Modern grid infrastructures rely heavily on connected devices and real-time communication for efficient and secure operation. While traditional machine learning, deep learning, and graph-based methods have shown success in detecting classic false data injection attacks (FDIAs), which manipulate readings using simple perturbation strategies, their performance degrades significantly against evasion attacks where adversaries generate adversarial samples to fool detection models without requiring system knowledge. Moreover, traditional graph-based approaches suffer from high computational overhead when trained on large power system topologies. In this work, we propose implementing a graphon neural network (WNN)-based detector against evasion attacks, assuming a realistic threat scenario where the adversary has no prior system knowledge. Unlike graph neural networks, which require retraining when the topology changes, WNNs enable efficient and transferable learning by modeling families of graphs that share structural properties. Experimental results demonstrate that our WNN-based detector maintains robust detection against evasion attacks with a 1–2% deterioration in detection rate, compared to a 9–10% deterioration in benchmark models, while enhancing decision-making latency by 116% and reducing training time by 133% compared to benchmarks.

The growing adoption of electric vehicles (EVs) increases the demand for a reliable and efficient charging infrastructure. Unexpected disruptions at electric vehicle charging stations (EVCSs) can negatively impact traffic flow and energy distribution. Traditional EV rerouting strategies to EVCS are limited by their static nature and fail to consider the interdependence between transportation and power distribution networks. Thus, traditional rerouting strategies are unable to effectively reroute EVs during EVCS disruptions that might occur due to maintenance, lack of public EVCS, or power unavailability, leading to congestion, increased travel time, and energy inefficiencies. In this paper, we propose a graph-based rerouting framework that integrates transportation and power networks to dynamically redirect EVs from disrupted to functional EVCSs while accounting for EVCS-to-EVCS distance, station capacity, and route capacity. The proposed method incorporates three years of travel monitoring analysis system traffic data with a synthetic power distribution network to capture realistic operating conditions. We introduce the EV rerouting with disruptions and capacity constraints (EVR-DC) algorithm, which incorporates system-level constraints and enables decentralized decision-making under disruptions. Our results show that the proposed approach reduces disrupted EVCS congestion by 73% within an hour and maintains an annual EV user rerouting satisfaction rate of 86%, demonstrating the effectiveness of the proposed method in enhancing the resilience of EV charging infrastructure against disruptions.

Unmanned Aerial Vehicle (UAV) swarms’ ability to dynamically adapt to communication topologies brings both great potential and significant security risks. Traditional intrusion detection systems (IDS) are designed for UAVs with fixed swarm topology and fail to secure dynamic topologies with constantly evolving communication patterns. The lack of datasets that reflect swarm behaviors across varying topologies further hinders the development of IDS. Moreover, these IDS typically focus on temporal information, overlooking the crucial spatial relationships within the UAV swarm. To address these challenges, we developed a testbed of six UAVs configured to communicate in six distinct topological graphs where each UAV acts as a node and communicates with its immediate neighbors. We then executed various cyberattacks, such as false data injection (FDI), evil twin, replay, and denial-of-service (DoS) attacks, and collected the data under both normal and attack conditions. We propose a graph neural network (GNN)-based IDS that explicitly incorporates spatial and temporal information patterns to detect intrusions. This paper seeks to answer the following questions: (a) Does exploiting spatio-temporal correlations improve detection compared to IDS trained solely on temporal data? (b) Can we develop a generalized IDS capable of detecting intrusions irrespective of the specific swarm communication topology? (c) Does the spatio-temporal correlation improve detection capabilities when the range of swarm topological data is expanded in training and the IDS is tested on unseen/new topology? Through extensive experiments on varying swarm topologies and comparison with traditional deep neural network models, we assess the effectiveness of the topology-aware GNN-based IDS in securing UAV swarm communications.

As a sustainable participant in the modernization of transportation systems, electric vehicles (EVs) call for a well-planned charging infrastructure. To meet the ever-increasing charging demands of EVs, an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs) is necessary. With newly allocated CSs, additional distributed generators (DGs) are required to compensate for the load increase. Given a budget to be allocated over a certain time horizon, we formulate the joint spatio-temporal CSs and DGs planning problem as a multi-objective optimization problem. During each planning period, the allocation strategy aims at minimizing the total power generation costs and CSs/DGs installation costs while satisfying budgetary and power constraints and ensuring a minimum level for the charging requests satisfaction rate. In this regard, we first predict the future power demand of EVs using a graph convolutional neural network (GCNN). Then, using the power demand forecast, we obtain the optimal number and locations of CSs and DGs at each time stage using reinforcement learning. A case study of the proposed allocation strategy over 6 time stages for the 2000-bus power grid of Texas coupled with 720 initially existing CSs is presented to illustrate the performance of the planning strategy.

