and edges, the sample size is reduced to approximately 8% of the original, with an average time cost reduction of 74.7%, while the detection performance fluctuation averages only about 1%. Extensive cross-dataset evaluations validate the effectiveness and efficiency of the proposed method.

Different from traditional object detectors such as YOLO, Detection Transformers (DETR) have reshaped the landscape of object detection by replacing heuristic-driven components like Non-Maximal Suppression with a fully end-to-end framework based on one-to-one Hungarian matching. While the majority of research has focused on improving the slow training convergence of DETR, this work investigates their security from an adversarial perspective. We unveil a critical vulnerability stemming directly from DETR’s core design: the deterministic one-to-one mapping between object queries and ground-truth objects can be exploited. This allows an adversary to craft perturbations that selectively manipulate specific target objects – causing them to vanish or be misclassified – while preserving the detection integrity of all other objects in the scene. Our initial analysis reveals that conventional gradient-based attacks are ill-suited for this task, as they induce unintended interference on non-target instances, a phenomenon we term as the “spillover effect”. To overcome this, we re-formulate the attack optimization by incorporating a novel penalty term that explicitly decouples the adversarial influence on target and non-target objects. Furthermore, we provide theoretical analysis to derive perturbation bounds under which the optimal matching assignments remain invariant, offering deeper insights into the model’s stability. Extensive experiments on standard benchmarks demonstrate that our proposed attack significantly improves the success rate and convergence speed while inducing far fewer feature-level artifacts, making the attack both more effective and stealthier.

With the increasing integration of measurement devices, the control and observation of power distribution systems have become significantly more dependent on cyberspace, making them more vulnerable to false data injection attacks (FDIAs). Contrary to the detection of FDIAs in power transmission systems, less attention has been paid to power distribution systems due to poor data quality, large volume sizes, and unbalanced data. This article proposes a dual-stage localization detection strategy for FDIAs in power distribution systems to detect and localize stealth FDIAs. Considering the time scale, arithmetic, and calculation overhead, this strategy can be transformed into two stages: presence detection and localization detection. Specifically, a cost-sensitive loss convolutional neural network based on Gaussian mixture autoencoder architecture is leveraged to capture data features from unbalanced data in presence detection. In localization detection, a Markov chain based on a cumulative state transfer probability (CSTP) is leveraged to locate the FDIAs exactly after presence detection. In this subject, presence detection can support the operator in rapidly filtering out compromised data, and localization detection can drive the control center to deploy countermeasures accurately. Based on the adjusted IEEE 14 and 118-bus test systems, numerical results indicate the effectiveness of the proposed strategy.

Distributed energy systems are shifting from stand-alone microgrids to networked multienergy microgrids (N-MEMGs), where energy management must handle multienergy coupling, renewable uncertainty, and strict device/network safety under distributed operation. A safety-guaranteed framework that couples a multi-actor single-critic multiagent deep reinforcement learning method with randomized ensembled double$Q$-learning (REDQ) and a model-based safety layer is proposed. Actors run locally at nodes for decentralized operation; a centralized REDQ-based critic module reduces$Q$-value overestimation and reliably guides policy updates; and the safety layer analytically enforces device, power balance, and multitimescale constraints, explicitly capturing inertia-induced effects along hydrogen device chains. On a five-node N-MEMG, the proposed method coordinates energy trading, hydrogen storage, and peak shaving, achieves zero observed safety violations over 12 000 evaluation steps, and outperforms mainstream baselines (multi-agent twin delayed deep deterministic policy gradient (MATD3), multiagent deep deterministic policy gradient, and multiagent soft actor critic) with an average daily adaptive normalized score of 98% across test days. The results point to a practical route to safe and scalable energy management in N-MEMGs.

In this article, we propose a planning-operation coordinated mitigation scheme for load redistribution (LR) attacks to overcome the deficiencies of separately designed phase shifting transformer-based mitigation strategies. Specifically, the interactions amongst the defender, attacker, and system are formulated as a trilevel optimization, where the deployment of defense devices and phase shift angles can be optimized according to possible operation state. Based on the proposed load similarity metric, a clustering-based approximate solution is designed to reduce the computational complexity caused by the integration of planning and operation stages. Simulation results on the IEEE 14-bus and 30-bus test systems verify the performance of the proposed mitigation scheme and the clustering-based approximate solution method.

Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally known as misclassifying attacks. By targeting the real-time processing capability, a new class of latency attacks has been reported recently. They exploit new attack surfaces in object detectors by creating a computational bottleneck in the post-processing module, which leads to cascading failure and puts the real-time downstream tasks at risk. In this work, we take an initial attempt to defend against this attack via background-attentive adversarial training that is also cognizant of the underlying hardware capabilities. We first draw system-level connections between latency attacks and hardware capacity across heterogeneous GPU devices. Based on the particular adversarial behaviors, we utilize objectness loss as a proxy and build background attention into the adversarial training pipeline, and achieve a favorable balance between clean and robust accuracy. The extensive experiments demonstrate the effectiveness of the defense in restoring real-time processing capability from 13 FPS to 43 FPS on Jetson Orin NX, with a better trade-off between the clean and robust accuracy. The source code is available at: https://github.com/Hill-Wu1998/underload.

Metaverse is a virtual world parallel to reality, where users can socialize with others in the form of the digital avatars. However, due to the excessive reliance on the avatar in the interaction process, there are vulnerabilities such as identity forgery. Attackers can steal the appearance and voice of the victim’s avatar and create a similar disguised avatar. Unlike fake identities on traditional social platforms, identity forgery in the Metaverse requires imitation of appearance and voice, and interactive behaviors must be synchronized in real time. To study this threat, an avatar disguise attack in the Metaverse scenario is proposed, and the effectiveness of the attack is verified. With the utilization of avatar creation tools and the Generative Pre-trained Transformer for the Speech-to-Video Voice Transformation System (GPT-SoVITS) model combined with Convolutional Recurrent Neural Networks (CRNN), appearance imitation and voice cloning are carried out. Then we manipulate the disguised avatar to deceive the victim into providing sensitive information and/or inducing behavior that harms virtual assets. The experimental results conducted on the Xirang and VSVR Metaverse platforms confirmed the feasibility of the disguised attack. The misrecognition rate of blind tests of appearance imitation is as high as 77%, and the success rate of the disguised attack after voice cloning can reach up to 60%. This attack poses a serious threat to identity security in the Metaverse.

