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There has been an asymmetric shift towards harnessing cloud-based technologies as the world focuses on shifting operations remotely. Data security for remote operations is crucial for the protection and preservation of critical infrastructure. Furthermore, there has been an emerging trend to integrate IoT based devices with the expanding cloud infrastructure. In this work, mobile cloudbased infrastructure is considered where contact opportunities are developed in an opportunistic manner so as to facilitate efficient data forwarding and secure process handling. A probabilistic framework is proposed that facilitates data routing between the nodes and local cloud in an IoT network coupled with a multi-tier trust and encryption scheme for secure data delivery in the cloudbased IoT network. The proposed scheme is evaluated with simulations over two datasets against attack resilience and routing efficiency-based performance metrics, comparing to standard protocols like PRoPHET, MaxProp, ProWait and TCAFE, which displays the enhancement in operations of Sec-CIoT.

Internet of Things (IoT) comprises of a huge number of connected devices that can communicate within the same network and across the networks. IoT enabled Wireless Sensor Networks (WSN) are getting growing interest due to its wide applicability in healthcare, patient monitoring, transportation, and surveillance. The main issue is that the network is mostly deployed in hostile environments where an attacker may physically destroy the CHs or other technical fault may occur. It isolates the cluster and causes loss of sensitive data from that region. This paper presents a Critical Data Reclamation (CDR) protocol that provides secure data transmission for isolated clusters. We present the data transfer, data aggregation algorithms for sensing nodes and data receiving and extraction at CH and sink. We performed extensive simulations using NS-2.35. Results prove the dominance of CDR in contrast to counterparts in terms of communication cost, energy consumption, and resilience.

Efficient traffic control center (TCC) management requires roadside units (RSUs) to frequently update vehicles with traffic messages (TMs). RSUs are costly to road operators in terms of deployment and maintenance. Therefore, vehicular users expect the TCC to deliver high-quality and rapid responses to query requests on TMs through RSUs. This paper presents a novel RSU selection scheme based on a software-defined network (SDN) for traffic message transmission. In the proposed scheme, the RSU selection is determined by comparing the utility function calculated at the SDN controller. The SDN controller discovers the network connectivity topology through a cluster approach in which a cluster head (CH) is elected to report the network topology to the SDN controller. Furthermore, the SDN controller relies on the RSU selection manager to load existing RSU deployment techniques. In addition, a distance vector hop localization algorithm (DV-hop) is used to locate hidden RSUs to control signaling between the SDN controller and local controllers at RSUs. The proposed scheme achieves minimum latency and appropriate RSU management within a higher packet reception rate, which improves the selection of RSUs and traffic message delivery.

EGR1 (early growth response 1) is dysregulated in many cancers and exhibits both tumor suppressor and promoter activities, making it an appealing target for cancer therapy. Here, we used a systematic multi-omics analysis to review the expression of EGR1 and its role in regulating clinical outcomes in breast cancer (BC). EGR1 expression, its promoter methylation, and protein expression pattern were assessed using various publicly available tools. COSMIC-based somatic mutations and cBioPortal-based copy number alterations were analyzed, and the prognostic roles of EGR1 in BC were determined using Prognoscan and Kaplan-Meier Plotter. We also used bc-GenEx- Miner to investigate the EGR1 co-expression profile. EGR1 was more often downregulated in BC tissues than in normal breast tissue, and its knockdown was positively correlated with poor survival. Low EGR1 expression levels were also associated with increased risk of ER+, PR+, and HER2- BCs. High positive correlations were observed among EGR1, DUSP1, FOS, FOSB, CYR61, and JUN mRNA expression in BC tissue. This systematic review suggested that EGR1 expression may serve as a prognostic marker for BC patients and that clinicopathological parameters influence its prognostic utility. In addition to EGR1, DUSP1, FOS, FOSB, CYR61, and JUN can jointly be considered prognostic indicators for BC. [BMB Reports 2021; 54(10): 497-504].

