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With the widespread prevalence of smart devices, mobile crowdsensing (MCS) becomes a new trend to encourage mobile nodes to participate in cooperative data collection in various Internet of Things (IoT) applications. In location-dependent MCS, location information of mobile nodes is collected and analyzed by service provider to assist in task allocation. If the service provider is not fully trusted, mobile nodes privacy is leaked and accessed by unauthorized parties. How to preserve privacy while maintaining task allocation accuracy and efficiency becomes challenging. To this end, we propose a learning-based mechanism that involves two parts: 1) privacy-preserving task release and task allocation; 2) accurate and efficient task allocation. In the first part, we design a location-based symmetric key generator, which enables two parties to self-generate a symmetric key without depending on fully trusted authorities. By utilizing this key generator and Proxy Re-encryption, we propose a privacy preserving protocol to protect location information in task release and task allocation. In the second part, we design a reinforcement learning based task allocation algorithm to optimize the winners selection, which obtains high accuracy and efficiency. Performance analysis reveals that our proposed mechanism achieves accurate and efficient task allocation while preserving privacy in location-dependent MCS.

The 5G ecosystem is comprised of the cellular 5G System, as well as a managed and orchestrated infrastructure providing virtualized network and service functions. The automotive industry with its stringent requirements for connected vehicles is a promising and yet challenging consumer of such 5G ecosystem. Deployment of service instances at distributed cloud resources of cellular network infrastructure edges enables localized low-latency access to these services from moving vehicles but comes along with challenges, such as the need for fast recon-figuration of the distributed deployment according to mobility pattern and associated service and resource demand. In this paper, we investigate a solution for the collaborative orchestration of services for Connected, Cooperative and Automated Mobility (CCAM) within such 5G ecosystem. A key objective is the service continuity for a highly dynamic automotive scenario, achieved by the associated management and orchestration of these services in distributed edge clouds. The proposed solution leverages a multi-tier orchestration system as well as localized management and protocol operations for collaborative edge resources. By means of both analytical and experimental evaluations, the paper draws conclusions on the gain in accelerating orchestration decisions and enforcements, while balancing associated protocol and computational load over the highly distributed and multi-layered orchestration system.

In this article, the exponential synchronization control issue of reaction-diffusion neural networks (RDNNs) is mainly resolved by the sampling-based event-triggered scheme under Dirichlet boundary conditions. Based on the sampled state information, the event-triggered control protocol is updated only when the triggering condition is met, which effectively reduces the communication burden and saves energy. In addition, the proposed control algorithm is combined with sampled-data control, which can effectively avoid the Zeno phenomenon. By thinking of the proper Lyapunov-Krasovskii functional and using some momentous inequalities, a sufficient condition is obtained for RDNNs to achieve exponential synchronization. Finally, some simulation results are shown to demonstrate the validity of the algorithm.

The future communication technologies like 6G are capable to provide higher mobility and better quality of service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next generation networks. In this paper, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost, load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next generation large scale IoT networks.

The Internet of things is gaining a lot of interestfrom day to day forvarious applications like Smart cities, Industrial health monitoring, smart homes, etc. IoT systems consist of sensor devices with communication capabilities, actuators, edgegateways, Back end Servers, and IoT applications. The Ultimate goal of an IoT network is to collect sensor data, reliable Transfer sensor data to the backend serverthrough the edgegateways. Back end server delivers the sensor data to the IoT Applications. IoT application process the Sensor data and apply the appropriate decision. Reliable Transfer of sensor data tothe Backend server isessential considering wireless sensor network characteristics, low computation power, and sensor devices' bandwidth. The IoT network deployment signaling protocol plays a crucial role in communicating sensor data to the IoT program. Various application messaging protocols are currently available, like MQ TELEMETRY TRANSPORT, COAP, HTTPetc. These protocols aredesigned, developed considering richcomputation powerand bandwidth. These protocolsare notenergy efficientfor sensor devices. MQ TELEMETRY TRANSPORT-SN protocol specified for sensor devices considering wireless sensor networks. In this article, Message Telemetry Transfer Protocol-sensor networks were planned, built, and validated according to the MQ TELEMETRY TRANSPORT-SN specification.