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the Internet of Things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this article, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. First, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Second, a new indicator is proposed to measure the effectiveness of MTDper. Third, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems.

Despite significantly enhancing system flexibility and reliability, the adoption of distributed secondary control in DC microgrids (DCmGs) introduces new vulnerabilities to false data injection (FDI) attacks. As a typical FDI attack, the zero trace stealthy (ZTS) attack has been recently disclosed for DCmGs, which can deteriorate the control objective while keeping stealthy to unknown input observer (UIO)-based detectors. In this work, we investigate the optimal deployment of ZTS attacks, where the adversary with limited resources aims to compromise a set of communication links such that the system state convergence error can be maximized. Specifically, we formulate the optimal ZTS attack deployment problem as a combinatorial optimization problem and unveil its NP-hard characteristic. Then, we discover the submodularity in the state convergence error function, enabling us to transform the original NP-hard problem into a tractable submodular maximization problem. Furthermore, based on the submodular optimization theory, we propose a novel distributed algorithm for the optimal ZTS attack deployment in DCmGs, which effectively balances the attack benefits and computation cost. Finally, comparisons between the centralized and distributed algorithms are illustrated through extensive simulations.

Industrial control systems (ICSs) govern the production activities of various critical infrastructures, where programmable logic controllers (PLCs) are essential devices for controlling industrial processes. However, PLCs have many vulnerabilities and might be configured inappropriately. With the trend of PLCs connecting to the Internet, such weaknesses will lead to various cyberattacks and have prompted many studies on the threat assessment for PLCs. Previous research has ignored PLCs’ security settings, such as operating mode and read/write authentication etc., which are the general security functionalities significantly affecting PLCs’ security. In this paper, we make the first attempt to propose a security settings-based threat assessment framework (SSTAF) to assess PLCs’ security.SSTAFconsists ofSScanner, a novel scanner to automatically extract the real-time configurations of security settings from PLCs, and the threat assessment criteria, serving to assess the appropriateness of PLC configurations and analyze risk levels of attacks based on PLCs’ security settings. Subsequently, usingSSTAF, we implement an Internet-wide threat assessment for PLCs exposed to the Internet. We deploySScanneron the Internet and interact with 41K ICS devices in cyberspace to acquire their configurations of security settings. Based on the scanning result and the threat assessment criteria, we reveal that 93.32% of PLCs have not appropriately configured their security settings. Additionally, each PLC might be subject to 4.96 attacks on average, of which 3.32 attacks are due to the inappropriate configurations of security settings.

With the advancement of carbon-neutral and new power system construction, numerous information devices are continuously connected to power distribution systems, gradually breaking the original unobservable state of power distribution systems and making them more vulnerable to false data injection attacks (FDIAs). Contrary to most existing research focusing on the unbalanced network, less attention has been paid to the influence of randomness and fluctuation of distributed photovoltaic (PV) to perform FDIAs in the power distribution system. In this article, the failure mechanism of FDIAs and the improved FDIAs method are proposed simultaneously for the distribution system with a high penetration of distributed PV scenarios. Specifically, based on the reactive power optimization process, the randomness and fluctuation of distributed PV are applied to decrease significantly the stealthiness of the FDIAs. Subsequently, an improved FDIA method, based on time-dependent loss conditional generative adversarial networks, is proposed to enhance the stealth and effectiveness of the attack. Finally, numerical results based on the modified IEEE 33 bus test systems demonstrate the effectiveness of the failure mechanism and the improved FDIAs. Research results can facilitate the execution of countermeasures for distribution systems with a high penetration of distributed PV, posing serious and pressing security concerns in power distribution systems with a high penetration of distributed PV scenarios.

In recent years, numerous studies have explored how to allocate limited defense resource among meters to increase difficulties for launching attacks in power systems. However, existing studies often simplify this problem by assuming the linearity of attack cost functions, which creates an inevitable gap between theoretical analysis and practical scenarios. In this article, general nonlinear attack cost functions are considered in the formulation of the optimal defense resource problem, resulting in a mixed-integer nonlinear programming problem. This poses a challenging task for numerical methods or popular commercial solvers. To address this issue, an advanced slime mould algorithm with specialized initialization and evolutionary schemes is proposed. Specifically, a customized initialization strategy is designed such that the population can be easily initialized within the nonconvex feasible region aligning with the nonlinear constraints. Besides, in the evolutionary process, the fitness function is well-designed to encourage the individuals violating nonlinear constraints to evolve toward feasible directions. Comprehensive case studies verify that, compared to the state-of-the-art solvers, the proposed approach can achieve satisfactory defense resource allocation results in terms of feasibility, optimality, and real-time performance.

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from the perspective of the power system and ML model, respectively. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) smart grid applications. Overall, our purpose is to encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs.

The physics-aware watermarking-based detection method has shown great potential in detecting stealthy False Data Injection Attacks (FDIAs) by adding appropriate watermarks to control commands or sensor measurements, especially in industrial control systems and grid-tied Distributed Energy Resources (DERs). However, existing watermarking-based detection methods have limitations in either handling the intricate physical couplings among DERs or characterising the fast changing power electronics dynamics, and thus cannot be directly applied to microgrids. Inspired by the methodology of Unknown Input Observer (UIO), which can be employed for the distributed anomaly monitoring in cyber-physical microgrids but would be easily bypassed once the adversary has the knowledge of certain electrical parameters, this paper makes the first attempt to investigate the physics-aware watermarking embedded in UIOs such that the stealthy FDIAs would be intentionally disrupted by the watermarking scheme. Based on the theoretical analysis of the detection enhancement and performance degradation under watermarking-enhanced UIOs, the watermark strengths, UIO parameters, and control gains are optimally co-designed to significantly enhance the detection effectiveness while not degrading the control performance. The robustness of the watermarking-enhanced UIO to Time Synchronisation Errors (TSEs) is improved by employing a sliding time window with appropriate length. The performance of the proposed method is validated through Matlab/Simulink studies and cyber-physical co-simulation experiments, and the sensitivities of the detection latency and TSE robustness to watermark strength and detection window’s length are comprehensively studied.

