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The healthcare industry deals with highly sensitive data which must be managed in a secure way. Electronic Health Records (EHRs) hold various kinds of personal and sensitive data which contain names, addresses, social security numbers, insurance numbers, and medical history. Such personal data is valuable to the patients, healthcare service providers, medical insurance companies, and research institutions. However, the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers. Hence, we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications. Blockchain is one of the promising solutions, aimed to provide transparency, security, and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems. Therefore, to solve the mentioned problems in healthcare applications, in this paper, we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications. We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications. Additionally, we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality, privacy and access control. The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security, regulation compliance, compatibility, flexibility, and scalability.

Modern vehicles equipped with a large number of electronic components, sensors, actuators, and extensive connectivity, are the classical example of cyber-physical systems (CPS). Communication as an integral part of the CPS has enabled and offered many value-added services for vehicular networks. The communication mechanism helps to share contents with all vehicular network nodes and the surrounding environment, e.g., vehicles, traffic lights, and smart road signs, to efficiently take informed and smart decisions. Thus, it opens the doors to many security threats and vulnerabilities. Traditional TCP/IP-based communication paradigm focuses on securing the communication channel instead of the contents that travel through the network. Nevertheless, for content-centered application, content security is more important than communication channel security. To this end, named data networking (NDN) is one of the future Internet architectures that puts the contents at the center of communication and offers embedded content security. In this paper, we first identify the cyberattacks and security challenges faced by the vehicular CPS (VCPS). Next, we propose the NDN-based cyber-resilient, the layered and modular architecture for VCPS. The architecture includes the NDN's forwarding daemon, threat aversion, detection, and resilience components. A detailed discussion about the functionality of each component is also presented. Furthermore, we discuss the future challenges faced by the integration of NDN with VCPS to realize NDN-based VCPS.

Vehicular sensor networks (VSN) provide a new paradigm for transportation technology and demonstrate massive potential to improve the transportation environment due to the unlimited power supply of the vehicles and resulting minimum energy constraints. This special issue is focused on the recent developments within the vehicular networks and vehicular sensor networks domain. The papers included in this Special Issue (SI) provide useful insights to the implementation, modelling, and integration of novel technologies, including blockchain, named data networking, and 5G, to name a few, within vehicular networks and VSN.

لقد كثرت الكتابات والنقاشات التي تطرقت الى تاصيل مناهج البحث وتفسيرها وتحديد الطرائق والاساليب المنهجية التي يستخدمها الباحثون والكتاب وهم يتصدون للموضوعات والمشكلات البحثية في مجالات العلوم الطبيعية والانسانية كلها,الا اننا نلحظ ان حركة العلوم ومعارفها وتطورها يتطلب تحديد الادوات والاساليب المنهجية الملائمة والمناسبة لكل علم من العلوم او محاولات بناء ادوات منهجية ملائمة لنشاط انساني ومعرفي يمكن ان يشار اليه بوصفه علما محددا يخط مسالك خاصة به من العلوم الانسانية التي هي بالتاكيد الاصل لكل البناء المنهجي والبحثي للمعارف الانسانية والاجتماعية والتاريخية الخ. ولاتساع نشاط وسائل الاعلام واهميتها وتطورها في مختلف الحياة وتداخلها مع فروع العلوم الاجتماعية والانسانية عامة. لابد من تحديد الادوات الخاصة وفرزها وتوظيفها بهذا النشاط المعرفي والانساني والتقني بحيث تصبح الادوات المنهجية التي تستخدم في الدراسات الاعلامية وما يتعلق باثرها وتاثيرها وجمهورها ملائمة للتطور الحاصل من جمع المعلومات وتفريغها وقياسها وباسلوبي الكمي والمعرفي ,لذا يمكن ان نخضع موضوعا ما يراد تحليله او مسحه بوساطة الادوات والدلائل الرياضية الكمية حتى نتمكن من وصف وتحديد عواملها ومتغيراتها الرئيسة والفرعية بصورة دقيقة ,ونوكد بدورنا ان عملية الوصف التعبيري تحتاج الى العلاقات والارتباطات الاحصائية الدالة والمعبرة عن فرضيات او تساؤلات البحث . وما سنجده في بحثنا هذا هي محاوات علمية تسعى لايجاد طرائق المناهج العلمية الخاصة بالظواهر الاعلامية وادواتها وتوظيفها وما يحيط بها من تداخلات وتفاعلات حركية انسانية اجتماعية تاريخية رياضية تلائم التطور الحاصل في مجالات الاعلام كلها ووسائل الاتصال المعاصرة .

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.

Flying Ad hoc Network (FANET) is a new resource-constrained breed and instantiation of Mobile Ad hoc Network (MANET) employing Unmanned Aerial Vehicles (UAVs) as communicating nodes. These latter follow a predefined path called ’mission’ to provide a wide range of applications/services. Without loss of generality, the services and applications offered by the FANET are based on data/content delivery in various forms such as, but not limited to, pictures, video, status, warnings, and so on. Therefore, a content-centric communication mechanism such as Information Centric Networking (ICN) is essential for FANET. ICN addresses the problems of classical TCP/IP-based Internet. To this end, Content-centric networking (CCN), and Named Data Networking (NDN) are two of the most famous and widely-adapted implementations of ICN due to their intrinsic security mechanism and Interest/Data-based communication. To ensure data security, a signature on the contents is appended to each response/data packet in transit. However, trusted communication is of paramount importance and currently lacks in NDN-driven communication. To fill the gaps, in this paper, we propose a novel trust-aware Monitor-based communication architecture for Flying Named Data Networking (FNDN). We first select the monitors based on their trust and stability, which then become responsible for the interest packets dissemination to avoid broadcast storm problem. Once the interest reaches data producer, the data comes back to the requester through the shortest and most trusted path (which is also the same path through which the interest packet arrived at the producer). Simultaneously, the intermediate UAVs choose whether to check the data authenticity or not, following their subjective belief on its producer’s behavior and thus-forth reducing the computation complexity and delay. Simulation results show that our proposal can sustain the vanilla NDN security levels exceeding the 80% dishonesty detection ratio while reducing the generated end-to-end delay to less than 1 s in the worst case and reducing the average consumed energy by more than two times.