The volume of data is increasing rapidly currently, which poses a great challenge for resource-constrained clients to process and analyze. A promising approach for solving computation-intensive tasks is to outsource them to the cloud to take advantage of the clouds powerful computing ability. However, it also brings privacy and security issues since the data uploaded to the cloud may contain sensitive and private information which should be protected. In this paper, we address this problem and focus on the privacy-preserving and secure outsourcing of large-scale nonlinear programming problems (NLPs) subject to both linear constraints and nonlinear constraints. Large-scale NLPs play an important role in the eld of data analytics but have not received enough attention. In our outsourcing protocol, we rst apply a secure and efcient transformation scheme at the client side to encrypt its private information. Then, we use the reduced gradient method and generalized gradient method at the server side to solve the transformed large-scale NLPs under linear constraints and nonlinear constraints, respectively. We provide security analysis of the proposed protocol, and evaluate its performance. The experimental results show that we can efciently solve the large-scale NLPs and save huge amount of time for the clients.

We propose a decentralized e-voting protocol that is coercion-resistant and vote-selling resistant, while being also completely transparent and not receipt-free. We achieve decentralization using blockchain technology. Because of the properties such as transparency, decentralization, and non-repudiation, blockchain is a fundamental technology of great interest in its own right, and it also has large potential when integrated into many other areas. We prove the security of the protocol under the standard DDH assumption on the underlying prime-order cyclic group (e.g. the group of points of an elliptic curve), as well as under standard assumptions on blockchain robustness.

Online transaction processing (OLTP) is widely used on modern cloud infrastructures to complete important businesses such as payments and stock exchanges. Remote Direct Memory Access (RDMA) is a technology that enables ultra-low inter-server memory access latency - which is critical for implementing high-performance concurrency control protocols in distributed OLTP. In this paper, we develop RCC, the first unified and comprehensive RDMA-enabled distributed transaction processing framework containing six serializable concurrency control protocolsnot only the classical protocols NOWAIT, WAITDIE, OCC, but also more advanced MVCC, SUNDIAL, and CALVIN the deterministic protocol. Our goal is to unbiasedly compare protocols on OLTP workloads in a common execution environment with the concurrency control protocol being the only changeable component. From RCC, we obtained new insights on building RDMA-based protocols. We analyzed stage-wise latency breakdown to develop more efficient hybrid implementations. Moreover, RCC can enumerate all stage-wise hybrid designs under a given workload characteristic. Our results show that throughput-wise hybrid designs are better than RPC or one-sided counterparts by 32.2% and up to 67%; three hybrid designs are better than their pure counterparts by up to 17.8%. RCC can provide performance insights and be used as the common open-source infrastructure for fast prototyping new implementations.

Online Ride Hailing (ORH) services gain remarkable development in the past decade, which enable riders and drivers to establish optimized rides via mobile device. To guarantee user safety, ORH service providers often monitor the ride trajectory and report the abnormal behavior once a trajectory deviation occurs. Along with the advantage of safety monitoring raises some vital privacy concerns on user location information leakage. In this paper, we propose a privacy-preserving safety monitoring scheme for ORH services, called pSafety. It enables an ORH service provider to detect user's trajectory deviation without learning anything about users locations. In pSafety, we propose two secure trajectory similarity computation algorithms by using somewhat homomorphic encryption, which are used to plan an agreed path and measure trajectory deviation, respectively. Furthermore, we also design a ciphertext compression algorithm and a secure comparison protocol to improve efficiency. Theoretical analysis and experimental evaluations show that pSafety is secure, accurate and efficient.

In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g., 98% of encrypted local models are recovered within 21 h). Then, we propose VerSA, a verifiable secure aggregation protocol for cross-device federated learning. VerSA does not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency of VerSA under diverse datasets, demonstrating that \textsc{VerSA} is orders of magnitude faster than verification in prior work.

The population of cloud computing greatly facilitates the sharing of explosively generated image today. While benefiting from the convenient of cloud, the privacy protection mechanism that commonly applied in cloud service makes the spreading of illegal and harmful data very hard to be detected or controlled. Such a realistic threat should be seriously treated, yet is largely overlooked in the literature. To address this issue, we propose the first cloud service framework that can simultaneously provide privacy protection and content regulation for the cloud storage image. In specific, we design a secure multi-party computation (MPC) protocol to protect the data privacy via random projection. By leveraging the distance preserving properties residing in random projection, we propose a privacy-preserving principal component analysis (PCA)-based recognition approach over the random projection domain to achieve content matching while respecting the data privacy. To facilitate the efficiency, we implement our system under the compressive sensing (CS) framework. Due to the compression effect of CS, the proposed cloud service can achieve remarkable reduction on the computation and communication complexity of the content matching process. Theoretical analysis and experimental results both show that our system can achieve privacy assurance and acceptable recognition performance, while with high efficiency.