The widespread use of programmable logic controllers (PLCs) in critical infrastructures has given rise to escalating cybersecurity concerns regarding PLC attacks. As a proactive defense mechanism, PLC honeypots emulate genuine controllers to engage adversaries so as to observe their attack tactics and techniques. As part of the arms race between the offense and defense, multiple PLC honeypot identification tools have been developed. However, many existing tools cannot recognize high-fidelity honeypots, since they rely on identifying common network services and fingerprints. In this paper, we propose an innovative and practical honeypot identification framework calledHoneyJudge, which goes beyond state-of-the-art (SOTA) network fingerprint-based identification tools like Nmap and the PLCScan tool.HoneyJudgetests the suspected target’s special memory content and features. Specifically,HoneyJudgemodels the internal memory of a PLC in three categories, from system-level, user-level, to process-level categories, based on which it extracts six representative memory features. All characteristics are acquired through automated network request messages. Then, we design a weighted voting algorithm to combine the test results over different memory features to reach the final conclusion. We validate the effectiveness ofHoneyJudgein comparison with several SOTA honeypot identification tools, and the results indicate that the memory-related issues have not been well addressed in existing PLC honeypots and still need substantial research efforts.

Enabled by the widely existed distributed flexible alternating current transmission system devices in power systems, moving target defense (MTD) has been considered as an effective way to detect stealthy false data injection (FDI) attacks. However, due to the limitation of power system topology, not all stealthy FDI attacks can be detected in power system with MTD. In this article, the authors propose a joint meter coding (MC) and moving target defense (MC-MTD) method to cost-effectively improve the detection of stealthy FDI attacks through integrating MC with MTD. Detection conditions and requirements on MC-MTD are theoretically analyzed, which reveal the close coupling between MC and MTD in collaboratively detecting stealthy FDI attacks. The design of the coding matrix and the selection of encoded measurements are theoretically analyzed to integrate MC with MTD in a special case that the coding matrix is diagonal. An optimization of MC-MTD is formulated and approximately solved to improve detection effectiveness with a small defending cost. Finally, simulations are carried out on both direct and alternating current state estimations to validate the performance of MC-MTD.

Programmable logic controllers (PLCs), the essential components of critical infrastructure, play a crucial role in various industrial manufacturing processes. Recent attack events show that attackers have a strong interest in tampering with the controller variables, such as the device status and internal program logic. A typical attack strategy is that the attackers just send malicious network traffic of industrial control protocols (ICPs) to change the controller variables of PLCs. To defend against this attack, a lot of countermeasures have been proposed to detect anomalies in network traffic based on the semantic analysis.

Machine learning-based intelligent systems enhanced with Internet of Things (IoT) technologies have been widely developed and exploited to enable the real-time stability assessment of a large-scale electricity grid. However, it has been extensively recognized that the IoT-enabled communication network of power systems is vulnerable to cyberattacks. In particular, system operating states, critical attributes that act as input to the data-driven stability assessment, can be manipulated by malicious actors to mislead the system operator into making disastrous decisions and thus cause major blackouts and cascading events. In this article, we explore the vulnerability of the data-driven power system stability assessment, with a special emphasis on decision tree-based stability assessment (DTSA) approaches, and investigate the feasibility of constructing a physics-constrained adversarial attack (PCAA) to undermine the DTSA. The PCAA is formulated as a nonlinear programming problem considering the misclassification constraint, power limits, and bad data detection, computing potential adversarial perturbations that reverse the “stable/unstable” prediction of the real-time input while remaining invisible/stealthy. Extensive experiments based on the IEEE 68-bus system are conducted to evaluate the impact of PCAAs on predictions of DTSA and their transferability.

The integration of distributed energy resources (DER) into the smart grid through digitalization has transformed the power grid into a more decentralized system, enhancing energy efficiency and resilience during significant catastrophes. However, the integration of a large number of DERs into the transmission and distribution networks poses reliability challenges due to the intermittent nature of renewable energy sources. To tackle this, smart grids have been using advanced metering infrastructure (AMI) and Internet of Things (IoT) devices for over two decades to improve grid observability and enable near-real-time forecasting of continent-wide anomalies. Cloud-edge-end orchestrated computing can achieve hierarchical management and innovative operational strategies, such as multi-level control and optimization of all-grid-level DERs, voltage and frequency regulation using phasor measurement units (PMU), and special protection schemes (SPS) to detect and prevent potential faults or large-scale cyber-attacks.

As a critical infrastructure, the traditional power system has transformed into a cyber-physical integrated smart grid. However, the vulnerabilities exposed in either the cyber or physical layer might be exploited by adversaries to construct complicated and coordinated attacks consequent in destructive impacts. In this paper, we propose a stealthy physics-manipulated attack (SPMA) by masking the physical attacks on the flexible AC transmission system (FACTS) with strategic cyberattacks. To construct the SPMA, we first manipulate the control command sent to the FACTS device to change the reactance and then tamper with the sensor measurements to conceal it. The SPMA is constructed with complete-informed and incomplete-informed attackers, noisy sensor measurements, and a nonlinear AC model, respectively. The impact of the physics manipulation on the real-time economic dispatch and the system’s operation security are formulated and numerically analyzed. Furthermore, we also provide potential countermeasures from three aspects to defend against SPMAs. Finally, extensive experiments are conducted with the IEEE test power systems to evaluate the stealthiness of SPMAs and the economic losses and potential cascading failures caused by SPMAs using real-world load profiles.

The watermarking method, which adds unique watermarks to data, has been widely used for integrity attack detection in industrial control systems (ICSs). Existing literature generally designs watermarking mechanisms without considering the existence of noises, which cannot be trivially applied to realistic ICS scenarios in the presence of strong noise interference. On one hand, the low-intensity watermarking will be ineffective under the strong noise environment; while on the other hand, the oversized watermarking can possibly degrade the control performance or even destabilize the system. Therefore, the intensity of watermarks plays a fundamental role in balancing the tradeoff between detection effectiveness and control performance, which, to the best of our knowledge, has never been thoroughly analyzed yet. To this end, in this paper, we for the first time propose an optimal watermarking design method for ICSs considering the detection-performance tradeoff. To begin with, we shift the watermark container from data points to segments and update the detection metrics to reduce the noise impact. Then, we formulate an optimization problem to determine the strength of watermarks to balance the detection-performance tradeoff. Meanwhile, the detection effectiveness and control performance metrics are analytically modeled and theoretically analyzed considering the discrepancy between added watermarks and noises, signal quality, detection latency, as well as estimation of detection metrics. Finally, extensive numerical simulations and systematical experiments based on a practical Ethanol Distillation ICS are conducted to validate the theoretical analysis and demonstrate the outperformance of our proposed watermarking method in comparison with related works.