The new paradigm of fog computing was extended from conventional cloud computing to provide computing and storage capabilities at the edge of the network. Applied to vehicular networks, fog-enabled vehicular computing is expected to become a core feature that can accelerate a multitude of services including crowd-sensing. Accordingly, the security and privacy of vehicles joining the crowd-sensing system have become important issues for cyber defense and smart policing. In addition, to satisfy the demand of crowd-sensing data users, fine-grained access control is required. In this paper, we propose a secure and privacy-preserving crowd-sensing scheme for fog-enabled vehicular computing. The proposed architecture is made by a double layer of fog nodes that is used to generate crowd-sensing tasks for vehicles, then collect, aggregate and analyze the data based on user specifications. To ensure data confidentiality and fined-grained access control, we make use of ciphertext-policy attribute-based encryption with access update policy (CP-ABE-UP), which is a well-known one-to-many encryption technique. The policy update algorithm allows the fog nodes to outsource the crowd-sensing data to other fog nodes or to data users directly. We also adopted the ID-based signature tied to pseudonymous techniques to guarantee the authentication and privacy-preservation of the entities in the system. From the upper fog layer to the data user, we show that an information-centric networking (ICN) approach can be applied to maximize the network resources and enhance the security by avoiding unauthorized and unauthenticated data owners. The security analysis confirms that our approach is secure against known attacks, whereas the simulation results show its efficiency in terms of communication with little computational overhead.

The Internet of Things (IoT) provides new opportunities for different IoT platforms connecting various devices together. The need to identify those devices is the foremost important to perform any kind of operation. Many organizations and standard bodies that provide specifications and frameworks for the IoT currently have their own identification mechanisms. Some existing industrial identification mechanisms can also be used in the IoT. There is no common Identification Scheme (IS) for the IoT as yet, because of the political and commercial differences amongst the standard bodies. The unavailability of a unified IS method makes the inter-working among IoT platforms challenging. This paper analyses and compares ISs used by several selected IoT platforms. This work will help in understanding the need for a common identification mechanism to provide inter-working among different IoT platforms.

Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today's medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

Vehicular networks aim to support cooperative warning applications that involve the dissemination of warning messages to reach vehicles in a target area. Due to the high mobility of vehicles, imperative technologies such as software-defined network (SDN) and edge computing (EC) have been proposed for the next-generation vehicular networks. The SDN separates the control plane from data plane entities and executes the control plane software on general purpose hardware. On the other hand, EC aims to reduce the network latency and packet loss rate by pushing the computations to the edge of the network. Nevertheless, the current solutions that integrate SDN and EC could not satisfy the latency requirements for data dissemination of vehicle-to-everything (V2X) services. To bridge the gap between the two technologies, the conventional EC is enhanced to multi-access edge computing (MEC) by collocating the edge computing servers with the radio access networks. In order to improve the latency for V2X services, we propose in this paper, an SDN-based multi-access edge computing framework for the vehicular networks (SDMEV). In the proposed solution, two main algorithms are implemented. First, a fuzzy logic-based algorithm is used to select the head vehicle for each evolved node B (eNB) collocated with road-side unit (RSU) for the purpose of grouping vehicles based on their communication interfaces. Afterward, an OpenFlow algorithm is deployed to update flow tables of forwarding devices at forwarding layers. In addition, a case study is presented and evaluated using the object-oriented modular discrete event network (OMNeT++) simulation framework which includes the INET framework-based SDN. Simulation results depict that the data dissemination based-SDN supported by multi-access edge computing over SDMEV can improve the latency requirements for V2X services.

