Location point set similarity range query aims to retrieve candidate point sets that are similar to the given point set in terms of location distribution patterns and geographical features, and it is vital in GIS (Geographic Information Systems), IoT (Internet of Things), and biometrics. Due to the economic and flexible advantages of cloud services, location data is frequently outsourced to cloud servers, which simultaneously increases the risk of privacy breaches. To address this, service providers choose to encrypt data before outsourcing. However, the existing schemes for similarity range query of encrypted location point sets have some problems, such as high computational complexity of measurements, which limit the query efficiency and security of schemes. To tackle these problems, this paper achieves the efficient and privacy-preserving location-point-set similarity range query for cloud computing (LPSQ). Firstly, we propose a lightweight similarity measurement called Geo-Jaccard similarity, to reduce the time complexity to$O(n)$. Secondly, to enhance the efficiency of the scheme, we integrate the kd-tree with pivot point technology to construct a pkd-tree, and design a corresponding filtering and verification algorithm. Thirdly, to enhance the security of our scheme, we encrypt the pkd-tree using a mix of matrix encryption and SHE (Symmetric Homomorphic Encryption), and design a series of protocols under SHE, such as SHE batch minimum value calculation protocol, SHE division protocol, and the approximation algorithm for computing Jaccard similarity securely. Finally, we prove that the security of our proposed LPSQ achieves CPA (Chosen Plaintext Attack) security. Furthermore, we conduct experiments to assess the performance, and the results demonstrate that LPSQ achieves sublinear search efficiency, while Geo-Jaccard similarity proves effective for similarity range queries on location point sets.

Traditional reverse k-nearest neighbor (RkNN) query schemes typically assume that users are available online in real-time for interactive key reception, overlooking scenarios where users might be offline. Moreover, existing privacy-preserving RkNN query schemes primarily focus on user features or spatial data, neglecting the significance of user reputation values. To address these limitations, we propose a privacy-preserving resilient RkNN query scheme over encrypted outsourced multi-attribute data (PRRQ). Specifically, to mitigate the challenges posed by resilient online presence (i.e., non-real-time online) of users for interactive key reception, we incorporate a non-interactive key exchange (NIKE) protocol and the Diffie-Hellman two-party key exchange algorithm to propose a multi-party NIKE algorithm (2K-NIKE), facilitating non-interactive key reception for multiple users. Considering the privacy leakage issues, PRRQ encodes original multi-attribute data (i.e., spatial, feature, and reputation values) alongside query requests based on formalized criteria. Additionally, we integrate the proposed 2K-NIKE and the improved symmetric homomorphic encryption (iSHE) algorithms to encrypt them. Furthermore, catering to the requirements of ciphertext-based RkNN queries, we propose a private RkNN query eligibility-checking (PREC) algorithm and a private reputation-verifying (PRRV) algorithm, which validate the compliance of encrypted outsourced multi-attribute data with query requests. Security analysis demonstrates that PRRQ achieves simulation-based security under anhonest-but-curiousmodel. Experimental results show that PRRQ offers superior computational efficiency compared to comparative schemes.

With the enormous scale of remote sensing imagery generation, on-orbit computing has become a crucial paradigm to enable near-real-time processing. Due to the limited onboard resources and on-orbit power supply, satellite edge computing (SEC) is developed for satellite-ground collaboration, aiding on-orbit computation. However, the intermittent satellite-to-ground transmission link poses an efficiency challenge when collaborating SEC resources. Therefore, this article proposes a satellite edge computing resource scheduling technique for on-orbit remote sensing imagery processing ($\textsf {SECORS}$). First, we design a remote sensing mission-specific SEC architecture, which involves an offline-online satellite working mode. Subsequently, a computational resource scheduling model ($\textsf {SEC}$-$\textsf {RSM}$) is established, including the directed acyclic graph (DAG) model and mathematical problem formulation. Next, to obtain effective scheduling solutions, we develop an end-to-end algorithm leveraging the multiagent proximal policy optimization and heuristic rule of the earliest finish time ($\textsf {SEC}$-$\textsf {MPH}$). Finally, we build a simulation SEC platform to carry out experiments and implement several methods as the comparison including multiobjective evolutionary algorithms, deep reinforcement learning approaches, and the scheme without optimization (baseline). Simulation results show that$\textsf {SECORS}$achieves 68.87% and 66.60% reductions in time and energy for on-orbit computation. Moreover, our method improves the energy efficiency ratio (EER) by three times and achieves high processing capacity with 548 pixels per unit of power (W) and time (ms).