Due to the close relevance to the reliability and efficiency of power systems, network parameters such as branch admittance have been the target of various cyberattacks. However, existing attack models are generally based on the impractical assumption that attackers can directly modify the data of network parameter stored in well-secured control centers. This article proposes a practical attack model and designs an optimal strategy to detect malicious modification of critical network parameters. Specifically, the vulnerability of network parameter error processing is discovered and exploited to indirectly modify the data of network parameter without accessing to the well-secured control center. A model of false-data-injection-enabled network parameter modification is proposed, which significantly reduces the requirements on attackers’ capability and system information. An optimal detection strategy is designed based on the analysis of the minimal protection set at a single branch, which can significantly reduce the number of protected measurements in detecting malicious modification of critical network parameters. Finally, numerical simulations are carried out on the PJM 5-bus and the IEEE 118-bus test systems to validate the theoretical results.

Moving target defense (MTD) is a new defensive mechanism developed in power systems to thwart false data injection attacks (FDIAs). However, since the MTD works by perturbing the branch parameters with the distributed flexible ac transmission system (D-FACTS), it might cause additional infrastructure and operation costs and affect the system dynamics. This is a complicated problem because it is closely related to which branches should be perturbed and how much they are changed. In this article, we analyze the essentials of MTD and construct an effective and low-cost MTD. To begin with, we provide a sufficient and necessary condition for MTD to protect a bus from being affected by the intended FDIA. Based on this result, we propose a new metric to quantify the protection level of MTD and an efficient algorithm to minimize the number of required D-FACTS devices for protecting a specific set of buses. To reduce the operation cost, we develop two strategies to make the increasing operation cost zero for activating the MTD. Furthermore, we analyze the impact of MTD on the system dynamics with a special emphasize on small signal stability. Finally, we conduct extensive simulations to validate our findings with the test cases of power systems in MATPOWER.

With the widespread adoption of Internet of Things (IoT) technologies, cyber–physical systems (CPSs) are facing threats from cyberattacks due to the vulnerabilities exposed in IoT devices. In this article, we analyze the feasibility of a coordinated attack, named TD-FDIA, on CPS by the synchronizing the time-delay attack (TDA) and false data injection attack (FDIA). It seems that the coordinated attack is more powerful than either one. But the analysis of its stealthiness and effectiveness is challenging. In the context of the networked control system, we first propose a general formulation for the impact of TD-FDIA on the system’s stability. Then, we analyze whether the combination of TDA and FDIA can destabilize the system and remain stealthy or not with different setups when the controller is with and without an observer, and the communication protocol between the controller and actuator is UDP and TCP, respectively. The conditions required to make TD-FDIA stealthy are given in some cases. Finally, we conduct extensive experiments to evaluate the impact of TDA, FDIA, and TD-FDIA on the system’s stability with three different CPS scenarios.

The smart grid (SG), as one of the largest evolutionary critical infrastructures, witnesses the deep integration of electricity facilities and Internet of Things (IoT). But recent events show that the vulnerabilities exposed in IoT devices can be exploited by adversaries to construct the cyberattacks on SG. To address this threat, plenty of countermeasures have been proposed to enhance the SG’s security. However, the additional costs introduced by some countermeasures make the utilities hesitate to implement them practically. To alleviate this concern, in this article, we propose a double-benefit moving target defense (dB-MTD) to protect the SG from cyber–physical attacks (CPAs) and also gain generation-cost benefits. The dB-MTD enables the prevention of stealthy CPAs on the transmission lines by perturbing the reactances with the distributed flexible AC transmission system (D-FACTS). To reduce the infrastructure cost, we minimize the number of required D-FACTS devices for a specific protection goal. Although it needs investment on D-FACTS, we find that the utility can make profits from the generation costs by appropriately setting the reactance perturbations. Therefore, we formulate an optimization problem to compute the optimal reactance perturbations to maximize the generation-cost benefits, without sacrificing the protection performance of dB-MTD. Finally, using the real-world load profiles, we conduct extensive simulations to evaluate the impact of CPA on the system operation and the benefits obtained by the dB-MTD from the aspects of the D-FACTS deployment and the generation-cost profits.

Sensor sharing is an important ability for vehicle platoon to reduce vehicle costs and improve driving safety. However, sensing data with a large amount of information is difficult to be shared by using traditional communication methods. Applying millimeter-wave communication can improve sensor sharing capabilities of vehicles, but millimeter wave has large propagation loss and is easy to be blocked by obstacles. Therefore, how to apply millimeter-wave communication to improve sensor sharing capabilities in vehicle platoon is still an open issue. To this end, we present a stochastic game approach and propose two distributed algorithms to achieve high-reliability and low-energy sensor sharing. First, we model the multihop millimeter-wave sensor sharing of the vehicle platoon as an optimization problem of reducing energy consumption while ensuring communication reliability, and then based on the characteristics of vehicle communication, we transform the optimization problem into a multiagent stochastic game. Second, by utilizing the characteristic that the immediate cost has nothing to do with the transfer function of the game, we prove that this stochastic game has equilibrium by proving that each subgame has a Nash equilibrium. Third, we propose an equilibrium-guided multiagent distributed (EGMD) algorithm to obtain the optimal action of each vehicle in the platoon, and then based on the analysis of the limitation of the approach, we present a multiagent distributed cooperation (MDC) algorithm to further reduce the system energy consumption. Finally, through simulation experiments, we evaluate a variety of approaches and demonstrate that our proposed methods can significantly reduce energy consumption while ensuring communication reliability.

