Showing papers by "Mohsen Guizani published in 2020"

A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact

Abstract: The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Abstract: The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Reliable Federated Learning for Mobile Networks

Abstract: Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, for example, the data poisoning attack, or unintentionally, for example, low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.

A Distributed Deep Learning System for Web Attack Detection on Edge Devices

Abstract: With the development of Internet of Things (IoT) and cloud technologies, numerous IoT devices and sensors transmit huge amounts of data to cloud data centers for further processing. While providing us considerable convenience, cloud-based computing and storage also bring us many security problems, such as the abuse of information collection and concentrated web servers in the cloud. Traditional intrusion detection systems and web application firewalls are becoming incompatible with the new network environment, and related systems with machine learning or deep learning are emerging. However, cloud-IoT systems increase attacks against web servers, since data centralization carries a more attractive reward. In this article, based on distributed deep learning, we propose a web attack detection system that takes advantage of analyzing URLs. The system is designed to detect web attacks and is deployed on edge devices. The cloud handles the above challenges in the paradigm of the Edge of Things. Multiple concurrent deep models are used to enhance the stability of the system and the convenience in updating. We implemented experiments on the system with two concurrent deep models and compared the system with existing systems by using several datasets. The experimental results with 99.410% in accuracy, 98.91% in true positive rate (TPR), and 99.55% in detection rate of normal requests (DRN) demonstrate the system is competitive in detecting web attacks.

Blockchain-Assisted Secure Device Authentication for Cross-Domain Industrial IoT

Abstract: Industrial Internet of Things (IIoT) is considered as one of the most promising revolutionary technologies to prompt smart manufacturing and increase productivity. With manufacturing being more complicated and sophisticated, an entire manufacturing process usually involves several different administrative IoT domains (e.g., factories). Devices from different domains collaborate on the same task, which raises great security and privacy concerns about device-to-device communications. Existing authentication approaches may result in heavy key management overhead or rely on a trusted third party. Thus, security and privacy issues during communication remain unsolved but imperative. In this paper, we present an efficient blockchain-assisted secure device authentication mechanism $\textsf{BASA}$ for cross-domain IIoT. Specifically, consortium blockchain is introduced to construct trust among different domains. Identity-based signature (IBS) is exploited during the authentication process. To preserve the privacy of devices, we design an identity management mechanism, which can realize that devices being authenticated remain anonymous. Besides, session keys between two parties are negotiated, which can secure the subsequent communications. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed mechanism.

A Blockchain-SDN-Enabled Internet of Vehicles Environment for Fog Computing and 5G Networks

Abstract: The goal of intelligent transport systems (ITSs) is to enhance the network performance of vehicular ad hoc networks (VANETs). Even though it presents new opportunities to the Internet of Vehicles (IoV) environment, there are some security concerns including the need to establish trust among the connected peers. The fifth-generation (5G) communication system, which provides reliable and low-latency communication services, is seen as the technology to cater for the challenges in VANETs. The incorporation of software-defined networks (SDNs) also ensures an effective network management. However, there should be monitoring and reporting services provided in the IoV. Blockchain, which has decentralization, transparency, and immutability as some of its properties, is designed to ensure trust in networking platforms. In that regard, this article analyzes the combination of blockchain and SDN for the effective operation of the VANET systems in 5G and fog computing paradigms. With managerial responsibilities shared between the blockchain and the SDN, it helps to relieve the pressure off the controller due to the ubiquitous processing that occurs. A trust-based model that curbs malicious activities in the network is also presented. The simulation results substantially guarantee an efficient network performance, while also ensuring that there is trust among the entities.

A Disaster Management-Oriented Path Planning for Mobile Anchor Node-Based Localization in Wireless Sensor Networks

Abstract: The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

SecAuthUAV: A Novel Authentication Scheme for UAV-Ground Station and UAV-UAV Communication

Abstract: Unmanned Aerial Vehicles (UAVs) are becoming very popular nowadays due to the emergence of application areas such as the Internet of Drones (IoD). They are finding wide applicability in areas ranging from package delivery systems to automated military applications. Nevertheless, communication security between a UAV and its ground station (GS) is critical for completing its task without leaking sensitive information either to the adversaries or to unauthenticated users. UAVs are especially vulnerable to physical capture and node tampering attacks. Further, since UAV devices are generally equipped with small batteries and limited memory storage, lightweight security techniques are best suited for them. Addressing these issues, a lightweight mutual authentication scheme based on Physical Unclonable Functions (PUFs) for UAV-GS authentication is presented in this paper. The UAV-GS authentication scheme is extended further to support UAV-UAV authentication. We present a formal security analysis as well as old-fashioned cryptanalysis and show that our protocol provides various security features such as mutual authentication, user anonymity, etc, and is resilient against many security attacks such as masquerade, replay, node tampering, and cloning attacks, etc. We also compare the performance of our protocol with state-of-the-art authentication protocols for UAVs, based on computation, communication, and memory storage cost.

DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach

Abstract: With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.

Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles

Abstract: Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a lightweight and real-time traffic light detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic lights, and a lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic light detection module can achieve $ ± 1 . 5 m errors at stop lines when working with other self-driving modules.

A High-Availability Data Collection Scheme based on Multi-AUVs for Underwater Sensor Networks

Abstract: In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is predefined. The nodes near the trajectory of an AUV directly send their data to the AUV while the others transmit data to nodes that are closer to the trajectory. Malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the proposed HAMA method increases the packet delivery ratio and the network lifetime.

Lightweight Mutual Authentication Protocol for V2G Using Physical Unclonable Function

Abstract: Electric vehicles (EVs) have been slowly replacing conventional fuel based vehicles since the last decade. EVs are not only environment-friendly but when used in conjunction with a smart grid, also open up new possibilities and a Vehicle-Smart Grid ecosystem, commonly called V2G can be achieved. This would not only encourage people to switch to environment-friendly EVs or Plug-in Hybrid Electric Vehicles (PHEVs), but also positively aid in load management on the power grid, and present new economic benefits to all the entities involved in such an ecosystem. Nonetheless, privacy and security remain a serious concern of smart grids. The devices used in V2G are tiny, inexpensive, and resource constrained, which renders them susceptible to multiple attacks. Any protocol designed for V2G systems must be secure, lightweight, and must protect the privacy of the vehicle owner. Since EVs and charging stations are generally not guarded by people, physical security is also a must. To tackle these issues, we propose Physical Unclonable Functions (PUF) based Secure User Key-Exchange Authentication (SUKA) protocol for V2G systems. The proposed protocol uses PUFs to achieve a two-step mutual authentication between an EV and the Grid Server. It is lightweight, secure, and privacy preserving. Simulations show that the proposed protocol performs better and provides more security features than state-of-the-art V2G authentication protocols. The security of the proposed protocol is shown using a formal security model and analysis.

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

Abstract: The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as the network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G network can provide an excellent platform for autonomous vehicles because SDN offers open programmability and flexibility for new services incorporation. This separation of control and data planes enables centralized and efficient management of resources in a very optimized and secure manner by having a global overview of the whole network. The SDN also provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. In addition, it promises the overall improved performance. In this paper, we propose a flow-based policy framework on the basis of two tiers virtualization for vehicular networks using SDNs. The vehicle to vehicle (V2V) communication is quite possible with wireless virtualization where different radio resources are allocated to V2V communications based on the flow classification, i.e., safety-related flow or non-safety flows, and the controller is responsible for managing the overall vehicular environment and V2X communications. The motivation behind this study is to implement a machine learning-enabled architecture to cater the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently. This paper also presents a proof of concept for leveraging machine learning-enabled resource classification and management through experimental evaluation of special-purpose testbed established in custom mininet setup. Furthermore, the results have been evaluated using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). While concluding the paper, it is shown that the LSTM has outperformed the rest of classification techniques with promising results.

A Survey on Intent-Driven Networks

Abstract: Software defined network and network function visualization enhance the network flexibility and management agility, which increase network fragility and complexity. However, the vast majority of network parameters are manually configured, which makes the configuration failures still inevitable. Future networks should be self-configuring, self-managing, and self-optimizing. Intent-driven network (IDN) is a self-driving network that uses decoupling network control logic and closed-loop orchestration techniques to automate application intents. At present, a unified definition of IDN has not yet been presented, and the research background and current status of IDN are not clear. Considering the emerging applications and research of IDN, in this article, we survey existing technologies, clarify definitions, and summarize features for IDN. Specifically, we discuss the basic architecture and key technologies of IDN. In addition, diversity gains and challenges are analyzed briefly. Finally, some future work is highlighted and wider applications of IDN are provided for further research.

Multi-Access Edge Computing: A Survey

Abstract: Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks’ architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

Blockchain-Based Incentives for Secure and Collaborative Data Sharing in Multiple Clouds

Abstract: The prosperity of cloud computing has driven an increasing number of enterprises and organizations to store their data on private or public cloud platforms. Due to the limitation of individual data owners in terms of data volume and diversity, data sharing over different cloud platforms would enable third parties to take advantage of big data analysis techniques to provide value-added services, such as providing healthcare services for customers by gathering medical data from multiple hospitals. However, it remains a challenging task to design effective incentives that encourage secure and collaborative data sharing in multiple clouds. In this paper, we propose a reliable collaboration model consisting of three types of participants, which include data owners, miners, and third parties, where the data is shared via blockchain and recorded by a smart contract. In general, these participants may acquire and store the sharing of data using their private or public clouds. We analyze the topological relationships between the participants and develop some Shapley value models from simple to complicate in the process of revenue distribution. We also discuss the incentive effect of sharing security data and rationality of the designed solution through analysis towards distribution rules.