Low Earth orbit satellite constellations with seamless network coverage and onboard computers enable autonomous on-orbit anomaly identification of remote wind turbines. However, they face several challenges. First, limited visible periods caused by orbital characteristics mandate that one satellite holds anomalies and the other collects surveillance data. Second, passively injected satellites could intercept and grasp onboard message flows. Third, a restricted onboard energy supply budget restrains intersatellite communications and onboard computations. With the above challenges, we propose a secure on-orbit anomaly identification (SOAI) scheme between a pair of satellites through an$\text{XOR}$filter, which further exploits laconic private set intersection to eliminate false positives. The secure on-orbit anomaly querying scheme achieves the verifiable querying of anomalies derived from$\text{SOAI}$. Comprehensive security analysis shows that the$\text{SOAI}$scheme achieves confidentiality under a simulation-based real/ideal world model. Moreover, we compare the$\text{SOAI}$scheme with two baseline schemes in terms of communication overheads and computational costs, and evaluation results show that our scheme outperforms the compared schemes, and our scheme is feasible in the OneWeb constellation near the polar regions.

The low-Earth orbit (LEO) satellite constellation holds immense potential for offshore wind farm surveillance since it can provide all-day and all-weather monitoring capabilities facilitated by satellite collaboration. However, it faces significant challenges. First, limited downlink transmission bandwidth constrained by ground stations and constraint on-orbit resources necessitate selective data downloads, focusing only on differences between consecutive data sets. Second, a passively injected satellite in open space poses a risk of unauthorized data extraction from neighboring satellites. Third, onboard energy constraints limit the feasibility of computationally intensive cryptographic operations. To tackle these challenges for the first time, we propose a novel secure and efficient on-orbit comparison (SEOC) scheme. Our solution begins with introducing a lightweight matrix encryption-based secure inner product (MSIP) technique tailored for secure on-orbit comparison. We further enhance communication efficiency by integrating a Cuckoo filter to reduce costs, complementing a novel difference comparison tree (DCTree) structure to manage false positives. Through comprehensive security analysis, the$\textsf {MSIP}$technique achieves selective security, and the$\textsf {SEOC}$scheme is secure under the universally composable (UC) framework. At last, performance evaluations demonstrate the high efficiency of our approach in terms of computational costs and communication overheads, which adapts to the limited on-orbit resources.

The role of on-orbit computing for satellites is transitioning from being a backup measure to becoming a primary key function. However, the limited computing resources available on satellites make it difficult to deploy advanced models with large parameters. Additionally, satellite on-orbit computing requires high speed and accuracy, posing significant challenges for developing suitable models. To overcome these challenges, we propose an efficient vision transformer, RepSViT, for satellite on-orbit computing. The RepSViT introduces Spiking neural networks (SNNs) with high biological plausibility, event-driven property and low power consumption into the field of remote sensing image processing and satellite on-orbit computing for the first time and incorporates structural reparameterization. Specifically, we design a dynamic dilated spiking convolution (D2SC) based on SNNs to improve the feature extraction capability and efficiency of RepSViT. We also develop a spiking guided attention module (SGAM) to make RepSViT pay more attention to object-related features with lower computational costs. Furthermore, we design an efficient coupled fine–coarse-grained block (ECFC) to enhance the model’s capability in extracting coarse and fine-grained features. To ensure effective feature extraction, inference speed and reduced computational costs, we design a reparameterized feed-forward network (RepFFN). RepSViT achieves an inference latency of 8.33 ms and a recognition accuracy of 95% on an embedded GPU, utilizing 3.77 million parameters and consuming 0.6 GFLOPs computational costs.