It is widely acknowledged that the success of deep learning is built on large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community.

As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.

In this article, the finite-time consensus problem is investigated for a group of heterogeneous nonlinear multiagent systems with input delays. Different from the existing results, input delays are assumed to be nonidentical and time-varying. To overcome the difficulty of nonidentical input delays, a novel compensation mechanism is introduced. Moreover, in order to save communication resources, a distributed finite-time self-triggered observer is constructed. By employing the observer and the dynamic surface control strategy, the finite-time adaptive neural control protocol is proposed. It is proved that not only the followers’ outputs can be synchronized with the leader in finite time, but also continuous communication among followers is avoided. Finally, two simulation examples are provided to illustrate the validity of the proposed control strategy.

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

This paper proposes SilentDelivery, a secure, scalable and cost-efficient protocol for implementing timed information delivery service in a decentralized blockchain network. SilentDelivery employs a novel combination of threshold secret sharing and decentralized smart contracts. The protocol maintains shares of the decryption key of the private information of an information sender using a group of mailmen recruited in a blockchain network before the specified future time-frame and restores the information to the information recipient at the required time-frame. To tackle the key challenges that limit the security and scalability of the protocol, SilentDelivery incorporates two novel countermeasure strategies. The first strategy, namely silent recruitment, enables a mailman to get recruited by a sender silently without the knowledge of any third party. The second strategy, namely dual-mode execution, makes the protocol run in a lightweight mode by default, where the cost of running smart contracts is significantly reduced. We rigorously analyze the security of SilentDelivery and implement the protocol over the Ethereum official test network. The results demonstrate that SilentDelivery is more secure and scalable compared to the state of the art and reduces the cost of running smart contracts by 85%., This paper has been accepted by IEEE Transactions on Services Computing

Blockchain allows for secure management of a shared ledger by agreement protocols, where transactions are validated over network without central authorities. Although the agreement protocol has been thoroughly conducted of propagation and consensus researches, mobility of nodes in unstructured overlay networks has not received much attention. Besides, current dynamic propagation schemes waste travel hops and are low of delivery ratio. In this paper, we propose a social characteristic-based propagation-efficient protocol NefSBFT to agree on system state plus consensus mechanisms in public blockchains. We devise a propagation technique (travel hops of at least $\frac{1}{3}$ savings, delivery ratio above $0.93$ , etc.) for message multicasting when exploiting real nodes' social characteristics of intermittent connectivity and frequent partitions. This propagation technique is executed in the improved FastBFT to achieve transaction ordering and block verification, thus, no controllable mobility is required during the whole system's execution. NefSBFT achieves fast propagation, small message complexity and few resource consumption of travel hops and running nodes for complete protocol execution. We analyze NefSBFT's security against DDOS attack of non-primary failure. The experiments show the performance tradeoff under different parameters, compare the propagation efficiency with Erlay and Flooding, and clarify NefSBFT's impact on the whole system performance through comparison.

This paper develops a distributed solution to the fully-heterogeneous containment control problem (CCP), for which not only the followers' dynamics but also the leaders' dynamics are non-identical. A novel formulation of the fully-heterogeneous CCP is first presented in which each follower constructs its virtual exo-system. To build these virtual exo-systems by followers, a novel distributed algorithm is developed to calculate the so-called normalized level of influences (NLIs) of all leaders on each follower, and a novel adaptive distributed observer is designed to estimate the dynamics and states of all leaders that have an influence on each follower. Then, a distributed control protocol is proposed based on the cooperative output regulation framework, utilizing this virtual exo-system. Based on estimations of leaders' dynamics and states and NLIs of leaders on each follower, the solutions of the so-called linear regulator equations are calculated in a distributed manner, and consequently, a distributed control protocol is designed for solving the output containment problem. Finally, theoretical results are verified by performing numerical simulations.