In order to cater to the energy requirement in the form of steam at a reasonable cost, the process industries are relying on the waste incineration plants by engaging themselves through industry symbiosis. However, before the establishment of industrial symbiosis, it is very crucial to monitor and predict the operational performance of the boiler used in the waste incineration plants. The existing works focus on using Artificial Neural Networks (ANNs) for prediction of the performance of the boiler in terms of pressure, temperature, and mass flow rate of steam using the input parameters viz. feed water temperature, feed water pressure, incinerator exit temperature and conveyor speed. However, the problem with this approach is that shallow ANNs cannot model the complex mathematical non-linear relationships so precisely. In addition, ANNs are not interpretable which makes stakeholders apprehensive to use these networks in production. In this paper, we address these drawbacks of ANNs by modeling the complex relationship governing the boiler performance by using a set of machine learning and deep learning models. Also, the research paper introduces multiple techniques like feature importance, Partial Dependence Plots(PDP) plots etc. which interpret the reason behind the model's output to make it more reliable for the stakeholders. It has been empirically shown that the new Machine Learning and Deep Learning models performed better than the ANNs for predicting the boiler performance. The Random Forest model made a Mean Absolute Percentage Error (MAPE) of 1.12 and LSTMs had a MAPE of 1.14 in the prediction of steam temperature Co which is a significant improvement in comparison to the original ANN model which had a MAPE of 6.93. In the case of the predictions for steam pressure kgf /cm2 the MAPE for the Random Forest model and LSTM was 5.54 and 4.21 respectively as compared to ANNs MAPE of 1.49. Similarly for steam mass flow rate(t/h), the MAPE was improved to 15.6 and 9.63 by Random Forest Model and LSTM respectively, which was originally 18.77 for ANN based model. These results clearly show that LSTM based models outperformed ANNs and Random Forests in terms of prediction accuracy.

A Web attack protection system is extremely essential in today's information age. Classifier ensembles have been considered for anomaly-based intrusion detection in Web traffic. However, they suffer from an unsatisfactory performance due to a poor ensemble design. This paper proposes a stacked ensemble for anomaly-based intrusion detection systems in a Web application. Unlike a conventional stacking, where some single weak learners are prevalently used, the proposed stacked ensemble is an ensemble architecture, yet its base learners are other ensembles learners, i.e. random forest, gradient boosting machine, and XGBoost. To prove the generalizability of the proposed model, two datasets that are specifically used for attack detection in a Web application, i.e. CSIC-2010v2 and CICIDS-2017 are used in the experiment. Furthermore, the proposed model significantly surpasses existing Web attack detection techniques concerning the accuracy and false positive rate metrics. Validation result on the CICIDS-2017, NSL-KDD, and UNSW-NB15 dataset also ameliorate the ones obtained by some recent techniques. Finally, the performance of all classification algorithms in terms of a two-step statistical significance test is further discussed, providing a value-added contribution to the current literature.

Current avoidance systems mainly focus on the safety of the car occupants. The surrounding entities including the pedestrians, the cyclists are assumed to use a different avoidance system for their safety. Vehicle speed is reported as one of the major factors that causes such severe road accidents that affect other entities on the road. In response, several solutions have been implemented to control the causalities of over speeding ranging from speed camera, speed detectors to car avoidance systems. However, those solutions have not significantly improved the rate of traffic accidents and their impact. Additionally, the current solutions do not ensure timely notification of all the road users (surrounding vehicle drivers, pedestrians or others) that can alleviate crash causalities in case of fatal traffic accidents. The fifth generation (5G) cellular network is predicted to overcome the current limitations of Internet-of-Vehicles (IoV) by offering fast, low latency and reliable connections to enable IoV based applications. Fog computing has also been proposed to complement IoV by bringing computational entities in nearby proximity of the vehicles. 5G based fog vehicular networks is a new paradigm that empower real-time and low latency services for Intelligent Transportation System (ITS). In this paper we proposed a secure and privacy-preserving collision avoidance system in 5G fog based IoV. The fog devices are used to collect speed violation report (TVR) sent by the vehicles’ speed sensors. The fog nodes aggregate multiple TVRs, verify the signatures on the TVRs and broadcast anonymous notifications to other entities in the vicinity. The protocol makes use of certificateless aggregate signcryption coupled with pseudonymous technique as the building blocks to ensure authentication, integrity, confidentiality and privacy preservation respectively. The batch verification technique is used by the fog devices to allow simultaneous TVRs signature verification for a timely response. The authorization of reporting speed sensors is both guaranteed by the location-based information along with the digital signature to discard all the bogus TVRs. The analysis of the protocol confirms its lightweightness and efficiency.