The low Earth orbit (LEO) satellite edge computing paradigm provides remote sites with flexible, reliable, and scalable edge computing capabilities. Characterized by the orbital motion patterns and harsh space environments, the LEO satellite edge computing faces unique security challenges in terms of the secure collaboration of multiple satellites and the intellectual property protection of models. Under the unique space environment and security demands, we propose a secure satellite edge computing framework in this paper. By taking a remote electricity line outage identification use case as an example, our framework first achieves the secure delegation of the line outage identification task among multiple satellites, which is realized through a secure query$(\mathsf {SQuery})$scheme to check the availability of the target time slot. Meanwhile, we also design a SHE-enabled secure inner-product encryption ($\mathsf {SSIPE}$) protocol, to achieve the secure multinomial logistic regression (MLR) based line outage identification on-orbit. To reduce the complexity brought by the computationally intensive homomorphic multiplication between two ciphertexts, we further grasp the idea and design a “divide-and-conquer” based secure query ($\mathsf {DSQuery}$) scheme, which converts this homomorphic multiplication operation between ciphertexts into the homomorphic addition operation. As far as we know, this is the first scheme investigating the secure task delegation among different satellites on-orbit. Besides, detailed security analyses are performed to demonstrate the security properties of confidentiality and authentication. In performance evaluations, we test and compare the computational and communication overhead of our scheme and other straightforward schemes. Simulation results show that the$\mathsf {DSQuery}$scheme greatly reduces the computational cost, which saves the stringent on-orbit computation resources of LEO satellites.

Geographic information system (GIS) enables operations for capturing, manipulating, analyzing, and displaying the spatial characteristics of objects on Earth's surface. As the objects in GISs are mostly location-dependent, various location privacy-preserving schemes are proposed to support the secure spatial query and analysis. However, existing location privacy-preserving mechanisms mainly focus on the$k$-nearest neighbor ($k$NN) queries and range queries and fail to consider the practical geographic implementation with quad-trees. We propose an efficient and privacy-preserving point-of-interest (POI) query scheme along the movement trajectory under the quad-tree setup in a two-server mode. Specifically, we first convert the secure identification of the target lowest-level tile into a series of private information retrieval (PIR) processes and securely derive the target POIs along the movement trajectory within the identified tile by constructing a linear polynomial passing through the origin and destination for secure distance comparison. Our scheme also supports the efficient loading of POIs contained in the adjacent tiles with privacy preservation. Security analysis demonstrates that ours can achieve the security goals of privacy preservation and confidentiality. We execute performance evaluations to show and validate the system efficiency, i.e., computational costs and communication overheads.

Summary As the new generation of power scheme, smart grid is proposed to overcome the shortcomings of traditional systems, such as low efficiency and reliability. In this article, a novel lattice‐based secure primitive for privacy‐preserving smart grid communications is proposed, which has the remarkable characteristics, such as scalable multi‐dimensional fine‐grained power data structure and differential privacy security. First, combining with the lattice‐based data encryption technology, while effectively resisting quantum attacks, the method of simultaneous processing of multi‐dimensional data is innovated. Second, through combining the additive homomorphism of the lattice‐based cryptosystem and the Chinese remainder theorem, the data aggregation mechanism that can directly perform homomorphic operations on compressed ciphertext is constructed. Thanks to the above innovative design ideas, the proposed scheme not only significantly improves the efficiency of data communication and processing, greatly reduces the computational cost of the intermediate entity, but also realizes the data confidentiality and information privacy. Finally, observing the decentralized topology of communication nodes in the typical cyber‐physical system of smart grid, the localized differential privacy technology is leveraged to optimize and balance the utility, security, and efficiency of differential privacy. Extensive performance evaluations are conducted to illustrate that the proposed scheme outperforms the state‐of‐the‐art similar schemes in terms of computation complexity and communication cost.

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling, drone-aided visual inspections, etc. However, dynamic perception and cooperative decision making among these heterogeneous and resource-constrained mobile clients in IIoT remains a big challenge. In this article, we propose an incentive-based federated learning scheme for digital twin (DT)-driven industrial mobile crowdsensing. Specifically, we first design a DT-driven industrial mobile crowdsensing architecture to achieve dynamic perception of the complex IIoT environment, among heterogeneous and resource-constrained mobile clients. Second, we develop a novel incentive-based federated learning framework incorporated with a contract-based reputation mechanism and a Stackelberg-based interclient incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of nonindependent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 data sets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.

Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on pure statistical signal processing or data-driven approaches, often struggle to generalize to new environments, which results in considerable time and effort being wasted. To address this challenge, we propose MetaLoc, which is the first fingerprinting-based localization framework that leverages the Model-Agnostic Meta-Learning (MAML). Specifically, built on a deep neural network with strong representation capabilities, MetaLoc is trained on historical data sourced from well-calibrated environments, employing a two-loop optimization mechanism to obtain the meta-parameters. These meta-parameters act as the initialization for quick adaptation in new environments, reducing the need for much human effort. The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately. Furthermore, the advanced distributed paradigm modifies the vanilla MAML loss function to ensure that the reduction of loss occurs in a consistent direction across various training domains, thus facilitating faster convergence during training. Our experiments on both synthetic and real datasets demonstrate that MetaLoc outperforms baseline methods in terms of localization accuracy, robustness, and cost-effectiveness. The code and datasets used in this study are publicly available at:https://github.com/WU-Dongze/MetaLoc.

