Precise braking force distribution is essential for the safety and efficiency of urban rail vehicles equipped with axle-controlled electro-pneumatic brake systems. However, conventional distribution strategies predominantly rely on static or empirical adhesion estimation, leading to suboptimal performance under time-varying track conditions and longitudinal load transfers. To address these issues, this paper proposes a dynamic distribution strategy based on real-time adhesion estimation. First, a longitudinal vehicle dynamics model incorporating dynamic axle load transfer and the LuGre friction formulation is established. An extended state observer is then designed to reconstruct the wheel-rail contact states and estimate the maximum available adhesion coefficient in real time. Subsequently, the braking force distribution is formulated as a constrained multi-objective optimization problem. An adaptive weight adjustment mechanism is also employed to dynamically coordinate total braking force tracking and wheel slide prevention. The proposed method is validated through hardware-in-the-loop experiments. Compared with conventional load-based and empirical adhesion estimation-based distribution methods, the proposed strategy significantly improves adhesion utilization and reduces braking distance under degraded adhesion scenarios.

Faults in braking actuators can compromise the safety and stability of urban rail train operations. Existing fault-tolerant control methods for trains struggle to guarantee both transient and steady-state braking performance quantitatively. In this paper, we propose a cooperative fault-tolerant braking control with prescribed performance for urban rail trains. A coupled multi-agent braking model is first developed, where each vehicle is treated as an independent and controllable agent subject to various uncertainties, input saturation and different levels of actuator faults. Further, incorporating a prescribed tracking performance function, a distributive adaptive terminal sliding mode controller is developed to ensure safe and reliable train braking control. The control input saturation nonlinearity is addressed by employing a smooth hyperbolic tangent function for approximation. Adaptation laws are introduced to mitigate the effects of parameter uncertainties and external disturbances. The efficacy of the proposed control scheme is validated through comprehensive numerical simulations.

With the rapid development of various applications in the Internet of Things (IoT), we have witnessed much progress with very wide differences in characteristics and requirements. In this article, we propose a novel dynamic channel assembling (DChA) strategy for channel access of heterogeneous secondary user (SU) flows in IoT-oriented cognitive radio networks (CRNs), making use of the priority queues based on fine-grained flow classification. Specifically, three categories of SU flows are considered, so-called the real-time SU (RSU) flows, the elastic large SU flows, and the elastic small SU flows. On top of this, channel access opportunities are distributed to the SU flows in three specially designed queues performing the channel access algorithm. The highlight of our main idea is that the RSU flows with higher priority are only supposed to assemble as few channels as possible, so long as their minimum requirements are fulfilled, thereby minimizing the impact on elastic SU (ESU) traffic. For the sake of performance evaluation, we utilize the continuous-time Markov chain to model our proposed strategy and conduct theoretical analyses. With the detailed theoretical analyses and extensive simulations, the proposed DChA strategy is demonstrated to be able to fulfill the deadline of SU flows, while significantly reducing the blocking probability as well as the completion time of the ESU flows.

This paper presents a multi-input piezoelectric (PE) energy harvesting circuit, which integrates a Series Synchronized Switch Harvesting on Inductor (S-SSHI) and a Voltage Doubler (VD). The proposed circuit uses the VD topology to reduce the number of diodes used in the resonant loop of the S-SSHI circuit, and hence more power available for the load. Besides, the circuit adopts a parallel connection for multiple different PE transducers, in which an inductor and the VD are re-used to harvest the energy. It improves the inductor's utilization and reduces the complexity of the circuit. The circuit is self-powered and capable of starting even if the battery is drained. Discrete components prototyped the circuit with a cost- effective implementation. The measured results show that compared with full-bridge topology, the proposed circuit increases the power harvested from two different PE transducer three times at the same excitation condition.