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

Smart grids are faced with the challenge of meeting the ever increasing load demands of electric vehicles (EVs). To provide acceptable charging services, operators need to be equipped with an efficient charging stations (CSs) planning strategy. Unfortunately, existing planning solutions are quite limited. They normally rely on standard IEEE bus systems or power grids that are specific to certain cities. In this paper, using stochastic geometry, we formulate the CSs planning on a stochastic geometry-based power grid model, that we previously showed to mimic real-world power grids. We study the effect of the density of EVs and the technical charging constraints on the minimum number of CSs to install.

The tight interaction between power grids and communication networks supports the advanced functionalities of smart grids and enables a more efficient utilization of the assets within the power system. Towards this objective, it is of utmost importance to develop a model that reflects such an interaction. Unfortunately, existing cyber-physical interdependent models are conceptual or are specific to certain regions and do not consider the spatial information and correlation of electrical elements. To address these limitations, in this paper, we propose an interdependent communication-power network model using tools from stochastic geometry, which takes into consideration the spatial locations of electrical buses and the required service quality within the communication network. A step-by-step process on building a communication network on top of the power grid while satisfying the average queuing delay and queuing stability constraints is described. A case study for a realization of this generative interdependent model is presented.

Mobile health (m-Health) is one potential application of cyber-physical systems, where biomedical sensors and mobile devices interact tightly together to transmit medical data to an m-Health server. In this paper, we consider a three-tier hierarchical m-Health system: 1) the sensor network tier capturing vital signals; 2) the mobile computing network tier processing and routing the sensed data to a fixed remote location; and 3) the back-end network tier processing and analyzing the sensed medical data along with patient's medical history. Based on this architecture, a physical layer security scheme for the second tier is developed and network performance of the introduced scheme is analyzed under different metrics using stochastic geometry. To be specific, secure transmission range and average end-to-end delay are analyzed for two different strategies: the mobile device transmits: 1) to the nearest neighbor and 2) to the furthest neighbor. Furthermore, we consider the cases of full knowledge on eavesdroppers' locations and when such information is unavailable. Results show that transmitting to the nearest neighbor achieves the highest secure transmission distance with the lowest mean delay when full information on eavesdroppers is available.

The susceptibility of millimeter waveform propagation to blockages limits the coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we derive expressions for the downlink coverage probability of relay-assisted mmWave cellular networks when the D2D links are implemented in either uplink mmWave or uplink microwave bands. We further investigate the spectral efficiency (SE) improvement in the cellular downlink, and the effect of D2D transmissions on the cellular uplink. For mmWave links, we derive the coverage probability using dominant interferer analysis while accounting for both blockages and beamforming gains. For microwave D2D links, we derive the coverage probability considering both line-of-sight and non-line-of-sight (NLOS) propagation. Numerical results show that downlink coverage and SE can be improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve better coverage because D2D connections can be established under NLOS conditions. However, mmWave D2D relays achieve better coverage when the density of interferers is large because blockages eliminate interference from NLOS interferers. The SE on the downlink depends on the relay mode selection strategy, and mmWave D2D relays use a significantly smaller fraction of uplink resources than microwave D2D relays.

A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.

As the novel paradigm in the field of machine learning, reservoir computing possesses exceptional performance, e.g., energy efficiency, in tasks in which the traditional von Neumann computing systems cannot incorporate. This makes reservoir computing an ideal candidate to enable the sustainable development of cyber-physical systems (CPS). In the realm of CPS, the tight interaction among physical objects places security threats under the spotlight of attention. For such systems, especially the power grid network, false data injection could potentially lead to catastrophic consequences such as blackouts in large geographical areas. In this paper, we will introduce a reservoir computing architecture, the delayed feedback system, and apply the reservoir computing architecture for anomaly detection. To be specific, detailed design of the three imperative components in the delayed feedback system will be discussed and the corresponding energy efficiency performance will be analyzed. The application of the reservoir computing architecture to anomaly detection in a smart grid network will be introduced.

