Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users’ devices, called clients. Only the training results, called gradients, are sent to the server to be aggregated which is used to generate an updated model. However, we cannot assume that the server can be trusted with the sensitive information, such as metadata related to the owner or source of the data. So, hiding client information from the server helps in reducing privacy-related attacks. Therefore, the privacy of the client’s identity, along with the privacy of the client’s data, is necessary to prevent such attacks. This paper proposes an efficient and privacy-preserving protocol for FL based on group signatures. A new group signature for federated learning, called GSFL, is designed to not only protect the privacy of the client’s data and identity but also significantly reduce the computation and communication costs considering the iterative process of federated learning. We show that GSFL outperforms existing approaches in terms of computation, communication, and signaling costs. Also, we show that the proposed protocol can handle various security attacks in the federated learning environment. Moreover, we provide security proof of our protocol using a formal security verification tool, Automated Validation of Internet Security Protocols and Applications (AVISPA).

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms.

In a flying ad hoc network (FANET), unmanned areal vehicles (UAV) communicate to route the message from the source node to the destination. Often, these UAVs carry sensitive information that needs to be transmitted with full confidentiality without revealing the identity of the sender node. Federated learning (FL) is an emerging machine learning approach that can protect the privacy of nodes in the network. However, implementing FL in FANET is challenging due to the highly dynamic network and limited resources. In the existing distributed FL, clients need to communicate with other clients participating in FL to exchange key information, which increases the overhead significantly. Therefore, we propose a group signature-based federated algorithm that can enhance the protection of the nodes’ identity and significantly reduce the overheads by eliminating the need to exchange key information among the drones. Our simulation results show that the proposed algorithm achieves significantly lower computation cost, communication cost, and signaling overhead than existing works. We also verify the security of our algorithm for various known attacks using AVISPA.

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.

Successful deployment of Long-Range Wide Area Network (LoRaWAN) technology in several Industrial Internet of Things (IIoT) scenarios, such as Outage Management System (OMS) in smart metering, rely on low energy consumption of the end device. In this work, we conducted an experiment to demonstrate an on-off Denial-of-Service (DoS) attack to analyze the impact on the energy consumption of the LoRaWAN end device. We implemented the attack that manipulates the end device to remain in packet retransmission mode for several seconds. The conducted experiments show that the configurable parameters of LoRaWAN that are required for applications, like OMS, are susceptible to energy consumption attacks. In summary, our results show that when an on-off DoS attack is performed, the end device utilizing the Spreading Factor (SF) 12 consumes 92 times more energy due to packet retransmissions as compared to the end node using SF 7 under no attack.

Crowdsourcing can be applied to provide scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract and retain a sufficient number of participants to provide a sustainable crowdsourcing service. However, there exist some gaps between the modeled entities or markets in the existing works and those in reality: 1) dichotomous task valuation and workers' punctuality and 2) crowdsourcing service market monopolized by a platform. To bridge those gaps of such impractical assumption, we model workers' heterogeneous punctuality behavior and task depreciation over time. Based on those models, we propose an expected social welfare maximizing (ESWM) mechanism that aims to maximize the expected social welfare (ESW) by attracting and retaining more participants in the long term, i.e., multiple rounds of crowdsourcing. In the evaluation, we modeled the continuous competition between the ESWM and one of the existing works in both short-term and long-term scenarios. The simulation results show that the ESWM mechanism achieves higher ESW and platform utility than the benchmark by attracting and retaining more participants. Moreover, we prove that the ESWM mechanism achieves the desirable economic properties: individual rationality, budget balance, computational efficiency, and truthfulness.

For industrial Internet of Things (IIoT), data trustworthiness should be maintained both at the time of sensing and at the time of transmission. This article is concerned with trustworthiness during transmission, which is determined by transmission reliability. We present a low-complexity and flexible method via partial and recursive superposition to improve the transmission reliability of IIoT, resulting in an IIoT with high trustworthiness. In our method, a portion of the previously transmitted data are superimposed onto the current transmitted data to introduce memory among different transmissions, which are then exploited by the windowed decoder to obtain performance gain. The proposed method is referred to as partially recursive block Markov superposition transmission of low-density parity-check (PrBMST-LDPC) codes. This article is focused on the construction of low-complexity PrBMST-LDPC codes since IIoT is resource-limited in nature. The first construction is the memory-one PrBMST-LDPC code. We present a simplified density evolution algorithm to optimize the superposition ratio for memory-one PrBMST-LDPC code. Both the analytical and numerical results show that PrBMST with memory one can be used to reduce the packet loss ratio (PLR) of IIoT using LDPC codes. Particularly, around 1.0 dB performance gain is obtained by PrBMST. We then present a low-complexity construction for PrBMST-LDPC codes with encoding memory larger than one. Simulation results show that compared with memory-one PrBMST, a further PLR reduction of around one order of magnitude can be obtained.