Nowadays, the proliferation of small satellites brings the skyrocketing rise in space data, especially the shift to on-orbit computing needs. On one hand, with the increasing volume of data generation, like high-resolution remote sensing images, on-orbit computing produces near real-time onboard solutions and quick responses. However, constrained by the limited size and energy supply of satellites, achieving energy-efficient on-orbit computing remains a crucial challenge. In this article, an on-orbit remote sensing image processing complex task scheduling model facing heterogeneous multiprocessor system (HMPS) is proposed. First, aiming at accelerating image processing, we establish a novel parallel task execution model using directed acyclic graph (DAG) to universally describe typical missions, i.e., cloud detection, geometric correction, and image classification. Subsequently, a mathematical task scheduling formulation is defined to calculate the makespan, and total energy consumption (TEC) required when executing DAG on HMPS. Second, a new Pareto-based iterated greedy optimizer (PIGO) is devised to complete the energy- and time-efficient task execution and resource allocation on HMPS through confined inserting mutation, destruction-reconstruction, and local search. Finally, we build an emulated on-orbit HMPS to conduct experiments. The results show that, in comparison with the scheme without model scheduling, the most savings of around 51% and 54% in makespan and TEC, respectively, are achieved by the proposed model. Moreover, the HMPS configured with our methodology can obtain 2.2× improvement in energy efficiency and process up to 2.56×105pixels per unit of power (W) and time (s).

In recent years, the performance of the deep learning based object detection models for remote sensing images improves with the increase of the hyperparameter scale. However, the state of the art of detection models are generally too cumbersome to adapt to the resource-constrained satellite platforms, and even general lightweight detection models cannot satisfy performance requirements. To solve those problems, a lightweight and accurate oriented object detection network for satellite on-orbit computing (SOCDet) is proposed from three levels. At the computing unit level, we propose an efficient computing unit Single-kernel Omni-dimensional Dynamic Convolution to make SOCDet feature extraction more efficient. At the network module level, we design a structural reparameterization block based on composite structure reparameterization to improve inference accuracy. A guided attention module for guiding SOCDet is designed to extract object features. We design a lightweight and concise one-stage detection architecture at the network architecture level to accommodate satellite platforms with extremely constrained computing and storage resources. We conduct ablation experiments on the ground server and compare experiments with the state-of-the-art methods on embedded GPU, using DOTA, HRSC2016 and FAIR1M. The experiment results show that SOCDet can achieve 2.36 times faster than the baseline in inference speed with 10.78 more mAP, 72.6% fewer Params and 92.56% fewer floating point operations. Compared with state-of-the-art methods, SOCDet improves the inference speed and derives competitive mAP results with affordable computing capability.

The existing indoor fingerprinting methods based on received signal strength (RSS) are rather accurate after intensive offline calibration for a specific scenario, but the well-calibrated localization model (can be a pure statistical one or a data-driven one) will present poor generalization ability in a new scenario, which results in big loss in knowledge and human effort. To break the scenario-specific localization bottleneck, we propose a new-fashioned data-driven fingerprinting method for localization based on meta-learning, named by MetaLoc, that can adapt itself rapidly to a new, possibly unseen, scenario with very little calibration work. Specifically, the underlying localization model is taken to be a deep neural network (NN), and we train an optimal set of group-specific meta-parameters by leveraging historical data collected from diverse well-calibrated indoor scenarios and the maximum mean discrepancy criterion. Simulation results confirm that the meta-parameters obtained for MetaLoc achieves very rapid adaptation to new scenarios, competitive localization accuracy, and high resistance to significantly reduced reference points (RPs), saving a lot of calibration effort.