Deploying machine learning (ML)-based intrusion detection systems (IDS) is an effective way to improve the security of industrial control systems (ICS). However, ML models themselves are vulnerable to adversarial examples, generated by deliberately adding subtle perturbation to the input sample that some people are not aware of, causing the model to give a false output with high confidence. In this article, our goal is to investigate the possibility of stealthy cyber attacks towards IDS, including injection attack, function code attack and reconnaissance attack, and enhance its robustness to adversarial attack. However, adversarial algorithms are subject to communication protocol and legal range of data in ICS, unlike only limited by the distance between original samples and newly generated samples in image domain. We propose two strategies - optimal solution attack and GAN attack - oriented to flexibility and volume of data, formulating an optimization problem to find stealthy attacks, where the former is appropriate for not too large and more flexible samples while the latter provides a more efficient solution for larger and not too flexible samples. Finally, we conduct experiments on a semi-physical ICS testbed with a high detection performance ensemble ML-based detector to show the effectiveness of our attacks. The results indicate that new samples of reconnaissance and function code attack produced by both optimal solution and GAN algorithm possess 80 percent higher probability to evade the detector, still maintaining the same attack effect. In the meantime, we adopt adversarial training as a method to defend against adversarial attack. After training on the mixture of orginal dataset and newly generated samples, the detector becomes more robust to adversarial examples.

Runtime log-based anomaly detection is one of several key building blocks in ensuring system security, as well as post-incident forensic investigations. However, existing log-based anomaly detection approaches that are implemented on large-scale Internet of Things (IoT) systems generally upload local data from edge devices to a centralized (cloud) server for processing and analysis. Such a workflow incurs significant communication and computation overheads, with potential privacy implications. Hence, in this paper, we propose a customizable and communication-efficient federated anomaly detection scheme (hereafter referred to as FedLog), designed to facilitate the identification of abnormal log patterns in large-scale IoT systems. Specifically, we first craft a Temporal Convolutional Network-Attention Mechanism-based Convolutional Neural Network (TCN-ACNN) model, to effectively extract fine-grained features from system logs. Second, we develop a new federated learning framework to support IoT devices in establishing a comprehensive anomaly detection model in a collaborative and privacy-preserving manner. Third, a lottery ticket hypothesis based masking strategy is designed to achieve customizable and communication-efficient federated learning in handling non-Independent and Identically Distributed (non-IID) log datasets. We then evaluate the performance of our proposed scheme with those of DeepLog (published in CCS, 2017) and Loganomaly (published in IJCAI, 2019) in both centralized learning and federated learning settings, using two publicly available and widely used real-world datasets (i.e., HDFS and BGL). The findings demonstrate the utility of the proposed FedLog scheme, in terms of log-based anomaly detection.

Data integrity attack (DIA) is one class of threatening cyber attacks against the Internet-of-Things (IoT)-based smart grid. With the assumption that the attacker is capable of obtaining complete or incomplete information of the system topology and branch parameters, it has been widely recognized that the highly synthesized DIA can evade being detected and undermine the smart grid state estimation. However, the branch parameters cannot be easily obtained or inferred by the attacker in practice. They can be changed or disturbed with time. In this article, we complete the class of DIA by designing the zero-parameter-information DIA (ZDIA), which makes it possible for the attacker to execute stealthy data tampering attacks without any information of the branch parameters. Only the topology information about the cut line is required to construct such attack. We prove that, the attacker can arbitrarily modify the state estimate of a one-degree bus, which is connected to the outside only by a single cut line; and modify the state estimates of all buses, with the same arbitrary bias, in a one-degree super-bus, which is a group of buses that is connected to the outside only by a single cut line. Besides, we extend ZDIA to the cases where a bus and super-bus are connected to the outside only by several cut lines. Moreover, we propose two countermeasures to address the topology vulnerability exploited by ZDIA, and present a branch perturbation strategy to defend against general DIAs. Finally, we conduct extensive simulations with the IEEE standard power systems to validate the theoretical results.

After the sensing-based semipersistent scheduling (SPS) is introduced in the media access control layer of the long-term evolution vehicle-to-everything (LTE-V2X) Mode 4, many tests have been conducted to measure its performance. However, until now, there is still no clear mathematical expression for the reliability of this scheduling. To this end, in this article, we attempt to provide the lower and upper bounds of the reliability and propose a distributed algorithm for improving reliability. First, we give a mathematical description of sensing-based SPS and apply packet pass rate (PPR) to represent its reliability. Then, we analyze the SPS without sensing and present a theoretical expression of the reliability, which is described as a function of channel busy rate (CBR). Second, an iterative approach is applied to analyze the sensing-based SPS, and the mathematical expressions of lower and upper-reliability bounds are derived. Third, based on the existing condition of the upper bound, we propose a reliability improvement solution, which utilizes a distributed algorithm to process added information, such as the counter and the offset for the remaining storage space. Finally, this solution is applied to LTE-V2X, and simulation shows that the proposed scheme can significantly improve the scheduling reliability.

In building-microgrid communities, renewable generation and time-varying load usually cause power fluctuations, which influence the ancillary support to the main grid. Thermostatically controlled loads (TCLs) can be utilized to compensate such power variations due to their aggregated and controllable power consumptions. Meanwhile, one basic requirement for the users' side of TCLs is to realize the fair sharing of power states and comfort states. This article proposes a distributed event-based control strategy, where information of neighboring TCLs is exchanged only when a dynamic event-triggered condition is satisfied, and thus it intelligently determines the necessary transmission frequency to save communication resources. From a cybersecurity perspective, the communication network of TCLs may be subject to hybrid attacks, for example, denial-of-service (DoS) and false data-injection (FDI) attacks. During DoS attack intervals, no information can be communicated even through the event-triggered condition is satisfied. Furthermore, the control inputs may also be tampered by FDI attacks. By utilizing the Lyapunov stability and hybrid control theories, sufficient conditions regarding the attack parameters are derived such that fair sharing of power states and comfort states of all involved TCLs can be achieved exponentially. The exclusion of Zeno behaviors is proved and a corollary for ideal communication situations is also deduced. Finally, simulation examples with various attack parameters are conducted to verify the effectiveness of the main results.