A Distributed Framework for Energy Trading Between UAVs and Charging Stations for Critical Applications

Abstract: Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions.

A Node Location Algorithm Based on Node Movement Prediction in Underwater Acoustic Sensor Networks

Abstract: Aiming at the problems of the low mobility, low location accuracy, high communication overhead, and high energy consumption of sensor nodes in underwater acoustic sensor networks, the MPL (movement prediction location) algorithm is proposed in this article. The algorithm is divided into two stages: mobile prediction and node location. In the node location phase, a TOA (time of arrival)-based ranging strategy is first proposed to reduce communication overhead and energy consumption. Then, after dimension reduction processing, the grey wolf optimizer (GWO) is used to find the optimal location of the secondary nodes with low location accuracy. Finally, the node location is obtained and the node movement prediction stage is entered. In coastal areas, the tidal phenomenon is the main factor leading to node movement; thus, a more practical node movement model is constructed by combining the tidal model with node stress. Therefore, in the movement prediction stage, the velocity and position of each time point in the prediction window are predicted according to the node movement model, and underwater location is then completed. Finally, the proposed MPL algorithm is simulated and analyzed; the simulation results show that the proposed MPL algorithm has higher localization performance compared with the LSLS, SLMP, and GA-SLMP algorithms. Additionally, the proposed MPL algorithm not only effectively reduces the network communication overhead and energy consumption, but also improves the network location coverage and node location accuracy.

A Distributed Mobile Fog Computing Scheme for Mobile Delay-Sensitive Applications in SDN-Enabled Vehicular Networks

Abstract: With the rapid development of intelligent transportation systems, enormous amounts of delay-sensitive vehicular services have been emerging and challenge both the architectures and protocols of vehicular networks. However, existing cloud computing-embedded vehicular networks cannot guarantee timely data processing or service access, due to long propagation delay and traffic congestion at the cloud center. Meanwhile, the current distributed network architecture does not support scalable network management, leading the intelligent data computing policies to be undeployable. With this motivation, we propose to introduce fog computing into vehicular networks and define the Multiple Time-constrained Vehicular applications Scheduling (MTVS) issue. First, to improve the network flexibility and controllability, we introduce a Fog-based Base Station (FBS) and propose a Software-Defined Networking (SDN)-enabled architecture dividing the networks into network, fog, and control layers. To address MTVS issue, instead of normal centralized computing-based approaches, we propose to distribute mobile delay-sensitive task in data-level over multiple FBSs. In particular, we regard the fog layer of SDN-enabled network as an FBS-based network and propose to distribute the computing task based on the FBSs along multiple paths in the fog layer. By Linear Programming, we optimize the optimal data distribution/transmission model by formulating the delay computation model. Then, we propose a hybrid scheduling algorithm including both local scheduling and fog scheduling, which can be deployed on the proposed SDN-enabled vehicular networks. Simulation results demonstrate that our approach performs better than some recent research outcomes, especially in the success rate for addressing MTVS issue.

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

Abstract: Vehicular Ad-hoc Network (VANET) is a modern era of dynamic information distribution among societies. VANET provides an extensive diversity of applications in various domains, such as Intelligent Transport System (ITS) and other road safety applications. VANET supports direct communications between vehicles and infrastructure. These direct communications cause bandwidth problems, high power consumption, and other similar issues. To overcome these challenges, clustering methods have been proposed to limit the communication of vehicles with the infrastructure. In clustering, vehicles are grouped together to formulate a cluster based on certain rules. Every cluster consists of a limited number of vehicles/nodes and a cluster head (CH). However, the significant challenge for clustering is to preserve the stability of clusters. Furthermore, a secure mechanism is required to recognize malicious and compromised nodes to overcome the risk of invalid information sharing. In the proposed approach, we address these challenges using components of trust. A trust-based clustering mechanism allows clusters to determine a trustworthy CH. The novel features incorporated in the proposed algorithm includes trust-based CH selection that comprises of knowledge, reputation, and experience of a node. Also, a backup head is determined by analyzing the trust of every node in a cluster. The major significance of using trust in clustering is the identification of malicious and compromised nodes. The recognition of these nodes helps to eliminate the risk of invalid information. We have also evaluated the proposed mechanism with the existing approaches and the results illustrate that the mechanism is able to provide security and improve the stability by increasing the lifetime of CHs and by decreasing the computation overhead of the CH re-selection. The StabTrust also successfully identifies malicious and compromised vehicles and provides robust security against several potential attacks.