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.

Recently, malicious mining using CPUs has become a trend - mining which the task is not detected by the users is even more of a threat. In this paper, we focused on discovering a new IA-32\footnoteIt stands for Intel Architecture-32bit. It is the 32-bit version of the x86 instruction set architecture which supports 32-bit computing. vulnerability and found an undetectable task using hardware task switching method. The created task is undetectable by the operating system and thus hidden from the system user. Although hardware task switching methods are replaced by more convenient software switching methods in the recent years, they still exist on modern computer systems. By manually manipulating hardware task switching, which is directly managed by the CPU, we show that it is possible to create a hidden scheduler aside from the ones created by the operating system. We demonstrate using a simple CPU consumption example that these hidden tasks have potential to evolve into more sophisticated malicious attacks that can go unnoticed by users.

This paper designs a distributed state estimation scheme considering cyber attacks using the internet of things (IoT) technologies. The IoT sensors are utilised to get synchronous generator information. After locally estimating the system states, the attack occurs during transmission of sensors information to the remote estimator. In the fusion center, the convex optimization problem is developed to estimate the generator states. Lastly, the feedback controller is proposed to regulate the generator states. Numerical results demonstrate that the developed algorithm can properly estimate and regulate the generator states.

This paper develops a state estimation and stabilisation scheme for monitoring and controlling the wind turbine. Basically, the estimation scheme is designed considering the Bayesian tree network. The estimated system states are corrected in the forward and backward direction of this network where the estimation errors are enforced to minimise. Therefore, the estimated system state converges to the actual states as time goes by. Furthermore, the optimal feedback controller is designed. Interestingly, the proposed algorithms are applied to the environment-friendly wind turbine, and it shows that the developed methods can effectively estimate and stabilise the turbine states.

This paper develops a distributed state estimation approach for smart grids. Particularly, the designed filter is developed in an interconnected way where the packet dropouts take place between estimators. The error function between true and estimated states is written in compact form, then it can be transformed into the linear matrix inequality (LMI). After solving the LMI problem, the desired gains are determined for the smart grid state estimation. The proposed method is applied to the IEEE 14-bus system where system state and input matrices are obtained from the Holt-Winters approach.

The industry has recognized the risk of cyber-attacks targeting to the advanced metering infrastructure (AMI). A potential adversary can modify or inject malicious data, and can perform security attacks over an insecure network. Also, the network operators at intermediate devices can reveal private information, such as the identity of the individual home and metering data units, to the third-party. Existing schemes generate large overheads and also do not ensure the secure delivery of correct and accurate metering data to all AMI entities, including data concentrator at the utility and the billing center. In this paper, we propose a secure and privacy-preserving data aggregation scheme based on additive homomorphic encryption and proxy re-encryption operations in the Paillier cryptosystem. The scheme can aggregate metering data without revealing the actual individual information (identity and energy usage) to intermediate entities or to any third-party, hence, resolves identity and related data theft attacks. Moreover, we propose a scalable algorithm to detect malicious metering data injected by the adversary. The proposed scheme protects the system against man-in-the-middle, replay, and impersonation attacks, and also maintains message integrity and undeniability. Our performance analysis shows that the scheme generates manageable computation, communication, and storage overheads and has efficient execution time suitable for AMI networks.