In recent years, aiming to reduce the metropolitan air pollution caused by fossil fuel-powered vehicles, the electrification of transportation, such as electric vehicles (EVs) and electric buses (EBs), has attracted great attention from the automobile industry, academia, and public transportation. EBs, driven by decarbonized electricity, can reduce the air pollution and noise level. Besides, they can also recover electricity from regenerative braking. Recent years have witnessed continuous works on the topics of EB energy storage, power management, and charging scheduling. In this review, we have comprehensively surveyed three primary parts: important components; existing research topics; and open issues of EBs. Specifically, we first introduce the important components of EBs, including energy storage systems, powertrains, interleaving elements and electric motors, and driving cycles. Then, we review the existing research topics of EBs, including the energy storage system sizing, power/energy management, range remedy methods, charging design/scheduling, and trial projects. At last, extending from existing literatures, we further propose the future research opportunities and ongoing challenges, such as extending EV related research to EBs, EB charging demand modeling, and EB impact on power systems.

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario.

Recent studies have exploited moving target defense (MTD) for thwarting false data injection (FDI) attacks against the state estimation (SE) by actively perturbing branch parameters (i.e., impedance or admittance) in power grids. To hide the activation of MTD from attackers, a new strategy named hidden MTD has been proposed by the latest literature. A hidden MTD can increase the defender’s chance to detect FDI attacks and avoid the attacker from inferring new branch parameters. However, by using an MTD-confirming detector like the bad data detection (BDD) checker in SE, we observe that it is still possible for the attacker to detect this hidden MTD when the power flows change with time. To uncover the insight of MTD’s hiddenness, we study the conditions needed for achieving a hidable MTD. We find that the hiddenness of MTD is closely related to the branch perturbations, system topology, and attacker’s knowledge. From the attacker’s perspective, we prove that an MTD can be detected by the attacker only if he/she knows the previous parameters of a set of branches that forms a circle and the measurements corresponding to those branches after MTD. But once the attacker has full knowledge of branch parameters before MTD and has obtained all measurements after MTD, it is proved that we can never achieve a hidable and effective MTD. From the defender’s perspective, since it is impossible to know the attacker’s capability, we cannot determine whether a constructed MTD is hidable or not by purely depending on the MTD design. To address this issue, we propose that, by protecting a basic set of measurements, we always can achieve a hidable and effective MTD regardless of the changes of power flows, the attacker’s knowledge, and the branch perturbations. Furthermore, we validate our findings with the IEEE standard test power systems.

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches l in the power system is not less than twice that of the system states n (i.e., l ≥ 2n, where n + 1 is the number of buses); (ii) the susceptances of more than n branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

Recent studies have investigated the possibilities of proactively detecting the high-profile false data injection (FDI) attacks on power grid state estimation by using the distributed flexible ac transmission system (D-FACTS) devices, termed as proactive false data detection (PFDD) approach. However, the feasibility and limitations of such an approach have not been systematically studied in the existing literature. In this paper, we explore the feasibility and limitations of adopting the PFDD approach to thwart FDI attacks on power grid state estimation. Specifically, we thoroughly study the feasibility of using PFDD to detect FDI attacks by considering single-bus, uncoordinated multiple-bus, and coordinated multiple-bus FDI attacks, respectively. We prove that PFDD can detect all these three types of FDI attacks targeted on buses or super-buses with degrees larger than 1, if and only if the deployment of D-FACTS devices covers branches at least containing a spanning tree of the grid graph. The minimum efforts required for activating D-FACTS devices to detect each type of FDI attacks are, respectively, evaluated. In addition, we also discuss the limitations of this approach; it is strictly proved that PFDD is not able to detect FDI attacks targeted on buses or super-buses with degrees equalling 1.

In this paper, we consider false data injection (FDI) attacks with limited information of transmission-line susceptances and new countermeasures in smart grids. First, we prove that the adversary could launch FDI attacks to modify the state variable on a bus or superbus only if he/she knows the susceptance of every transmission line that is incident to that bus or superbus. Based on this observation, we provide a new countermeasure against FDI attacks, i.e., to make the susceptances of n-1 interconnected transmission lines that cover all buses unknown to the adversary (e.g., by proactively perturbing transmission-line susceptances through distributed flexible AC transmission system (D-FACTS) devices), where n is the total number of buses. This new countermeasure can work alone or in conjunction with traditional ones to reduce the number of meter measurements/state variables that are to be secured against FDI attacks. The implementation of FDI attacks with limited susceptance information and the effectiveness of new countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

In the future, smart grid charging stations will be critical infrastructures for plug-in electric vehicle (PEV) to replenish their batteries in a convenient way. Due to the ever-increasing penetration rate of PEVs, how to efficiently manage the loads of PEV charging stations to ensure system efficiency and reliability is a major challenge faced by the distribution service providers (DSPs) in the smart grid. This challenge is further complicated by the highly dynamic PEV mobility, which results in random PEV arrivals, departures, and charging demands. In order to address this challenge, a stochastic game approach is proposed in this paper to characterize the interactions among DSP, charging stations, and PEV owners, where the randomness in charging decision making processes of PEV owners is modeled by a Markov decision process. Based on the Nash equilibrium solution of the stochastic game, a real time pricing scheme is proposed for the DSP to minimize power distribution losses while ensuring system reliability. The performance of the proposed approach is evaluated via extensive simulations based on the IEEE 123 bus test feeder with real vehicle mobility data from the 2009 National Household Travel Survey and the 2010 National Travel Survey.

By equipping with the advanced smart meters and two-way communications infrastructure, smart grids, as a key component of future smart cities, are able to improve the energy efficiency and reduce the energy cost through real-time monitoring and customer load scheduling. However, the high penetration of intermittent renewable energy such as solar power may cause frequent overvoltage and undervoltage problems at certain buses, making the load scheduling face new challenges on voltage regulation. In this paper, we investigate the impact of voltage constraints on load scheduling by power flow analysis in a power distribution system with renewable generators. A voltage regulator (VR) is introduced to regulate the voltage of buses in the distribution system and assist load scheduling. To jointly minimize the cost and stabilize the voltages of the distribution system, we propose a grid-customer coordinated load scheduling strategy, which simultaneously determines the tap changes of the VR and scheduling of customer electricity loads in each time slot. Finally, we evaluate the performance of the proposed strategy based on realistic power demand and renewable energy generation datasets. Extensive numerical results demonstrate that the proposed strategy can remarkably reduce the energy cost and stabilize the voltage fluctuation of distribution systems.