This article studies the multiagent bipartite consensus in networks with deterministic disturbances and antagonistic interactions. An observer-based output-feedback controller design is provided to guarantee the bipartite consensus with deterministic disturbances that satisfy the matching condition. Then, by considering that the bandwidths of communication channels are limited in practical systems, the event-triggered scenario of the proposed output controller for the bipartite consensus is further studied; the node-based broadcast updating fashion is utilized and the Zeno behavior is ruled out. Simulations are also offered to support the theoretical results of protocol designs.

Cyber-physical-social systems (CPSS) as an emerging computing paradigm are revolutionizing the relationship between humans, devices and physical environments and will be expected to have a fundamental impact and influence in our daily lives. In a typical CPSS scenario, plenty of nodes (or devices) often adopt wireless distributed contention protocols to transmit sensed environment states for decision making. Therefore, designing a high-efficient and collision-less contention protocol is of great importance for wireless networking. As is known, in the classic time-to-frequency (T2F) protocol, each node signals on one randomly chosen subcarrier for contention. Though reducing the conventional time-domain contention time, T2F leads to many contention collisions (i.e., multiple nodes choose the same subcarrier) when the number of nodes is large. Aiming at this challenge, in this paper, we propose M-T2F (where each node signals on multiple randomly chosen subcarriers for contention) and analyze its consensus achievement. Specifically, M-T2F supports more nodes for transmission but introduces less collision, benefiting from lots of multicarrier choice combinations. Since the subset relationship between subcarrier choices poses a challenge to reach a consensus among nodes, M-T2F enables all nodes to reach a consensus by excluding the subset relationship and reduces contention time by utilizing wireless broadcast characteristic

Cloud-assisted Internet of Things (IoT) is increasingly prevalent used in various fields, such as the healthcare system. While in such a scenario, sensitive data (e.g., personal electronic medical records) can be easily revealed, which incurs potential security challenges. Thus, Symmetric Searchable Encryption (SSE) has been extensively studied due to its capability of supporting efficient search on encrypted data. However, most SSE schemes require the data owner to share the complete key with query users and take malicious cloud servers out of consideration. Seeking to address these limitations, in this paper we propose a Verifiable Privacy-preserving data Search scheme with Limited key-disclosure (VPSL) for cloud-assisted Internet of Things. VPSL first designs a trapdoor generation protocol for obtaining a trapdoor with disclosing limited key information and without revealing plaintext query points to others. Then, VPSL provides an efficient result verification and search processing by employing the Merkle hash tree structure and k-means clustering technique, respectively. VPSL is secure against the level-2 attack. Finally, an enhanced VPSL (called VPSL+) resisting the level-3 attack is constructed by introducing the random splitting technique. Empirical experiments demonstrate the accuracy and efficiency of VPSL or VPSL+ using real-world datasets.

This article studies the vision-based tracking control problem for a nonholonomic multirobot formation system with uncertain dynamic models and visibility constraints. A fixed onboard vision sensor that provides the relative distance and bearing angle is subject to limited range and angle of view due to limited sensing capability. The constraint resulting from collision avoidance is also taken into account for safe operations of the formation system. Furthermore, the preselected specifications on transient and steady-state performance are provided by considering the time-varying and asymmetric constraint requirements on formation tracking errors for each robot. To address the constraint problems, we incorporate a novel barrier Lyapunov function into controller design and analysis. Based on the recursive adaptive backstepping procedure and neural-network approximation, we develop a vision-based formation tracking control protocol such that formation tracking errors can converge into a small neighborhood of the origin in finite time while meeting the requirements of visibility and performance constraints. The proposed protocol is decentralized in the sense that the control action on each robot only depends on the local relative information, without the need for explicit network communication. Moreover, the control protocol could extend to an unconstrained multirobot system. Both simulation and experimental results show the effectiveness of the control protocol.

The Industrial Internet of Things (IIoT) is creating a massive impact in a wide range of applications. In addition, with the forthcoming 5G and 6G technologies, vehicular ad-hoc networks will have pioneer advancements. However, security concerns are not well addressed, as vehicular networks should be deployed at a large scale. To address the security concerns, especially to ensure secure emergency message transmission, a blockchain-based protocol is proposed in this paper, where one of the blockchains is to store the authentication information of the vehicle, and another one to store and distribute blockchain services. Experimental analysis revealed that the proposed blockchain-based protocols are superior than the existing ones in terms of several metrics.

