Vehicular ad hoc networks (VANETs) have the potential to transform the way people travel through the creation of a safe interoperable wireless communications network that includes cars, buses, ...traffic signals, cell phones, and other devices. However, VANETs are vulnerable to security threats due to increasing reliance on communication, computing, and control technologies. The unique security and privacy challenges posed by VANETs include integrity (data trust), confidentiality, nonrepudiation, access control, real-time operational constraints/demands, availability, and privacy protection. The trustworthiness of VANETs could be improved by addressing holistically both data trust, which is defined as the assessment of whether or not and to what extent the reported traffic data are trustworthy, and node trust, which is defined as how trustworthy the nodes in VANETs are. In this paper, an attack-resistant trust management scheme (ART) is proposed for VANETs that is able to detect and cope with malicious attacks and also evaluate the trustworthiness of both data and mobile nodes in VANETs. Specially, data trust is evaluated based on the data sensed and collected from multiple vehicles; node trust is assessed in two dimensions, i.e., functional trust and recommendation trust, which indicate how likely a node can fulfill its functionality and how trustworthy the recommendations from a node for other nodes will be, respectively. The effectiveness and efficiency of the proposed ART scheme is validated through extensive experiments. The proposed trust management theme is applicable to a wide range of VANET applications to improve traffic safety, mobility, and environmental protection with enhanced trustworthiness.
Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast ...adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, the security of IoT should start with foremost securing WSNs ahead of the other components. However, owing to the absence of a physical line-of-defense, i.e., there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. More specifically, for the application areas in which CIA (confidentiality, integrity, availability) has prime importance, WSNs and emerging IoT technology might constitute an open avenue for the attackers. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: "Passive Attacks" and "Active Attacks". Understanding these attacks and their associated defense mechanisms will help paving a secure path towards the proliferation and public acceptance of IoT technology.
Cyber-Physical Systems Houbing Song, Danda B Rawat, Sabina Jeschke, Christian Brecher / Houbing Song, Danda B. Rawat, Sabina Jeschke, Christian Brecher
2016, 2016-09-11
eBook
Cyber-Physical Systems: Foundations, Principles and Applications explores the core system science perspective needed to design and build complex cyber- physical systems. Using Systems Science's ...underlying theories, such as probability theory, decision theory, game theory, organizational sociology, behavioral economics, and cognitive psychology, the book addresses foundational issues central across CPS applications, including System Design -- How to design CPS to be safe, secure, and resilient in rapidly evolving environments, System Verification -- How to develop effective metrics and methods to verify and certify large and complex CPS, Real-time Control and Adaptation -- How to achieve real-time dynamic control and behavior adaptation in a diverse environments, such as clouds and in network-challenged spaces, Manufacturing -- How to harness communication, computation, and control for developing new products, reducing product concepts to realizable designs, and producing integrated software-hardware systems at a pace far exceeding today's timeline. The book is part of the Intelligent Data-Centric Systems: Sensor- Collected Intelligence series edited by Fatos Xhafa, Technical University of Catalonia. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS * Includes in-depth coverage of the latest models and theories that unify perspectives, expressing the interacting dynamics of the computational and physical components of a system in a dynamic environment * Focuses on new design, analysis, and verification tools that embody the scientific principles of CPS and incorporate measurement, dynamics, and control * Covers applications in numerous sectors, including agriculture, energy, transportation, building design and automation, healthcare, and manufacturing
The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user ...terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users' preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.
Visual perception refers to the process of organizing, identifying, and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition ...technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this article, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis, and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this article also summarizes the current challenges of visual perception and predicts its future development trends.
Cognitive networks (CNs) are one of the key enablers for the Internet of Things (IoT), where CNs will play an important role in the future Internet in several application scenarios, such as ...healthcare, agriculture, environment monitoring, and smart metering. However, the current low packet transmission efficiency of IoT faces a problem of the crowded spectrum for the rapidly increasing popularities of various wireless applications. Hence, the IoT that uses the advantages of cognitive technology, namely the cognitive radio-based IoT (CIoT), is a promising solution for IoT applications. A major challenge in CIoT is the packet transmission efficiency using CNs. Therefore, a new Q-learning-based transmission scheduling mechanism using deep learning for the CIoT is proposed to solve the problem of how to achieve the appropriate strategy to transmit packets of different buffers through multiple channels to maximize the system throughput. A Markov decision process-based model is formulated to describe the state transformation of the system. A relay is used to transmit packets to the sink for the other nodes. To maximize the system utility in different system states, the reinforcement learning method, i.e., the Q learning algorithm, is introduced to help the relay to find the optimal strategy. In addition, the stacked auto-encoders deep learning model is used to establish the mapping between the state and the action to accelerate the solution of the problem. Finally, the experimental results demonstrate that the new action selection method can converge after a certain number of iterations. Compared with other algorithms, the proposed method can better transmit packets with less power consumption and packet loss.
Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical ...imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.
Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud ...infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.
The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, ...biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.
Many researches show that the power consumption of network devices of ICT is nearly 10% of total global consumption. While the redundant deployment of network equipment makes the network utilization ...is relatively low, which leads to a very low energy efficiency of networks. With the dynamic and high quality demands of users, how to improve network energy efficiency becomes a focus under the premise of ensuring network performance and customer service quality. For this reason, we propose an energy consumption model based on link loads, and use the network's bit energy consumption parameter to measure the network energy efficiency. This paper is to minimize the network's bit energy consumption parameter, and then we propose the energy-efficient minimum criticality routing algorithm, which includes energy efficiency routing and load balancing. To further improve network energy efficiency, this paper proposes an energy-efficient multi-constraint rerouting (E2MR2) algorithm. E2MR2 uses the energy consumption model to set up the link weight for maximum energy efficiency and exploits rerouting strategy to ensure network QoS and maximum delay constraints. The simulation uses synthetic traffic data in the real network topology to analyze the performance of our method. Simulation results that our approach is feasible and promising.
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