In this paper, we studied the transmission performance of underwater wireless networks, where underwater network users (UNUs) can transmit their data through wireless optical and acoustic communication in a certain range to improve the overall underwater networks. By jointly considering UNUs' volume of data transferred and overall network transmission performance, we introduced an auction game based optical and acoustic communication mechanism (AGOC). With AGOC mechanism, the base transceiver station (BTS) sells wireless optical communication chances through auctions. The users will decide whether to bid according to their own situation, and then the winner could use wireless optical to transmit finally. The simulation results verified the effectiveness of our proposed algorithm. It also be concluded that AGOC mechanism could improve the overall underwater wireless network performance through reducing the number of UNUs contending for the wireless optical channel.

Under the dynamic pricing environment, electric vehicle (EV) owners are faced with the EV charging and discharging scheduling problem to minimize the electricity cost. On one hand, in existing literatures that only consider the EV charging scenario, the solution cannot be trivially extended to the EV discharging scenario. On the other hand, existing approaches for solving integer programming either have the exponential computational complexity or cannot guarantee the optimal solution. In this paper, instead of focusing on the action selection at each time slot, we traverse all possible states within the state transition process, since the total number of states is much smaller than that of action schedules. Then, the computational complexity of the problem solving can be reduced from an exponential order to a polynomial one. Algorithms with both certain and uncertain future electricity prices are developed. It is demonstrated with real price data from Commonwealth Edison Company that our proposed algorithms can facilitate the optimization solution.

This paper investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders' decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems.

The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.

Mobile crowdsourcing, as an emerging service paradigm, enables the computing resource requestor (CRR) to outsource computation tasks to each computing resource provider (CRP). Considering the importance of pricing as an essential incentive to coordinate the real-time interaction among the CRR and CRPs, in this paper, we propose an optimal real-time pricing strategy for computing resource management in mobile crowdsourcing. Firstly, we analytically model the CRR and CRPs behaviors in form of carefully selected utility and cost functions, based on concepts from microeconomics. Secondly, we propose a distributed algorithm through the exchange of control messages, which contain the information of computing resource demand/supply and real-time prices. We show that there exist real-time prices that can align individual optimality with systematic optimality. Finally, we also take account of the interaction among CRPs and formulate the computing resource management as a game with Nash equilibrium achievable via best response. Simulation results demonstrate that the proposed distributed algorithm can potentially benefit both the CRR and CRPs. The coordinator in mobile crowdsourcing can thus use the optimal real-time pricing strategy to manage computing resources towards the benefit of the overall system.

Under the real-time electricity pricing environment in smart grid, building owners are faced with the indoor temperature control problem to minimize the daily electricity cost. Taking the heating scenario as an example, an intuitive strategy is to maintain the building's indoor temperature always at the lower bound of the predetermined comfort range. However, this strategy may not always achieve the lowest electricity bill, especially with the significant fluctuation of electricity prices. On the other hand, the cost minimization problem can be optimally solved one day ahead in a temporally- coupled manner, but the challenge lies in that the building owner needs to acquire the accurate information of electricity prices and outdoor temperatures of the next day, which may not be available. In this paper, we equivalently decouple the cost minimization problem into subproblems at each hour. Each subproblem can be temporally decoupled and optimally solved, only requiring the next-hour electricity price. Besides, the temporally-decoupled algorithm explicitly indicates when to take advantage of pre-heating/cooling for electricity cost reduction. It is demonstrated with the real data that our proposed algorithm could result in considerable economic savings compared with the intuitive strategy, paving the way towards practically applicable cost-effective smart buildings.

In this paper, we propose a behavior rule-based methodology for insider threat (BLITHE) detection of data monitor devices in smart grid, where the continuity and accuracy of operations are of vital importance. Based on the dc power flow model and state estimation model, three behavior rules are extracted to depict the behavior norms of each device, such that a device (trustee) that is being monitored on its behavior can be easily checked on the deviation from the behavior specification. Specifically, a rule-weight and compliance-distance-based grading strategy is designed, which greatly improves the effectiveness of the traditional grading strategy for evaluation of trustees. The statistical property, i.e., the mathematical expectation of compliance degree of each trustee, is particularly analyzed from both theoretical and practical perspectives, which achieves satisfactory tradeoff between detection accuracy and false alarms to detect more sophisticated and hidden attackers. In addition, based on real data run in POWER WORLD for IEEE benchmark power systems, and through comparative analysis, we demonstrate that BLITHE outperforms the state of arts for detecting abnormal behaviors in pervasive smart grid applications.

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated.

Abstract The growing peak‐hour power demand has invoked an urgency to increase the peak‐hour supply. Although smart grid has been envisioned as the next generation power system due to its two‐way communication of information and power, the peak‐hour power shortage problem still exists. In this paper, we propose a credit‐based incentive tariff (CIT) scheme with fraud‐traceability for smart grid. Specifically, the CIT encourages retail customers to sell the power generated by their renewable resources back to the grid during peak hours via giving additional incentive rate to them based on their credits. If a fraud is detected during the power transaction, the malicious customer's identity can be traced out and his or her credit can be correspondingly reduced. The security analysis shows that the CIT resists various security threats and makes the incentive tariff fair and more secure. The performance evaluation demonstrates that the CIT can dramatically increase the peak‐hour supply and reduce the peak‐to‐average power demand ratio by up to 7%. Copyright © 2013 John Wiley & Sons, Ltd.

With the increasing trend in adoption of plug-in hybrid and plug-in electric vehicles, they will play a prominent role in the future electric energy market by acting as responsive loads to increase the grid stability and facilitate the integration of renewables. However, due to the large number of controllable devices in the future grid, central vehicle to grid (V2G) management would be challenging and vulnerable to single points of failure. This paper introduces a novel distributed approach for optimal management of unidirectional V2G considering multiple energy suppliers. Each charging station as well as each energy supplier is equipped with a local price regulator to control the price paid to the energy suppliers and the price paid by the vehicles through coordination with their neighbors. In response to the updated prices, the vehicles adjust their charging rates and energy suppliers adjust their production to maximize their benefit. The main advantages of the proposed approach are that it manages unidirectional V2G in a fully distributed way considering multiple energy suppliers and vehicles, and it converges to the global optimum despite the greedy behavior of the individuals.