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 &times, 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for health services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services. The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses. However, unless the underlying combination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients. Patients also lose full control of their EHR since most health services within IoMT are constructed under a centralized platform outsourced in the cloud. Therefore, it is appealing to design a decentralized, auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and security. Using the features of blockchain including decentralization, auditability and immutability, we propose a secure EHR framework which is mainly maintained by the medical centers. In this framework, the patients' EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the blockchain. We make use of security primitives to offer authentication, integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain technology. The security analysis and performance evaluation of the proposed framework confirms its efficiency., 9 pages (CMC Journal, Tech Science Press)

Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas, e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the unmonitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient unsupervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM) and K Nearest Neighbor (KNN) to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM., 25 pages, 10 figures, accepted for the publication in future issue of "Computer Communications (2020)"

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices, the client device sends the victim&rsquo, s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

Smart cities are claimed to be smart if the new technologies are capable of providing desired sustainable outcome. The sustainable properties of smart city applications require less energy consumption and efficient resource allocation. The Internet-of-Things (IoT), 5G, and fog networks have emerged as the most crucial researched areas due to their numerous applications for smart cities to provide the desired sustainable outcome. The sustainable properties of Wireless Sensor Networks (WSNs) play a vital role in the deployment of these technologies into the physical world and efficient utilization of the available spectrum is a major problem faced here. As a potential solution of this, Cognitive Radio (CR) merged with WSN as Cognitive Radio Sensor Networks (CRSNs) make the smart perspective with high resource management through cooperative communication. The proposed work establishes a dynamic correlation between Secondary Users/nodes (SUs) in a single cluster according to their statistical behavior at the time of performing smart cooperative communication in CRSNs to improve sustainability of smart world IoT applications. Distributed Artificial Intelligence (DAI) is used to calculate the real-time resource allocation to these clusters using their respective Coordinator Agent (CoA) based on the dynamic behaviors. To improve sustainability in the smart city applications, the time delay in the prediction of vacant channels is reduced which results in making these applications become more energy efficient. The effectiveness of the proposed work is illustrated with mathematical analysis and simulation results confirm its better sustainable performance compared to the existing techniques.

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle&rsquo, s size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurrence such as accidents or roads sliding and can contain much more heavy elements including pictures, audio or videos. Security, architecture and communication solutions for BSM use cases have been largely documented on in the literature contrary to EDM due to several concerns such as the variant size of EDM, the appropriate architecture along with latency, privacy and security. In this paper, we propose a secure and blockchain based EDM protocol for 5G enabled vehicular edge computing. To offer scalability and latency for the proposed scenario, we adopt a 5G cellular architecture due to its projected features compared to 4G tong-term evaluation (LTE) for vehicular communications. We consider edge computing to provide local processing of EDM that can improve the response time of public agencies (ambulances or rescue teams) that may intervene to the scene. We make use of lightweight multi-receiver signcryption scheme without pairing that offers low time consuming operations, security, privacy and access control. EDM records need to be kept into a distributed system which can guarantee reliability and auditability of EDM. To achieve this, we construct a private blockchain based on the edge nodes to store EDM records. The performance analysis of the proposed protocol confirms its efficiency.