The use of Internet key exchange protocols in IP Security architecture and in IoT environments has vulnerabilities against various malicious attacks and affects communication efficiency. To address these weaknesses, we propose a novel efficient and secure Internet key exchange protocol (ESIKE), which achieves a high level of security along with low computational cost and energy consumption. ESIKE achieves perfect forward secrecy, anonymity, known-key security and untraceability properties. ESIKE can resist several attacks, such as, replay, DoS, eavesdropping, man-in-the-middle and modification. In addition, the formal security validation using AVISPA tools confirms the superiority of ESIKE in terms of security.

Power consumption is the most important factor to evaluate the performance of Wireless Sensor Networks (WSNs). Most sensor network Medium Access Control (MAC) protocols operate on the basis of a duty cycle mechanism. The asynchronous receiver initiated MAC duty cycle protocols are popular due to their relatively higher energy efficiency. However, recent advances harnessing the benefits of cooperative communication has become one of the solutions of MAC duty cycle protocol. In this article, we improve the RI-MAC protocol by introducing a short frame identifier to notify the sender when the receiver wakes up. This resolution reduces idle listening, which increases energy performance. When the sender node receives a short frame identifier, it cooperates with neighboring senders, which minimizes collisions.Our protocol is called: a Cooperative Short Frame Identifier Receiver Initiated MAC protocol, COSFI-RIMAC is an asynchronous MAC protocol cooperative service cycle initiated by the receiver. The simulation result on the NS2 simulator shows that the COSFI-RIMAC mechanism reduces power consumption, produces minor latency and increases the rate of packet delivery.

As public safety migrates from existing digital land mobile radio networks to fourth generation (4G) long-term evolution (LTE), a novel feature in the latter is the enablement of direct communications between devices. In this article, we examine the performance of the LTE device-to-device direct discovery process in out-of-coverage scenarios (Mode 2). We model individual discovery periods as a slotted random access protocol with half-duplex user equipment (UE). We outline a simulation framework using the open-source network simulator 3 (ns-3) and evaluate the discovery process’s performance as a function of the size of the resource pool, UE density, and the transmission probability. We establish that there exists an optimal transmission probability that minimizes discovery time for a given network configuration. However, because a UE cannot know the number of neighbors when it goes off-network, we develop a method to allow UEs to learn the optimal transmission probability during discovery. We compare our algorithm’s performance to the 3rd Generation Partnership Project standards prescribed method and others to demonstrate its superiority.

Web3.0, often cited to drastically shape our lives, is ubiquitous. However, few literatures have discussed the crucial differentiators that separate Web3.0 from the era we are currently living in. Via a thorough analysis of the recent blockchain infrastructure evolution, we capture a key invariant featuring the evolution, based on which we provide the first academic definition for Web3.0. Our definition is not the only way of understanding Web3.0, yet, it captures the fundamental and defining trait of Web3.0, and meanwhile it is has two desirable properties. Under this definition, we articulate three key infrastructural enablers for Web3.0: individual blockchains, federated or centralized platforms capable of publishing verifiable states, and an interoperability platform to hyperconnect those state publishers. While innovations in all categories are necessary to fully enable Web3.0, in this paper, we present a design for the third enabler, namely HyperService, that delivers interoperability and programmability across heterogeneous blockchains and state publishers. HyperService is powered by two innovative designs: a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in 62,000 lines of code to demonstrate its practicality, usability, and scalability.

The aggregate signature is a special signature mode widely used in the wireless medical sensor network. The aggregate signature can reduce bandwidth and storage space in this sensor network by aggregating multiple signatures from different signers into a short signature. Traditional aggregate signature protocols based on the discrete logarithm problem and the great integer factorization problem cannot resist quantum attacks. In this article, we propose a new noninteractive identity-based aggregate signature protocol from lattices. We prove that the proposed protocol is secure in the quantum random oracle model. Additionally, the performance of the proposed protocol is much better than most previous protocols according to our experiment. Meanwhile, the size of the aggregate signature in our protocol is a logarithmic function of the number of signatures being aggregated.