Recently, smart grid is considered as the next generation of power grid by introducing information and communication technologies. Line-loss is an important synthetic indicator which can directly reflect the energy efficiency and power management level of smart grid enterprises. In order to obtain all residential areas, line-loss requires obtaining electricity consumption of each user. However, data about users’ electricity consumption could reveal sensitive information; a sophisticated adversary can use some data analysis methods to deduce economic situation, habits, lifestyles, etc. In order to solve the problem, we propose an Efficient Privacy-preserving scheme for Line-loss Calculation, named EPLC. In our scheme, a data item is reading from one smart meter which implies the energy consumption in a time period of the user who owns it, and each user lives in a residential area. For each user, we encrypt user’s data based on Paillier cryptosystem by using two Horner parameters, by leveraging homomorphism, and each residential area gateway calculates relevant data about corresponding line-loss and control center hides the area-level polynomial into the final output for representing line-loss of all residential areas which are both in the form of ciphertext. Finally, we can still recover each residential area line-loss with possessing private keys and Horner parameters. Moreover, EPLC adopts the batch verification technique to lower authentication cost. Finally, our analysis indicates that EPLC is not only efficient but also can protect individual user’s electricity consumption privacy, and the flexibility and expansibility of EPLC are very suitable for smart grid.

In smart grid, some attackers manage to get their false price data and malicious disconnect commands to pass authentications on smart meters by stealing legal credentials. Against those threats, we propose the detection mechanisms based on context analyses. Specifically, we analyze the communication scenarios of true price data and normal disconnect command and investigate the attack scenarios. We also formalize the contexts for that true data and command as well as the attack contexts. We further design the detection unit with specific requirements and propose the detection algorithms respectively against false price data and malicious disconnect command, of which both are with legal credentials. We evaluate the detection mechanisms by selecting evaluation parameters through simulation procedures. Simulation results demonstrate how the parameters affect the detection accuracy and we suggest improvement measures to increase the detection accuracy.

In this paper, we propose two dependence link addition strategies to enhance the robustness of interdependent Cyber-Physical Systems. One is based on intra-degree and receiving capability difference and the other is based on intra-degree and receiving capability ratio. Numerical simulations demonstrate that the two strategies are better than adding dependence links randomly.

Summary Skyline computation is particularly useful in multi‐criteria decision‐making applications. However, it is inadequate to answer queries that need to analyze not only individual points but also groups of points. Compared to the traditional skyline computation, computing group‐based skyline is much more complicated and expensive. This computational challenge promotes us to use modern computing platforms to accelerate the computation. In this paper, we introduce a novel multi‐core algorithm to compute group‐based skyline. We first compute the skyline layers of a data set in parallel, which are a critical intermediate result. In the algorithm, we maintain an efficiently updatable data structure for the shared global skyline layers, which is used to minimize dominance tests and maintain high throughput. Then we design an efficient parallel algorithm to find group‐based skyline based on the skyline layers. Extensive experimental results on real and synthetic data sets show that our algorithms achieve 10‐fold speedup with 16 parallel threads over state‐of‐the‐art sequential algorithms on challenging workloads.

Summary Query processing over uncertain preferences is very common in real‐life situations, because many times, we cannot model users' preferences as strict partial orders. In this paper, we investigate skyline queries over uncertain preferences. The latest state‐of‐the‐art algorithm, calledUsky‐basealgorithm, makes significant advances. However, it still needs to be perfected in 2 aspects. (1) Theoretic analysis: The correctness of the algorithm is not fully verified. (2) Efficiency: Due to the heavy calculation introduced by adoptinginclusion‐exclusion principleto express the skyline probability, it needs massive time when computing skyline probabilities for large data sets. To address the above 2 concerns, we first review theUsky‐basealgorithm and lemmas it based on. Then we propose a novel parallel algorithm, calledParallel‐sky, to compute skyline probability of a given object. Moreover, we propose an adding algorithm and a deleting algorithm to deal with dynamic scenarios where new objects are added in and outdated objects are deleted out. Furthermore, we extend our algorithm from computing skyline probability of a given object to all objects in a data set. We conduct extensive experiments on real and synthetic data sets to validate the effectiveness and efficiency of our proposals.