Navigating security and privacy challenges is one of the crucial requirements in the vehicle-to-grid (V2G) network. Since electric vehicles (EVs) need to provide their private information to aggregators/servers when charging/discharging at different charging stations, privacy of the vehicle owners can be compromised if the information is misused, traced, or revealed. In a wide V2G network, where vehicles can move outside of their home network to visiting networks, security and privacy become even more challenging due to untrusted entities in the visiting networks. Although some privacy-preserving solutions were proposed in the literature to tackle this problem, they do not protect against well-known security attacks and generate a huge overhead. Therefore, we propose a mutual authentication scheme to preserve privacy of the EV's information from aggregators/servers in the home as well as distributed visiting V2G networks. Our scheme, based on a bilinear pairing technique with an accumulator performing batch verification, yields higher system efficiency, defeats various security attacks, and maintains untraceability, forward privacy, and identity anonymity. A performance analysis shows that our scheme, in comparison with the existing solutions, significantly generates lower communication and computation overheads in the home and centralized V2G networks, and comparable overheads in the distributed visiting V2G networks.

The smart grid, as the next generation of the power grid, is characterized by employing many different types of intelligent devices, such as intelligent electronic devices located at substations, smart meters positioned in the home area network, and outdoor field equipment deployed in the fields. In addition, there are various users in the smart grid network, including customers, operators, maintenance personnel, and so on, who use these devices for various purposes. Therefore, a secure and efficient mutual authentication and authorization scheme is needed in the smart grid to prevent various insider and outsider attacks on many different devices. In this paper, we propose an authentication and authorization scheme for mitigating outsider and insider threats in the smart grid by verifying the user authorization and performing the user authentication together whenever a user accesses the devices. The proposed scheme computes each user role dynamically using an attribute-based access control and verifies the identity of the user together with the device. Security and performance analysis show that the proposed scheme resists various insider as well as outsider attacks, and is more efficient in terms of communication and computation costs in comparison with the existing schemes. The correctness of the proposed scheme is also proved using BAN-Logic and Proverif.

For the future smart grid, decentralized inverter control is essential in distributed generation (DG) microgrids where a powerful central controller is unavailable for cost and reliability concerns. However, decentralized inverter control suffers from a limited system stability mainly because of the lack of communications among different inverters. In this paper, we investigate the stability enhancement of the droop based decentralized inverter control in microgrids. Specifically, we propose a power sharing based control strategy which incorporates the information provided by a wireless network to improve system stability. The wireless network is used to acquire the total real and reactive power generation of all DG units in a decentralized manner. Based on the desired power sharing of each DG unit and the acquired information of total generation, additional control terms are added to the traditional droop controller. We evaluate the performance of our proposed control strategy based on small-signal stability analysis. Extensive numerical results are presented to demonstrate the system stability.

As an important component of smart grid, the vehicle-to-grid (V2G) system is recently introduced to enable bidirectional energy delivery between the power grid and plug-in electric vehicles. Communication technology is incorporated to facilitate the energy delivery by providing electricity pricing and energy demand information. However, different from the stationary energy storage systems, the energy store-carry-and-deliver mechanism for a V2G system poses new challenges for performance optimization, such as bi-directional energy flow and non-stationary energy demand. How to utilize the statistical information provided by the communication system to achieve efficient energy delivery is critical for a V2G system and is still an open issue. In this paper, we address a specific problem in this new research area, i.e., daily energy cost minimization of vehicle owners under time-of-use (TOU) electricity pricing. We investigate a plug-in hybrid electric vehicle (PHEV) with a realistic battery model, which is general for both battery electric cars and plug-in hybrids. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and TOU electricity price. We prove the optimality of a state-dependent double-threshold (or (S, S')) policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average (EWMA) algorithm is used to estimate the statistics of PHEV mobility and energy demand. The performance of the proposed scheme is demonstrated by simulations based on survey and real data collected from Canadian households. Numerical results indicate that our proposed scheme performs closely to a scheme with a priori knowledge of the PHEV mobility and energy demand information. Compared with the existing approaches, the proposed scheme can achieve energy cost reduction, which increases with the battery capacity.