Mobile payment system has been expected to provide more efficient and convenient payment methods. However, compared to traditional payments, the issues of mobile payment related to the security of electronic accounts and payment apps present serious challenges. In this paper, we find the potential security risks by analyzing the commonly used tokenized mobile payment method and put forward the corresponding off-site attack strategy. In this scenario, the attackers are not only limited to malicious third parties but also can be illegal merchants. To address the off-site attack, especially the potential attackers who may be malicious merchants, we also propose SALP, a secure and authenticated payment protocol. We conduct case studies to demonstrate that the SALP can effectively prevent the off-site payment attack without a trusted hardware environment. In particular, we finally argue that SALP does not bring additional system overhead without degrading the convenience of mobile payment.

Deep neural network (DNN) shows great promise in providing more intelligence to ubiquitous end devices. However, the existing partition-offloading schemes adopt data-parallel or model-parallel collaboration between devices and the cloud, which does not make full use of the resources of end devices for deep-level parallel execution. This paper proposes eDDNN (i.e. enabling Distributed DNN), a collaborative inference scheme over heterogeneous end devices using cross-platform web technology, moving the computation close to ubiquitous end devices, improving resource utilization, and reducing the computing pressure of data centers. eDDNN implements D2D communication and collaborative inference among heterogeneous end devices with WebRTC protocol, divides the data and corresponding DNN model into pieces simultaneously, and then executes inference almost independently by establishing a layer dependency table. Besides, eDDNN provides a dynamic allocation algorithm based on deep reinforcement learning to minimize latency. We conduct experiments on various datasets and DNNs and further employ eDDNN into a mobile web AR application to illustrate the effectiveness. The results show that eDDNN can achieve the latency decrease by 2.98x, reduce mobile energy by 1.8x, and relieve the computing pressure of the edge server by 2.57x, against a typical partition-offloading approach.

Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this paper, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework. First, we design a Large-Scale Heterogeneous WSNs Collaborative Identity Verification protocol to ensure reliable data sources. Second, we build a Hierarchical Massive Data Aggregation scheme to collect massive IoT data efficiently and securely. Third, we depict a Blockchain-Based Massive IoT Data Management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.

Alternating current (AC) electrolysis is a promising, yet challenging, and under-developed protocol in organic synthesis. To achieve as high an atom-efficiency as possible and avoid the use of exte...

This article is concerned with the security-based resilient robust model predictive control (RMPC) problem for a class of discrete-time polytopic uncertain systems with deception attacks and the round-robin (RR) protocol. The well-known RR protocol is adopted with the hope to reduce the communication burden, where the control signal is transmitted only at the given transmission instant. By means of the concept of bounded energy and the independent Bernoulli-distributed (BD) white sequences, a representative attack model is given. A definition of mean-square (MS) security in H₂-sense is employed to reasonably explain the dynamics process of the controlled systems. We aim at designing a set of resilient RMPC controllers so as to make the closed-loop system state under consideration of deception attacks and the RR protocol enter and stay in a certain bounded region. Furthermore, with the aid of stochastic analysis methods and inequality analysis techniques, sufficient conditions are obtained to satisfy the desirable security requirements. To tackle the nonconvex obstacles resulting from attacks and the RR protocol, the cone complementary linearization (CCL) method is exploited to cast them into a convex optimization problem (OP) for its solvability. Then, an online OP regarding a certain upper bound of the concerned objective is provided for the solvability and resilient RMPC-based controller gains are obtained. Finally, two examples, including a high-purity distillation one and a numerical one, are used to demonstrate the effectiveness and the validity of the proposed techniques.

This paper investigates the second-order consensus issue for multi-agent systems subject to both limited communication resources and replay attacks. First, an asynchronous dynamic edge event-triggered (DEET) communication scheme is developed to reduce the utilization of network resources in the absence of attacks. Then, we further consider the case of replay attacks launched by multiple adversaries, under which the transmitted information is maliciously replaced by a previous unnecessary message. To overcome the impact caused by replay attacks, a modified DEET scheme and an effective attack-resilient consensus protocol are well constructed, both of which successfully guarantee second-order consensus in the presence of replay attacks. In addition, internal dynamic variables are utilized in the proposed DEET schemes such that the triggering time sequence does not exhibit Zeno behavior. Finally, one numerical example is provided to illustrate our theoretical analysis.