The integration of social networking concepts with Internet of Vehicles (IoV) has led to the novel paradigm “Social Internet of Vehicles (SIoV),” which enables vehicles to establish social relationships autonomously to improve traffic conditions and service discovery. There is a growing requirement for effective trust management in the SIoV, considering the critical consequences of acting on misleading information spread by malicious nodes. However, most existing trust models are rater-based, where the reputation information of each node is stored in other nodes it has interacted with. This is not suitable for vehicular environment due to the ephemeral nature of the network. To fill this gap, we propose a Ratee-based Trust Management (RTM) system, where each node stores its own reputation information rated by others during past transactions, and a credible CA server is introduced to ensure the integrality and the undeniability of the trust information. RTM is built based on the concept of SIoV, so that the relationships established between nodes can be used to increase the accuracy of the trustworthiness. Experimental results demonstrate that our scheme achieves faster information propagation and higher transaction success rate than the rater-based method, and the time cost when calculating trustworthiness can meet the demand of vehicular networks.

Supervisory control and data acquisition (SCADA) system is a vital component of critical infrastructures (CIs). However, most protocols in SCADA systems lack either authentication or integrity checking mechanisms, which makes them extremely vulnerable to cyber attacks when increasingly more SCADA systems are connected with external networks. Intrusion detection systems (IDSs) have been proposed to enhance the system security, but few of them can effectively resist response injection and denial of service attacks at the same time. In this paper we present an IDS named PT-IDS to fill this gap by investigating the periodicity and telemetry patterns of network traffic within typical SCADA systems. Firstly, we analyze the periodicity characteristics in SCADA networks and classify them into four categories through designing an analyzer algorithm. Furthermore, in order to effectively detect response injection attacks, we design an auxiliary module to analyze the network telemetry pattern. Results from both modules are considered simultaneously to promote the accuracy of intrusion detection, especially for denial of service attacks. Beyond that, our proposed system can give alarm reports including both warnings and matching severity information. The time complexity of both analyzer algorithms is polynomial and simulations demonstrate the effectiveness and efficiency of our IDS mechanism.

In wireless ad hoc networks (WANETs), collaboration among nodes is usually inefficient and the network is vulnerable to various attacks. Although some intrusion detection systems (IDSs) and reputation strategies have been proposed to enhance the network's robustness, the significant power consumption they cause will reduce the networks' lifetime. In this paper, we propose a power-efficient mechanism based on game theoretic analysis to improve the security of WANETs and stimulate the cooperation among nodes. Specifically, we introduce two detection modes for the IDS and formulate the interactions between malicious/regular nodes and IDSs as a Bayesian game. The Nash equilibrium strategies in static Bayesian game model is firstly analyzed, which can not only reduce the probability of misbehaviors (e.g., attacks and non-cooperations) but also prolong the IDS's service time. Then, We extend the static game to a multi-stage imperfect dynamic Bayesian game, in which the IDS updates its belief based on observations and nodes' equilibrium strategy will also change accordingly. Finally, the perfect Bayesian equilibrium (PBE) knowledge is applied to analyze this game, based on which the optimal monitoring scheme for IDS is made out. Simulations on two typical networks demonstrate the effectiveness and practicability of the proposed approach.

The popularity of online social network (OSN) services has given rise to a variety of social network applications. However these applications lack a common platform for information sharing and people interoperating. In this paper, we propose SocAware, a middleware designed for OSN services. SocAware extracts social relation from heterogeneous networks, and builds a uniform knowledge base to manage the social information. SocAware distinguishes itself from other OSN middlewares by analyzing the OSN activities to classify the social relations, and by calculating the strength of social relations to provide reusability between social applications. We also provide a set of API to facilitate third-party application development and the effective utilization of these relations. In order to validate SocAware, we developed two prototype applications above the middleware. The experimental results demonstrate the usability and expansibility of the middleware.