Millimeter-wave (mmWave) communications is one of the most promising candidate technologies for next generation cellular networks due to the global bandwidth shortage for mobile broadband access. The susceptibility of millimeter waveform propagation to blockages, however, may largely restrict the coverage of mmWave signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we develop a coverage probability model for the downlink of a relay- assisted mmWave cellular network using dominant interferer analysis, which accounts for both beamforming gains and blockages. Theoretical analysis and simulation results show that the downlink coverage of a mmWave cellular network can be improved by using two-hop D2D relay transmissions.

Supporting massive numbers of machine-type communication (MTC) devices poses several challenges for future 5G networks, including network control, scheduling, and powering these devices. A potential solution is to offload MTC traffic onto device-to-device (D2D) communication links to better manage radio resources and reduce MTC devices' energy consumption. However, this approach requires D2D users to use their own limited energy to relay MTC traffic, which may be undesirable. This motivates us to exploit recent advancements in RF energy harvesting for powering D2D relay transmissions. In this paper, we consider a D2D communication as an underlay to the cellular network, where D2D users access a fraction of the spectrum occupied by cellular users. This underlay model presents a fundamental trade-off: to protect cellular users, the spectrum available to D2D users needs to be reduced, which limits the number of D2D transmissions, but increases the amount of time that D2D users can spend harvesting energy to support MTC traffic. We study this trade-off by characterizing the spectral efficiency of MTC, D2D, and cellular users using stochastic geometry. The optimal spectrum partition factor is characterized to achieve fairness and balance in the network, while increasing the average MTC spectral efficiency.

Energy efficiency is one of the major challenges of 5G networks. As data rates are expected to increase by 1000x from 4G to 5G, energy efficiency will need to improve by about the same amount. Recently, energy harvesting techniques have attracted lots of attention from the scientific community, due to their ability to increase network lifetime. More specific, energy harvesting from ambient radio frequency (RF) signals is of special importance, especially with the recent RF circuit advancements. In this paper, we consider a device-to-device (D2D) communication in underlay cellular networks, where D2D users reuse the spectrum occupied by cellular users. We introduce the concept of spatial RF energy harvesting, where D2D users harvest RF power from uplink cellular transmissions, if it exceeds a predesigned threshold, in a spatial region. Using tools from stochastic geometry, we obtain a closed form expression for the probability of activating RF power conversion circuit by making full use of spatial locations of ambient RF signals. Subsequently, we study the impact of RF energy harvesting region radius to harvest sufficient power on the signal-to-interference (SIR) ratio of D2D network. Simulation results provide insights for the required advancements to design highly efficient RF harvesting circuits.

Device-to-Device (D2D) communication is one of the key technologies to get to very high data rates in future 5G networks through offloading part of the cellular traffic onto D2D networks. While extensive research is targeted on addressing the many challenges D2D brings along in cellular networks, security issues have not gained much attention, especially that the direct connections between proximity devices are more vulnerable to security threats, which in turn deteriorate users' experience. In this paper, we consider a D2D communication overheard by eavesdroppers in an underlay cellular network, where D2D users access a proportion of the spectrum occupied by cellular users. To mitigate the potential security threats, we turn towards a lightweight low-complexity approach by exploiting the physical characteristics of the wireless channels. Using tools from stochastic geometry, we derive the D2D transmission region and the average transmit power that guarantee a minimum secrecy rate and a target outage probability. Results reveal that when offloading less cellular traffic onto D2D links, the large cellular interference can be exploited to protect D2D links from eavesdroppers. On the other hand, with aggressive D2D offloading, D2D users can achieve a much higher secrecy capacity due to significantly reducing the distances between them.

With the explosive growth of mobile traffic demand, device-to-device (D2D) communication offers a promising approach to reduce cellular congestion by offloading users' traffic to D2D network. In this paper, we consider a relay- assisted D2D communication in underlay cellular networks, where D2D users access a proportion of the spectrum occupied by cellular users. This poses two fundamental issues. The first issue is related to protecting the cellular transmissions by reducing the spectrum partition factor, which measures the fraction of spectrum available to D2D users. The second issue is that as we limit the spectrum available to D2D users, less D2D transmissions can be supported, which in turn increases the distances between them. To address these issues, we allow a relay user to assist D2D communications in order to achieve higher D2D spectral efficiency without affecting cellular spectral efficiency. In fact, results show significant improvements in spectral efficiency not only to D2D, but to the whole network, especially in a dense D2D environment.