In this paper, periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles (UAVs). Firstly, based on the Artificial Potential Field (APF) method, a novel sliding manifold is proposed for controller design, which can solve the problem of collision avoidance. Then, the event-triggered strategy is applied to the distributed formation control of multi-UAV systems, where the evaluation of the event condition is continuous. In addition, the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound. Next, a periodic event-triggered mechanism is developed for formation control based on the continuous event-triggered mechanism. The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors, which can reduce the control cost. The stability of the control system is proved by the Lyapunov function method. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.

Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range of applications such as environmental control and surveillance. A crucial capability for autonomous operation is obstacle detection for timely reaction and collision avoidance, which has been recently explored in the context of camera-based visual scene interpretation. Owing to curated datasets, substantial advances in scene interpretation have been made in a related field of unmanned ground vehicles. However, the current maritime datasets do not adequately capture the complexity of real-world USV scenes and the evaluation protocols are not standardised, which makes cross-paper comparison of different methods difficult and hinders the progress. To address these issues, we introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation. Nineteen recent state-of-the-art object detection and obstacle segmentation methods are evaluated using the proposed protocol, creating a benchmark to facilitate development of the field. The proposed dataset, as well as evaluation routines, are made publicly available at vicos.si/resources., 16 pages, 15 figures. The dataset, as well as the proposed evaluation protocols, are published on our website: https://www.vicos.si/resources/

In this article, we study the consensus issues of multiagent systems (MASs) without any information of the system model by using the reinforcement learning (RL) method and event-based control strategy. First, we design an adaptive event-based consensus control protocol using the local sampled state information so that the consensus errors of all agents are uniformly ultimately bounded. The validity of the above event-triggered adaptive control protocol is confirmed by excluding the Zeno behavior within finite time. Then, based on the RL approach, we present a model-free algorithm to get the feedback gain matrix, and accomplish constructing the adaptive event-triggered control strategy without the knowledge of model information. Distinct with the existing related works, this RL-based event-triggered adaptive control algorithm only relies on the local sampled state information, irrelevant to any model information or global network information. Finally, we provide some examples to demonstrate the validity of the above adaptive event-based consensus algorithm.

This article addresses the three-dimensional (3-D) coordinated control problem of directed networked aircraft-like vehicles, that is to track a set of given orbits on a sphere and achieve a lateral formation flight. Different from the case of Newton particles, a nonholonomic dynamics with unknown disturbances is considered. A novel method to decouple the spherical orbit tracking subsystem and the lateral formation flying subsystem is proposed. By overlooking the control of the vehicle's surge velocity, a nonsmooth spherical orbit tracking algorithm is designed by backstepping. Without considering the spherical orbit tracking errors and using any global information of topologies, a distributed, nonsmooth formation protocol is designed. The input-to-state stability (ISS) theory is used to analyze the converge property of the interconnected system consisting of these two subsystems. Simulation results are given to verify the theoretical analysis.

Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

Modification of a pitched roof from a flat roof transforms the flat roofs of multi-storey buildings into pitched roofs to improve the building function and landscape effect, provided that the building structures and the foundation bearing capacity meet the requirements; it normally improves the thermal insulation performance of the top layer and solves problems such as leakage of the top layer. This study developed an optimal efficient design protocol for pitched roof modification from a flat roof through comparative analysis. The case study was conducted in an existing building in Hangzhou city, China. In the process of energy consumption simulation on pitched roof modification from a flat roof, control variables were used to keep the building materials, building environments and meteorological conditions consistent. The indoor environment of the building top floor with different gradient settings was simulated in groups, and the hourly temperature, monthly heat gains and losses, and energy consumption of each model were analysed. The results showed a consistent trend in building energy performance of the different models. With an increase in the slope gradient, the building energy performance is higher. This study provides an important reference for modification of pitched roofs from flat roofs in future in terms of building energy efficiency.

Online Ride Hailing (ORH) services enable a rider to request a taxi via a smartphone app in real time. When using ORH services, users (including riders and taxis) have to submit their locations to the ORH server. With received locations, the ORH server makes online ride matching between riders and taxis. There are serious privacy concerns for users to reveal location information to ORH servers. In this paper, we propose an efficient and privacy-preserving ride matching scheme for ORH services, named EPRide. EPRide can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location information of both taxis and riders against ORH servers or other curious servers. In EPRide, we propose an efficient exact shortest road distance computation approach over encrypted data, which converts road distance computation into Hamming distance computation over packed ciphertexts by using road network hypercube embedding and somewhat homomorphic encryption. Meanwhile, we design a secure comparison protocol, which efficiently compares encrypted distances in parallel by using ciphertexts blinding and packing, without leaking any distance. Theoretical analysis and experimental evaluations show that EPRide is secure, accurate and efficient.