The security of inter-domain routing system greatly depends on the trustworthiness of routing information and routing behavior of autonomous system (AS). Many researches on e-commerce, grid, and p2p have proven that reputation mechanism is helpful to inhibit the spread of false route and the occurrence of malicious routing behavior. To increase AS resistance to malicious routing attack, we designs an alliance based reputation model for AS routing behavior trust evaluation. Our approach calculates AS reputation with the Bayesian probability model and manages AS reputation with AS alliance. Compared with the fully distributed reputation model, our model has lower storage and communication overhead. This reputation model is incremental deployment and easy to implement. It can be employed for securing AS routing and assisting malicious behavior detection.

The current implementation of BGP protocol has a variety of vulnerabilities and weakness. Monitoring BGPpsilas behavior is an effective way to improve the security of inter-domain routing. Due to the difficulty of obtaining routing tables from autonomous systems, a packet-based model for detecting routing anomalies is presented. This model contains data collectors, anomaly detection engine, routing information database and result visualization module. A rule-based approach is designed, and the combined usage of rules and routing information database is proved to be effective on improving the accuracy of detection. Experiment results show that model performs well in detecting various anomalies. The feasibility and validity of the detecting approach are demonstrated by the detailed description of the deployment and performance analysis.

Border gateway protocol (BGP) acts as a vital part of the global infrastructure. Attacks against BGP are increasing in number and severity. Unfortunately, most security mechanisms based on public key cryptography suffer from performance, trust model and other issues. This paper proposes a solution that takes advantages of the power-law and rich-club features of the AS-level topology, and proposes the notion of AS Alliance and a new trust model - translator trust model (TTM). TTM avoids the global distribution of certificates by trust translating between different trust domains. It achieves that with much less memory overhead than traditional solutions, and a shorter validation chain. We develop a novel SE-BGP (security enhanced BGP) mechanism based on TTM. It introduces new path attributes to carry origin certificates and path signatures, and the algorithms to process origin authentication and path authentication. Our analyses indicate that SE-BGP is a viable solution.

experimental Mbone for a number of years. The practical pricing mechanism is the foundation for the deploying of IP multicast in the inter-domain Internet. The IP multicast service model and its pricing mechanism are discussed in this paper. Three models are proposed for all applications in the real environments. They are ICP-USER model, ICP-ISP model and ICP-ISP-USER model. Here, the Internet is considered as an ecosystem, and the entities construct a supply chain with the welfare maximum purpose. Our work gives a general discussion on the practical pricing mechanism for the stability of the economic relationship between ICPs, ISPs and users in the Internet. Key Words—IP multicast, pricing mechanism, Cost-sharing mechanism

WSN congestion occurs when offered traffic load exceeds available capacity. It causes overall channel quality to degrade and drop rates to rise. Furthermore, redundant transmissions are always adopted to guarantee reliable data delivery, which may deteriorate congestion since they bring on more contention and in reverse hampers the reliability. In this paper, we propose a framework to avoid, detect and mitigate congestion effectively. In this framework, a congestion aware traffic allocation (COTA) is used in multipath routing to balance traffic around the whole network COTA uses some heuristic information to analyze the potential congestion region and avoid traversing these regions. A runtime traffic adjustment CODEM is presented to use accurate metrics to detect and mitigate congestion. Compared with previous works, our work can control congestion while achieving the desired reliability at the same time. Comprehensive simulations have validated the distinguished performance in several aspects of our framework.

This paper analyzes the relationship between BGP convergence and the power-law of the Internet. The inter-domain routing system is classified into three hierarchies based on the power-law and commercial relations of autonomous systems. The relation of network topology and three convergence parameters-convergence time T, affected ASs set Nc and affected paths factor mu is presented for all sorts of convergence events in different layers. The result shows that the power-law nature of network influences the BGP convergence greatly

Random walks are an excellent search mechanism in unstructured P2P network. However, it suffers long delay when searching files, especially for uncommon files. An efficient search mechanism - ARW is presented. ARW utilizes path information carrying and walker self-replication technologies to increase the number of different peers all walkers visit and reduces the delay which random walks spends on searching files especially for uncommon files greatly. Experimental results show that ARW reduces the delay of random walks on searching uncommon files by 63.7% with almost no extra overhead in a Gnutella-like overlay network and gets a good tradeoff between performance and overhead.