Fog computing, characterized by extending cloud computing to the edge of the network, has recently received considerable attention. The fog is not a substitute but a powerful complement to the cloud. It is worthy of studying the interplay and cooperation between the edge (fog) and the core (cloud). To address this issue, we study the tradeoff between power consumption and delay in a cloud-fog computing system. Specifically, we first mathematically formulate the workload allocation problem. After that, we develop an approximate solution to decompose the primal problem into three subproblems of corresponding subsystems, which can be independently solved. Finally, based on extensive simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

As the next generation power grid, smart grid is characterized as an informationized system, and demand response is one of its important features to deal with the ever-increasing peak energy usage. However, the supply capacity and required demand make the demand response problem with both spatially and temporally coupled constraints, which, to the best of our knowledge, has not been thoroughly investigated in a distributed manner. The complexity lies in how to guarantee privacy and convergence of distributed algorithms. Aiming at this challenge, in this paper, we first propose a distributed algorithm, which is based on dual decomposition and does not require each user to reveal his/her private information. Then, the convergence analysis is conducted to provide guidance on how to choose the proper step size; through which, we notice that the convergence speed of the subgradient projection method is not fast enough and it is highly dependent on the choice of the step size. Therefore, to increase the convergence rate of the distributed algorithm, we further propose a fast approach based on binary search. Finally, the distributed algorithms are illustrated by numerical simulations and the extensive comparison results validate the better performance of the fast approach.

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

In this paper, we investigate the network throughput and energy sustainability of green-energy-powered maritime wireless communication networks. Specifically, we study how to optimize the schedule of data traffic tasks to maximize the network throughput with Worldwide Interoperability for Microwave Access technology. To this end, we formulate it as an optimization problem to maximize the weight of the total delivered data packets, while ensuring that harvested energy can successfully support transmission tasks. The formulated energy and content-aware vessel throughput maximize problem is proved to be NP-complete. We propose a green energy and content-aware data transmission framework that incorporates the energy limitation of both infostations and delay-tolerant network throw boxes. The green energy buffer is modeled as a G/G/1 queue, and two heuristic algorithms are designed to optimize the transmission throughput and energy sustainability. Extensive simulations demonstrate that our proposed algorithms can provide simple yet efficient solutions in a maritime wireless communication network with sustainable energy.

Data collection by multiple sinks is a fundamental problem in wireless sensor networks. Existing work focused on designing optimal offline algorithms provided that the number and positions of sensors and sinks are predetermined. This may not be practical as, though sensors are cheap, sinks are quite expensive in reality. A more practical scenario is that sinks are deployed step by step during the network operation due to the budget constraint, and we do not know the number, positions and capacities of sinks in prior. In this paper we investigate such an optimal data collection problem by multiple sinks, and design a near-optimal online algorithm via primal-dual approach, requiring very little priori knowledge. We theoretically derive the competitive ratio and show how to improve it by finding the optimal sink location region with an approximation ratio. Extensive simulations are conducted to verify the performance of the proposed online algorithm.

Coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. However, designing an efficient coordinated charging strategy to route mobile EVs to fast-charging stations for globally optimal energy utilization is very challenging. In this paper, we investigate a special smart grid with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits vehicular ad-hoc networks (VANETs) to support real-time communications among road-side units (RSUs) and highly mobile EVs for collecting real-time vehicle mobility information or dispatching charging decisions. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. Specifically, the mobility-incurred travel cost for an EV is considered in two aspects: 1) the travel distance from the current position of the EV to a charging station; and 2) the transmission delay for receiving a charging decision via VANETs. The optimal mobility-aware coordinated EV charging problem is formulated as a time-coupled mixed-integer linear programming problem. By solving this problem based on Lagrange duality and branch-and-bound-based outer approximation techniques, an efficient charging strategy is obtained. To evaluate the performance of the proposed strategy, a realistic suburban scenario is developed in VISSIM to track vehicle mobility through the generated simulation traces, based on which the travel cost of each EV can be accurately calculated. Extensive simulation results demonstrate that the proposed strategy considerably outperforms the traditional EV charging strategy without VANETs on the metrics of the overall energy utilization, the average EV travel cost, and the number of successfully charged EVs.

In this paper, we study power allocation in cognitive sensor networks where cognitive users (cognitive enabled sensor nodes) opportunistically share a common spectrum with primary users (licensed devices). We define an energy efficiency-oriented utility function as a new metric to evaluate power allocation. Consider that sensor nodes are self-interested to maximize their own utility, we formulate the energy efficient power allocation problem as a non-cooperative game. We firstly prove that there exist Nash equilibriums in the proposed game. Secondly, we prove that the power allocation game is a supermodular game with some conditions. Finally, we use best response algorithm to identify the Nash equilibrium. Simulations are conducted to demonstrate that the proposed power allocation strategy can achieve satisfactory performance in terms of energy efficiency, convergence speed and fairness in cognitive sensor networks.

Nowadays, with the dramatically increased penetration of wireless access, the conflict between spectrum scarcity and under-utilisation is becoming more and more aggravating. A promising technology to tackle such challenge is cognitive radio, of which spectrum sensing is one of the most important functionalities. In this study, the authors consider an essential problem of energy-efficient spectrum sensing in cognitive radio networks. Although most existing works of spectrum sensing mainly focus on determining an optimal sensing time to maximise the detection probability and/or to minimise the false alarm probability, our problem of how to schedule the power-constrained sensor is much more challenging, because of the trade-off among interests of the primary user, secondary user and sensor. The authors formulate it as a non-linear optimisation problem to maximise the sensor lifetime, with necessary constraints of quality and delay of spectrum sensing, and throughput for performance guarantee of primary and secondary users. Moreover, the authors incorporate the distribution information of channel occupancy/vacancy durations into the problem to yield a desirable solution. They propose a novel framework to obtain the optimal energy-efficient periodic scheduling by adopting both non-linear programming and linear programming. Extensive simulation results are provided to validate our theoretical analysis.

Protocol testing has been one of the most active fields in computer networks. In Wireless Sensor Networks (WSNs), before directly employing protocols on hardware testbed, we expect an effective test framework to verify the logical correctness of protocols on computers, which could facilitate the whole test process. Towards this goal, this paper introduces an upper test framework in WSNs, namely Protocol Testing Framework for WSNs (PTFW).