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

In the age of the Internet of Things, connected devices are changing the delivery system in the healthcare communication environment. With the integration of IoT in healthcare, there is a huge potential for improvement of the quality, safety, and efficiency of health care in addition to promising technological, economical, and social prospects. Nevertheless, this integration comes with security risks such as data breach that might be caused by credential-stealing malware. In addition, the patient valuable data can be disclosed when the perspective devices are compromised since they are connected to the internet. Hence, security has become an essential part of today&rsquo, s computing world regarding the ubiquitous nature of the IoT entities in general and IoT-based healthcare in particular. In this paper, research on the algorithm for anonymizing sensitive information about health data set exchanged in the IoT environment using a wireless communication system has been presented. To preserve the security and privacy, during the data session from the users interacting online, the algorithm defines records that cannot be revealed by providing protection to user&rsquo, s privacy. Moreover, the proposed algorithm includes a secure encryption process that enables health data anonymity. Furthermore, we have provided an analysis using mathematical functions to valid the algorithm&rsquo, s anonymity function. The results show that the anonymization algorithm guarantees safety features for the considered IoT system applied in context of the healthcare communication systems.

Wireless sensor networks suffer from false report injection attacks. This results in energy drain over sensor nodes on the event traversal route. Novel en-route filtering schemes counter this problem by filtering these attacks on designated verification nodes. However, these filtering schemes among other limitations inherently are network lifetime inefficient. Generally, report traversal paths and verification nodes are also fixed. In this paper, we cater these limitations in our proposed scheme. Simulation experiments results show that proposed schemes outperforms existing en-route filtering schemes in networks lifetime. We employed a Fuzzy Logic System to select forwarding nodes from candidate nodes based on current network conditions. Proposed scheme gains in network lifetime, and energy-efficiency while having comparable false report filtering efficiency.

Traffic violations such as moving while the traffic lights are red have come from a simple omission to a premeditated act. The traffic control center cannot timely monitor all the cameras installed on the roads to trace and pursue those traffic violators. Modern vehicles are equipped and controlled by several sensors in order to support monitoring and reporting those kind of behaviors which some time end up in severe causalities. However, such applications within the vehicle environment need to provide security guaranties. In this paper, we address the limitation of previous work and present a secure and privacy preserving protocol for traffic violation reporting system in vehicular cloud environment which enables the vehicles to report the traffic violators, thus the roadside clouds collect those information which can be used as evidence to pursue the traffic violators. Particularly, we provide the unlinkability security property within the proposed protocol which also offers lightweight computational overhead compared to previous protocol. We consider the concept of conditional privacy preserving authentication without pairing operations to provide security and privacy for the reporting vehicles.

Intelligent transportation systems seek to maximize the usage of data generated by the vehicles for road users assistance. Combining vehicular ad hoc network with cloud computing makes it easy for the fixed infrastructures along the roads to collect and analyze safety messages for the benefits of the road users and the transportation authorities. However, security features need to be fully assessed before the adoption of such applications. In this paper, we present a secure vehicle traffic data dissemination and analysis in vehicular cloud computing in which the identity privacy of the vehicles and their generated message is achieved through pseudonym techniques. We use anonymous credential to guarantee the authorization of service demanding vehicles. ID-based signature is applied to assure the authentication of the vehicles for given pseudonyms. The efficient verification of signature on the message and revoked vehicles are achieved through batch verification technique and pseudonymous revocation list, respectively. Traffic estimation methods are applied to mine coarse-grained data collected by road side infrastructures. The merits of the proposed protocol compared to existing ones are shown through extensive simulations.

The Third Generation Partnership Project (3GPP) is currently studying the extension of the Cellular Internet of Thing (CIoT) towards the newly defined 5G architecture. While Machine Type Communication (MTC) was defined before only for the Evolved Packet System (EPS), new functionality is defined already in 5G Phase 1 that could be utilized also for CIoT in 5G, which officially is part of 3GPP 5G Phase 2. This paper introduces the new features in the 3GPP 5G architecture as well as the protocol enhancements and latest developments on the Machine to Machine Communication (M2M) platform with CIoT to satisfy the network and communication requirement. Based on a traffic accident data collection scenario within the automotive industry under consideration of Vehicle to Everything (V2X) communication, we discuss how an M2M platform can be deployed to fulfill the ultimate objectives of 5G cellular IoT. We consider oneM2M, a global initiative that aims at providing a unique architecture for M2M interoperability as a special form of M2M/IoT platform. We describe how oneM2M platform can be adopted for the traffic accident use case to provide interfaces that facilitate the communication between the vehicle-mounted devices, the V2X Control Function and V2X application server.