A Survey of Blockchain Enabled Cyber-Physical Systems

Abstract: Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. It has the capability to revolutionize a diverse set of business applications, ranging from sharing economy to data management and prediction markets. In this paper, we present a holistic survey of various applications of CPS where blockchain has been utilized. Smart grids, health-care systems, and industrial production processes are some of the many applications that can benefit from the blockchain technology and will be discussed in the paper.

Blockchain-Enhanced High-Confidence Energy Sharing in Internet of Electric Vehicles

Abstract: To introduce the opportunities brought by plug-in hybrid electric vehicles (PHEVs) to the energy Internet, we propose a local vehicle-to-vehicle (V2V) energy trading architecture based on fog computing in social hotspots and model the social welfare maximization (SWM) problem to balance the interests of both charging and discharging PHEVs. Considering transaction security and privacy protection issues, we employ a consortium blockchain in our designed energy trading architecture, which is different from the traditional centralized power systems, to reduce the reliance on trusted third parties. Moreover, we improve the practical Byzantine fault tolerance (PBFT) algorithm and introduce it into a consensus algorithm, called the delegated proof of stake (DPOS) algorithm, to design a more efficient and promising consensus algorithm, called DPOSP, which greatly reduces resource consumption and enhances consensus efficiency. To encourage PHEVs to participate in V2V energy transactions, we design an energy iterative bidirectional auction (EIDA) mechanism to resolve the SWM problem and obtain optimal charging and discharging decisions and energy pricing. Finally, we conduct extensive simulations to verify the proposed DPOSP algorithm and provide numerical results for a comparison with the performance of the genetic algorithm and the Lagrange algorithm in achieving EIDA.

DAGIoV: A Framework for Vehicle to Vehicle Communication Using Directed Acyclic Graph and Game Theory

Abstract: Data sharing and content offloading among vehicles is an imperative part of the Internet of Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly promising solution for fast communication among vehicles. To ensure security and data tracking, existing studies use blockchain as a solution. The Blockchain-enabled Internet of Vehicles (BIoV) requires high computation power for the miners to mine the blocks and let the chain grow. Over and above, the blockchain consensus is probabilistic and the block generated today can be eventually declared as a fork and can be pruned from the chain. This reduces the overall efficiency of the protocol because the correct work done initially is eventually not used if it becomes a fork. To address these challenges, in this paper, we propose a Directed Acyclic Graph enabled IoV (DAGIoV) framework. We make use of a tangle data structure where each node acts as a miner and eventually the network achieves consensus among the nodes. A game-theoretic approach is used to model the interactions between the vehicles providing and consuming offloading services. The proposed model is proven to be highly scalable and well suited for microtransactions or frequent data transfer among the nodes in the vehicular network.

Ant-Colony-Based Complete-Coverage Path-Planning Algorithm for Underwater Gliders in Ocean Areas With Thermoclines

Abstract: Underwater gliders are being increasingly used for data collection, and the development of methods for optimizing their routes has become a topic of active research. With this aim in mind, in this paper, a complete-coverage path-planning obstacle-avoidance (CCPP-OA) algorithm that ensures avoidance for underwater gliders in sea areas with thermoclines is proposed. First, the entire sea area with the thermocline layer is stratified based on the underwater communication radii of the gliders. Next, the glide angles and initial navigation points of the gliders are determined based on their communication radii at each level to construct the complete-coverage path. Finally, by combining the ant colony algorithm and the determined initial navigation points, the complete-coverage path with obstacle avoidance is planned for the gliders. Simulation results show that the proposed CCPP-OA algorithm enables complete coverage of the entire sea area. Furthermore, the length of the planned path is shorter and the amount of energy consumed is less than that of other algorithms.

A Lightweight Authentication and Attestation Scheme for In-Transit Vehicles in IoV Scenario

Abstract: With the rise of new technological paradigms such as the Internet of Things (IoT) and the Internet of Vehicles (IoV), we are going to see an unprecedented growth of connected vehicles on the roads. Also, with the ever-increasing complexity of vehicular electronics and with the increasing number of Electronic Control Units (ECUs) inside these next-generation vehicles, the need for verification of the firmware and software running on these ECUs using attestation techniques is heightened all the more. In this paper, we propose a lightweight and secure authentication and attestation scheme for attesting vehicles while they are on the roads. Since this attestation is proposed to be carried out on moving vehicles, there is also a need for authenticating the vehicles with the Road Side Units (RSUs) first before carrying out attestation. Therefore, a combined attestation and authentication scheme for verification of the vehicle ECU firmware is presented here. The ECU firmware running on the vehicles can be attested from the edge servers connected to the RSUs while the vehicles are in-transit and passing through these RSUs. We perform a security analysis of the proposed attestation and authentication protocol and compare it with other similar existing protocols. We also do a performance analysis of the proposed protocol and show the feasibility of its deployment.