Security and privacy of data-in-transit are critical issues in Industry 4.0 which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this work, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. Additionally, we conduct a security analysis that proves that the scheme resists several attacks in the industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

This article focuses on the distributed cooperative compound tracking issue of the vehicular platoon. First, a definition, called compound tracking control, is proposed, which means that the practical finite-time stability and asymptotical convergence can be simultaneously satisfied. Then, a modified performance function, named finite-time performance function, is designed, which possesses the faster convergence rate compared to the existing ones. Moreover, the adaptive neural network (NN), prescribed performance technique, and backstepping method are utilized to design a distributed cooperative regulation protocol. It is worth noting that the convergence time of the proposed algorithm does not depend on the initial values and design parameters. Finally, simulation experiments are given to further verify the effectiveness of the presented theoretical findings.

In this paper, a data-driven distributed leader-follower iterative learning consensus tracking control approach is proposed for unknown repetitive nonlinear non-affine discrete-time multi-agent systems. The leader's command is only communicated to a subset of the following agents and each following agent exchanges information only with its neighbors under a directed graph. A local iterative learning consensus control protocol is designed using only local measurements communicated among neighboring agents without the availability of physical and structural information of each agent by virtue of the dynamic linearization method both on the agent and the ideal distributed learning controller along the iteration axis. The convergent consensus properties of the tracking errors along the iteration axis are rigorously established under the strongly connected iteration-independent and iteration-varying communication topologies. One example is provided to validate the effectiveness of the proposed iterative learning consensus control protocol.

In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients' data at any point of time. Secure cloud storage protocols enable a client to check integrity of outsourced data. In this work, we explore the possibility of constructing a secure cloud storage for dynamic data by leveraging the algorithms involved in secure network coding. We show that some of the secure network coding schemes can be used to construct efficient secure cloud storage protocols for dynamic data, and we construct such a protocol (DSCS I) based on a secure network coding protocol. To the best of our knowledge, DSCS I is the first secure cloud storage protocol for dynamic data constructed using secure network coding techniques which is secure in the standard model. Although generic dynamic data support arbitrary insertions, deletions and modifications, append-only data find numerous applications in the real world. We construct another secure cloud storage protocol (DSCS II) specific to append-only data --- that overcomes some limitations of DSCS I. Finally, we provide prototype implementations for DSCS I and DSCS II in order to evaluate their performance.

This article proposes a dynamic gain approach for investigating the leader-follower consensus problem of nonlinear multiagent systems with both distributed delays and discrete delays. It is assumed that each agent is in the strict-feedback form and satisfies the Lipschitz condition with an unknown constant. A novel dynamic gain observer is first constructed for each follower by only utilizing the output information of the follower and its neighbors. By introducing an observer, a distributed output-feedback control protocol is designed for each follower agent under a directed communication topology. It is proved by the Lyapunov-Razumikhin theorem that the consensus of the multiagent system is achieved with the proposed consensus protocol. Different from the existing results, the dynamic gain is constructed for each follower to propose the consensus protocol, without resorting to the celebrated backstepping technique. Moreover, the time-varying gain is designed in a uniform framework. The effectiveness of the proposed approaches is illustrated by a simulation example.

With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two- way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this paper, we propose a privacy-preserving fair exchange scheme V2GEx for V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions cost only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.

For leader-following networked systems with the topology switching and the aperiodic silence, limited-energy output formation tracking problems are investigated. Firstly, a new output formation tracking control protocol is proposed, which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents, respectively, and the aperiodic silence, the topology switching and the energy constraint index is introduced properly. Then, a two-step transformation method is presented to separate the whole dynamics of a networked system into the relative dynamics between the leader and tracking intelligent agents and the dynamics of the leader, and sufficient conditions for limited-energy output formation tracking for networked systems with limited energy and aperiodic silence are presented, which are extended into networked systems without the aperiodic silence. Especially, a partition checking algorithm is presented to check limited-energy output formation tracking design criteria. Finally, a numerical example is illustrated to demonstrate the validness of theoretical results.