As essential building blocks of the future smart grid, microgrids can efficiently integrate various types of distributed generation (DG) units to supply the electric loads at the minimum cost based on the economic dispatch. In this paper, we introduce a decentralized economic dispatch approach such that the optimal decision on power generation is made by each DG unit locally without a central controller. The prerequisite power generation and load information for decision making is discovered by each DG unit via a multiagent coordination with guaranteed convergence. To avoid a slow convergence speed which potentially increases the generation cost because of the time-varying nature of DG output, we present a heterogeneous wireless network architecture for microgrids. Low-cost short-range wireless communication devices are used to establish an ad hoc network as a basic information exchange infrastructure, while auxiliary dual-mode devices with cellular communication capabilities are optionally activated to improve the convergence speed. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs. The performance of the proposed schemes is analyzed and evaluated based on real power generation and load data collected from the Waterloo Region in Canada. Numerical results indicate that our proposed schemes can better utilize the cellular communication links and achieve a desired tradeoff between the communication and generation costs as compared with the existing schemes.

In this paper, we propose a distributed asynchronous clock synchronization (DCS) protocol for Delay Tolerant Networks (DTNs). Different from existing clock synchronization protocols, the proposed DCS protocol can achieve global clock synchronization among mobile nodes within the network over asynchronous and intermittent connections with long delays. Convergence of the clock values can be reached by compensating for clock errors using mutual relative clock information that is propagated in the network by contacted nodes. The level of clock accuracy is depreciated with respect to time in order to account for long delays between contact opportunities. Mathematical analysis and simulation results for various network scenarios are presented to demonstrate the convergence and performance of the DCS protocol. It is shown that the DCS protocol can achieve faster clock convergence speed and, as a result, reduces energy cost by half for neighbor discovery.

A synchronization error is unavoidable in mobile wireless multihop networks, especially in sparse networks, due to the lack of effective synchronization algorithms. Fortunately, some level of clock synchronization is possible among nodes. In this paper, we propose a distributed semi-asynchronous sleep scheduling protocol (DSA) considering loosely synchronized clocks in sparse mobile wireless networks. The sleep schedules are constructed to guarantee contacts among distributed nodes having synchronization errors. Also, the protocol can be optimized using the distribution of the synchronization error to maximize the energy efficiency. With simulation results, we demonstrate that the DSA can achieve higher energy efficiency than existing asynchronous sleep scheduling protocols, especially for large synchronization errors.

In this paper, we focus on power management for Delay/Disruption Tolerant Network (DTN), and propose two asynchronous clock-based sleep scheduling protocols that are distributed, adaptive, and energy efficient. Moreover, the sleep schedules can be constructed using simple systematic algorithms. We also discuss how the proposed protocols can be implemented in mobile devices for adapting to dynamic network conditions in DTN. Theoretical analysis is given to demonstrate the energy efficiency and scalability of the proposed protocols. Simulation results show that the proposed protocols reduce the energy consumption in the idle listening mode up to 35 percent in comparison with other existing asynchronous clock-based sleep scheduling protocols, and more than 90 percent compared with the protocol without power management, while maintaining comparable packet delivery delay and delivery ratio.

Global clock synchronization is important to mobile wireless multihop networks that require precise timing information for data collection and energy conservation in MAC layer protocols. In this paper, we propose a global clock synchronization protocol for Delay Tolerant Network (DTN). The protocol achieves global clock synchronization under asynchronous, long delayed, and intermittent network dynamics of DTN by compensating clock errors with contacted nodes using propagated relative clock information and updating stored information according the compensated logical clock information. Simulation results are given to demonstrate the convergence speed and robustness of the proposed protocol.

Recently, considerable research efforts have been devoted to Delay/disruption Tolerant Network (DTN) in order to enable communications between disconnected network entities. In this paper, we focus on power management for DTN, and propose two asynchronous clock based power saving protocols with distributed adaptive sleep scheduling protocols. The proposed protocols allow different levels of power saving and are robust to long delayed and intermittent network dynamics of DTN. Analytical and simulation results are given to demonstrate the energy efficiency and scalability of the proposed protocols. In addition, we show how the proposed protocols can be applied to the mobile devices for adapting to dynamic network conditions of DTN in order to maximize energy efficiency.

In this paper, a new power saving mechanism in delay/disruption tolerant networks is designed. By exploiting the intermittent connection characteristic of delay/disruption tolerant network in synchronized clock based scenario, an adaptive exponential beacon protocol is proposed where the beacon periods of nodes are independently adjusted depending on the trend of contact availability. The proposed protocol is optimized for different network environments using distribution of contact durations. Simulation results show that power savings up to 35 percent are achieved compared with existing power saving protocols, while maintaining similar average packet delays and packet delivery ratios to that without a power management.