In this paper, we consider device-to-device (D2D) communication underlaying cellular networks, where D2D users harvest radio frequency (RF) power from uplink cellular transmissions. This paper addresses two important issues for energy harvesting-based D2D cellular networks. The first is how the energy harvested from cellular ambient RF signals affects the D2D spectral efficiency. Using tools from stochastic geometry, we investigate this issue by first characterizing the transmission probability of D2D transmitter as the probability of having enough battery power, and then obtaining analytic expressions of the spectral efficiency for D2D and cellular networks. The second issue is related to the cellular spectral efficiency: the more RF energy D2D users can harvest, the higher their transmission probability becomes, which leads to generating more interference in the cellular network. We carry out simulations to understand this impact of RF energy harvesting on the spectral efficiency of both D2D and cellular networks. Results show promising improvement to the whole network, in terms of the weighted spectral efficiency, when employing RF harvesting technology and when there are enough available channels in the network.

The technological advancements in the health care system have made possible the massive integration of biomedical sensors for monitoring patients' health and disease progression. In this paper, we consider a three tier medical body area network (MBAN): intra-MBAN, inter-MBAN, and beyond-MBAN. The intra-MBAN transmits sensors' data to a controller, which in turn transmits them in the inter-MBAN tier to an access device like a PDA or a tablet device, usually connected to a patient's medical database. The access device then serves as a mean of communication between intra-MBAN and beyond-MBAN to access the hospital information systems. This widely deployed design in hospitals places security and privacy violation threats on the spotlight of attention, especially in the inter-MBAN tier. This has motivated us to optimize the average MBAN controller transmit power that minimizes the probability of eavesdroppers overhearing the communication, using tools from stochastic geometry. We analyze this privacy protection scheme for eHealth systems through simulations. Results show that the proposed scheme achieves higher privacy protection, but at the expense of reduced coverage.

Cooperative schemes for critical content distribution over vehicular networks are presented and analyzed. The first scheme is based on unicasting from the base station, whereas the second is based on threshold based multicasting. Long Term Evolution (LTE) is used for long range communications with the base station (BS) and 802.11p is considered for inter-vehicle collaboration on the short range. A high mobility environment with correlated shadowing is adopted. Both schemes are shown to outperform non-cooperative unicasting and multicasting, respectively, when the appropriate 802.11p power class is used. The first scheme achieves the best performance among the compared methods, and a practical approximation of that scheme is shown to be close to optimal performance. © 2012 IEEE.

Relay-based multicasting for the purpose of cooperative content distribution is studied. Optimized relay selection is performed with the objective of minimizing the energy consumption or the content distribution delay within a cluster of cooperating mobiles. Two schemes are investigated. The first consists of the BS sending the data only to the relay, and the second scheme considers the scenario of threshold-based multicasting by the BS, where a relay is selected to transmit the data to the mobiles that were not able to receive the multicast data. Both schemes show significant superiority compared to the non-cooperative scenarios, in terms of energy consumption and delay reduction.

ABSTRACT Cooperative ad hoc networks for the efficient distribution of content of common interest are studied in the case of fast channel variations. Mobiles are grouped into cooperative clusters for the purpose of receiving the content with optimized energy efficiency. Data are sent to mobile terminals on a long range (LR) link, and then, the terminals exchange the content by using an appropriate short range wireless technology. When channel state information is available for the LR links, unicasting is used on the LR. When accurate channel state information is not available, threshold‐based multicasting is implemented on the LR. Energy minimization is formulated as an optimization problem for each scenario, and the optimal solutions are determined in closed form in scenarios with fast channel variations. Results show significant energy savings in the proposed schemes compared with the noncooperative case and other previous related work. Furthermore, the energy minimizing solutions are shown to lead to reduced delay in the content distribution process. Practical implementation aspects of the proposed methods are also discussed. Copyright © 2012 John Wiley & Sons, Ltd.