The Internet of things(IoT) is predicted to connect millions of devices. Therefore, standardized IoT technologies play a crucial role connecting massive IoT devices with interoperability. Various Standards Development Organizations (SDO) are working towards a horizontal solution that would fit several vertical platforms including oneM2M global IoT service layer standards initiative. Blockchain is a distributed ledger technology which is being applied in diverse applications such as crypto currency and smart contract. In this demo, we develop a blockchain-enabled IoT service layer platform based on oneM2M IoT standards and an blockchain hybrid application. We use a blockchain system named Logchain that is suitable for IoT due to its consensus algorithm. The hybrid application and oneM2M optional attribute added to the IoT platform enables the IoT users to either store their data in a conventional database or distributed ledger database.

In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities’ authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol.

We present an innovative approach for a Cybersecurity Solution based on the Intrusion Detection System to detect malicious activity targeting the Distributed Network Protocol (DNP3) layers in the Supervisory Control and Data Acquisition (SCADA) systems. As Information and Communication Technology is connected to the grid, it is subjected to both physical and cyber-attacks because of the interaction between industrial control systems and the outside Internet environment using IoT technology. Often, cyber-attacks lead to multiple risks that affect infrastructure and business continuity, furthermore, in some cases, human beings are also affected. Because of the traditional peculiarities of process systems, such as insecure real-time protocols, end-to-end general-purpose ICT security mechanisms are not able to fully secure communication in SCADA systems. In this paper, we present a novel method based on the DNP3 vulnerability assessment and attack model in different layers, with feature selection using Machine Learning from parsed DNP3 protocol with additional data including malware samples. Moreover, we developed a cyber-attack algorithm that included a classification and visualization process. Finally, the results of the experimental implementation show that our proposed Cybersecurity Solution based on IDS was able to detect attacks in real time in an IoT-based Smart Grid communication environment.

The integration of cloud computing into vehicular ad hoc networks (VANETs) has allowed the possibility of offering multiple services to the vehicle owners ranging from safety to entertainment services. The ubiquitousness of cloud computing involves a multitude of service providers; thus, an efficient and secure access control mechanism is required. However, rigorous security features need to be addressed for such commercial-based services to be fully adopted. In this paper, we present a secure billing protocol over attribute-based encryption in vehicular cloud computing. We achieve the identity privacy of the vehicles and their requested services through pseudonym techniques. Certificateless signature scheme is used to assure the authentication of legitimate vehicles which can enjoy the provided services. We use attribute-based encryption to guarantee access control based on the purchased services. We make use of hash chain technique to provide authorization through electronic voucher (credits) which a vehicle has to possess in order to purchase a service. Unlike existing protocols in VANETS, the proposed protocol is not built on expensive bilinear pairing operations. We provide the efficiency of the proposed protocol through performance analysis and simulation.

One of the main goals of Intelligent Transportation System (ITS) is to provide safety for both the drivers and passengers of vehicles. Collecting and analyzing road traffic data seem to be the first step towards safety related application in ITS. However security issues while collecting and transmitting traffic data need to be properly handled before the full adoption of those systems. Vehicular Cloud (VC) which combines vehicle ad hoc networks (VANETs) and cloud computing (CC) presents a promising paradigm for ITS. In this paper, we present a secure traffic data transmission protocol for vehicular cloud. We make use of ID-based signature and anonymous credential techniques to allow registered vehicles to anonymously transmit the traffic data within the vehicle’s sensors.