Nowadays, the vehicle to grid (V2G) technology enables bi-directional energy interactions between the power grid and the battery of an electric car. However, there still exist some issues in terms of security and privacy preservation. In this paper, we propose an efficient and privacy-preserving scheme to achieve the two-way electricity trading between vehicles and the power grid, by exploiting a dynamic threshold public-key encryption algorithm. Our proposed scheme mainly consists of two phases: The first phase includes the secure aggregation of the vehicles’ electricity requests and the recovery of the aggregation result; if the power grid can satisfy the aggregated electricity request, the vehicles execute in the second phase electricity trading. Meanwhile, the proposed scheme can adapt to a varying threshold of participating users, which enables the dynamic fluctuation of users. Extensive security analysis demonstrate the security properties of the proposed scheme in terms of privacy preservation and authentication. Performance evaluations are conducted to show the efficiency of our proposed scheme, and simulation results show that our scheme greatly reduces the introduced communication and computation overheads.

As the future energy infrastructure, smart grid aims to overcome the disadvantages of traditional power grid, e.g., low efficiency and unstable service. However, the frequent collection and analysis of the user’s electricity data may bring various security and privacy threats. Besides, the traditional centralized data storage model in the smart grid is prone to the single point of failure. To address these challenges, in this article, for fair and secure smart grid communication, a blockchain-based novel paradigm, named BBNP, is proposed. Specifically, based on the pseudorandom function and auxiliary information generation and sharing technology, a lightweight data aggregation protocol is designed first to protect the user’s data privacy and ensure communication confidentiality. Then, a novel efficient authentication mechanism is proposed to generate and share session keys in a noninteractive way, which is leveraged for MAC authentication to achieve data integrity of the transmitted data. After that, based on the subjective logic reputation model, a blockchain node consensus mechanism is studied to efficiently store smart grid big data and effectively solve the single point failure problem. By constructing the long-term reputation model for consensus nodes (CNs) and integrating batch verification technology, the problems of CN fair selection and scalability of large-scale nodes are solved simultaneously. Finally, the performance evaluation indicates that BBNP outperforms the state-of-the-art similar schemes in computing complexity, communication cost, system availability, and fairness of block generation.

Smart grids have been increasingly spotted as high-profile targets of cyber assaults over the years. To better understand the cyber threat landscape, honeypots have been widely used in the smart grid security community, i.e., identifying unauthorized penetration attempts and observing the behaviors in such activities. In this paper, we propose a honeypot-enabled optimal defense strategy selection approach for smart grids, based on a novel stochastic game. Specifically, the interactions between the attacker and smart grid defender are captured using our designed stochastic game, a non-cooperative two-player game with incomplete information. We take into account various possible defenses from a smart grid defender and offensive strate-gies from the attacker. Then the Nash equilibrium is calculated by the stochastic game model, which is derived exhibiting an optimal defense strategy for the smart grid defender. Extensive simulation experiments demonstrate the effectiveness of the proposed scheme.

Federated learning-based navigation has received much attention in vehicular IoT. The intention is to employ a big number of end-users for data collection along different trajectories and perform local training of a global learning model to substitute the global positioning system (GPS) in urban areas. The prerequisites for its commercialization, however, lie in the location-dependent input data trustworthiness and participants’ privacy preservation. In this paper, we propose a privacy-preserving proof-of-location mechanism using blockchain to meet these conditions. Specifically, the proposed scheme utilizes a Threshold Identity-Based Encryption (TIBE) system for the generation of secret shares, such that each anonymous location proof can only be verified with at least a threshold number of participants. In addition, the proposed scheme exploits a cuckoo filter for the secure and efficient maintenance and dissemination of location proofs. Systematic security analysis is conducted to demonstrate the fulfillment of harsh security requirements. Performance evaluations are carried out to validate the computation efficiency in comparison with an oblivious transfer (OT) protocol, which has been widely adopted for secure data acquisition.

High volumes of real-time energy consumption data are generated by smart meters each day, which may create tremendous values if shared to third parties, e.g., government agencies, real estate agents, and travel agencies, etc. However, significant security and privacy challenges are always in place in case these sensitive digital assets are directly shared outside the grid utilities without any protection. It is, therefore, vital to guarantee the data security and privacy while maintaining its values. To meet this gap, in this paper we propose a secure and efficient data sharing scheme with anti-collusion for smart grids. In this scheme, we devise a privacy-preserving data acquisition algorithm for smart meters based on a modified Paillier cryptosystem, and also an anti-collusion proxy re-encryption algorithm for the control center & cloud server to achieve secure energy consumption data sharing of its mean and variance. Importantly, the proposed scheme is also designed to support customer identity preservation while requesting data sharing services. Security analysis strictly demonstrate the security of the proposed scheme, and extensive experiments validate the high efficiency of the proposed scheme.

Supervisory control and data acquisition (SCADA) networks provide high situational awareness and automation control for industrial control systems, whilst introducing a wide range of access points for cyber attackers. To address these issues, a line of machine learning or deep learning based intrusion detection systems (IDSs) have been presented in the literature, where a large number of attack examples are usually demanded. However, in real-world SCADA networks, attack examples are not always sufficient, having only a few shots in many cases. In this paper, we propose a novel few-shot learning based IDS, named FS-IDS, to detect cyber attacks against SCADA networks, especially when having only a few attack examples in the defenders’ hands. Specifically, a new method by orchestrating one-hot encoding and principal component analysis is developed, to preprocess SCADA datasets containing sufficient examples for frequent cyber attacks. Then, a few-shot learning based preliminary IDS model is designed and trained using the preprocessed data. Last, a complete FS-IDS model for SCADA networks is established by further training the preliminary IDS model with a few examples for cyber attacks of interest. The high effectiveness of the proposed FS-IDS, in detecting cyber attacks against SCADA networks with only a few examples, is demonstrated by extensive experiments on a real SCADA dataset.

Federated learning-based automotive navigation has recently received considerable attention, as it can potentially address the issue of weak global positioning system (GPS) signals under severe blockages, such as in downtowns and tunnels. Specifically, the data-driven navigation framework combines the position estimation offered by the high-sampling inertial measurement units and the position calibration provided by the low-sampling GPS signals. Despite its promise, the privacy preservation and flexibility of the participating users in the federated learning process are still problematic. To address these challenges, in this article, we propose an efficient, flexible, and privacy-preserving model aggregation scheme under a federated learning-based navigation framework named FedLoc. Specifically, our proposed scheme efficiently protects the locally trained model updates, flexibly supports the fluctuation of participants, and is robust against unregistered malicious users by exploiting a homomorphic threshold cryptosystem, together with the bounded Laplace mechanism and the skip list. We perform a detailed security analysis to demonstrate the security properties in terms of privacy preservation and dishonest user detection. In addition, we evaluate and compare the computational efficiency with two traditional schemes, and the simulation results show that our scheme greatly improves the computational efficiency during participant fluctuation. To validate the effectiveness of our scheme, we also show that only part of the model update is excluded from aggregation in the case of a dishonest user.

With the advances of Internet of Things (IoT) solutions in intelligent transportation systems, collected vehicle data can produce insights on emerging vehicular phenomenon, and further contribute to the further improvement of innovative and efficient vehicular systems. Particularly, by leveraging data collected from vehicle sensors and maintenance models constructed from operation and repair history, predictive maintenance aims to detect the anomalies of vehicles and provide early warnings before the occurrence of failure. However, privacy preservation still remains as one of the top concerns for vehicle owners in predictive maintenance, as the sensory data could potentially violate their location and identity privacy. To address this challenge, in this article, we propose a privacy-preserving and verifiable continuous data collection scheme with the intent of predictive maintenance in vehicular fog, which gathers and organizes the sensor data of each individual vehicle on a sliding window basis. Specifically, our proposed scheme exploits the homomorphic Paillier cryptosystem and truncated α-geometric technique to protect the content of each individual piece of sensory data. Meanwhile, our proposed scheme also aggregates and authenticates the collected sensory data reports on a time-series sliding window basis, which achieves the continuous observation of the recently collected vehicular sensory data. Detailed security analysis is carried out to demonstrate the security properties of our proposed scheme, including confidentiality, authentication and privacy preservation. In performance evaluations, we also compare our proposed scheme with a traditional scheme, and our scheme shows great improvement in terms of communication and computation overheads. Furthermore, to show the feasibility of our proposed scheme, we also compare and discuss the expected squared error introduced by the differential privacy mechanism.

Vehicular sensing is advocated to perform data collection by exploiting a plethora of vehicular on-board sensors; meanwhile, with the merging of vehicular sensing and fog computing, the deployed road side units (RSUs) can act as fog nodes to collect and share vehicular sensory data at the network edge. However, there are still several problems in terms of the secure and reliable sharing of sensory data in vehicular fog. To resolve these issues, in this paper, we present an efficient, privacy-preserving and verifiable sensory data collection and sharing scheme with a permissioned blockchain in vehicular fog. During the data collection phase, by combining the homomorphic 2-DNF (Disjunctive Normal Form) cryptosystem and an identity-based signcryption scheme, our proposed scheme achieves the secure and verifiable computation of the average and variance of the collected vehicular sensory data. Meanwhile, to achieve efficient and reliable data sharing, we exploit a permissioned blockchain to maintain an immutable and tamper-proof record of the derived sensory data. Security analysis demonstrates the security properties of the proposed scheme, in terms of location privacy preservation, verifiability and immutability. Performance evaluations are conducted to validate the efficiency of the proposed scheme, i.e., improvements in computation and communication efficiency in comparison with a scheme without exploiting blockchain.

In the duration of handover, coordinated multipoint (CoMP) joint transmission can help not only improve the throughput but also reduce the interference at cell edges. However, the current secure handover key management scheme in LTE-A system has not yet supported the CoMP joint transmission handover. Therefore, to solve the problem, in this paper, we present a novel secure CoMP joint transmission handover key management scheme in LTE-A vehicular networks. Specifically, to achieve the diversity gain brought by the CoMP joint transmission and accommodate to protect the backward/forward key separation, the session key is generated by the vehicle and securely delivered towards the cooperating eNBs, and then decrypted by each cooperating eNB respectively. Security analysis shows that the proposed scheme can successfully establish session keys with the cooperating eNBs during the CoMP joint transmission handover with backward/forward key separation. In addition, performance evaluation is conducted to demonstrate the feasibility of the proposed scheme.

Internet of Things is expanding the network by integrating huge amount of surrounding objects which requires the secure and reliable transmission of the high volume data generation, and the mobile relay technique is one of the efficient ways to meet the on-board data explosion in LTE-Advanced (LTE-A) networks. However, the practice of the mobile relay will pose potential threats to the information security during the handover process. Therefore, to address this challenge, in this paper, we propose a secure handover session key management scheme via mobile relay in LTE-A networks. Specifically, in the proposed scheme, to achieve forward and backward key separations, the session key shared between the on-board user equipment (UE) and the connected donor evolved node B (DeNB) is first generated by the on-board UE and then securely distributed to the DeNB. Furthermore, to reduce the communication overhead and the computational complexity, a novel proxy re-encryption technique is employed, where the session keys initially encrypted with the public key of the mobility management entity (MME) will be re-encrypted by a mobile relay node (MRN), so that other DeNBs can later decrypt the session keys with their own private keys while without the direct involvement of the MME. Detailed security analysis shows that the proposed scheme can successfully establish session keys between the on-board UEs and their connected DeNB, achieving backward and forward key separations, and resisting against the collusion between the MRN and the DeNB as the same time. In addition, performance evaluations via extensive simulations are carried out to demonstrate the efficiency and effectiveness of the proposed scheme.

Vehicular cloud, which is constituted by gathering the under-utilized on-board capabilities on the road, has received considerable attention in recent years. In this paper, we propose a novel secure and privacy-preserving incentive mechanism in vehicular cloud, which employs the Stackelberg Game to model the interaction between the leader and follower vehicles. With the proposed incentive mechanism, the leader vehicle which represents the task announcement server can select competent follower vehicles to collaborate for the announced task, and the selected follower vehicles can earn payments from participating in and completing the announced tasks. By exploiting the group signature technique, the leader and follower vehicles can achieve mutual verification with each other without privacy-related information disclosure. To show the efficiency of the proposed scheme, numerical analysis are conducted, and the derived results demonstrate that the proposed incentive mechanism can bring benefits to both parties, in terms of the utilities of the involved vehicles.

Heterogeneous Networks (HetNets) are an attractive way of increasing network throughput, expanding network coverage, and reducing energy consumption, but it may lead to the problems of high cost infrastructure investment and high computational complexity to mobile operators. In this paper, we propose a new paradigm for the deployment of HetNets in LTE-Advanced system, where the Donor evolved nodeB (DeNB) cooperates with multiple relay nodes (RNs) in a crowdsourcing way. In order to stimulate these RNs, we propose an incentive mechanism by exploiting the game theory. In our incentive mechanism, the DeNB announces rewards to the participating RNs, and the RNs adjusts their transmission strategies, i.e., transmission power. This incentive mechanism can bring about a win-win situation, in which the DeNB can increase its income and the RNs can receive satisfying reward from the cooperative transmission. Extensive numerical simulations are conducted to validate the effectiveness and efficiency of our proposed incentive mechanism.