In recent years, the rapid progress of Artificial Intelligence (AI) coupled with the rapid connectivity offered by the Internet of Things (IoT) has paved the way for a transformative framework that holds immense potential for the implementation of novel and groundbreaking ideas, and to provide innovative solutions to enhance the daily life of people, especially in healthcare domain. However, patient overcrowding remains as a glaring issue that plagues hospitals, especially during seasonal diseases such as influenza, leading to extended waiting periods for numerous patients, or dropping out of the queue by others. Therefore, FedRx, a new privacy-preserving Federated Distillation (FD) approach, is proposed in this paper. Such approach relies on the Internet of Vehicles (IoV), where each vehicle acts as a form of Mobile Edge Computing (MEC) to sense disease and generate digital medical prescriptions to avoid overcrowding in hospitals and pharmacies, while also reducing network burdens, improving sustainability, and enhancing the quality-of-life factors for people in smart cities.

With the expansion of Internet-of-Things (IoT), security of smart devices is becoming major or primary concern in today’s era. The increasing demand of consumer electronics due its recent evolution, the personal information that is shared is becoming valuable. In addition, the next generation of Industrial Internet of Things (IIoT) devices include features such as low cost, automation, intelligence provision, reduced overhead, efficiency, and remote interactions while communicating or transmitting information among themselves. There are very few authors who have focused on next gen IIoT while improving the efficiency along with providing the security among devices in the network. Therefore, we have proposed a hybrid trusted model by integrating objective model and fuzzy evaluation matrix method to ensure a secure and efficient transmission method among devices in the network. The proposed mechanism is simulated and experimented over various parameters such as detection ratio and network-related performance and functional tests compared to state-of-art solutions.

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection.

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model’s accuracy, under UAV constraints of energy consumption, communication quality, and local datasets’ heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

Industrial Internet of Things (IIoT) has emerged as a new paradigm in the era of smart systems where interactions and transmission of messages among various entities in an industrial ecosystem are done autonomously. This includes manufacturing of products, shipment process, storage of records, and counting the data, to name a few. However, the involvement of multiple cyber threats that accompany the smart devices may lead the organizations on a heavy risk of profits. Even though the number of cybersecurity approaches have been proposed to ensure a secure and efficient communication mechanism in IIoT systems, the identification of cybersecurity issues in IIoT applications while exchanging the data is still at its early stage. The aim of this article is to propose an efficient cybersecurity communication mechanism using the trust evaluation system of each device to make a correct decision during communication. In addition, the evaluated trust of each device is further analyzed and monitored through a blockchain-based mechanism. The proposed approach is validated against an existing scheme in terms of various security metrics, such as sensitivity of devices while communicating, convergence time of transmitting information and percentage, and probability of malicious devices in terms of data delivery rate, false authentication, and trust value computation.

The Internet of Vehicles (IoV) can deliver services for intelligent transportation systems. However, it typically relies on the exchange of sensitive information, including passwords and personal data, which makes it vulnerable to many security risks, such as phishing attacks, which have largely increased in the last decade. Cryptography-based security measures look as a form of protection to preserve sensitive information, but they can be bypassed by inside attackers and, in addition, they increase the burden on the network. To tackle the aforementioned issues, in this paper, we present an approach to control phishing attacks in the IoV environment, based on Machine Learning (ML) and Deep Learning (DL) techniques.

With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracing methods have become crucial to protect people. However, conventional mechanisms, for instance based on the manual search of close contacts, lack of high efficiency due to the high time consumption. The research community is therefore exploring new ways to track contagious diseases by exploiting modern communications paradigms and technologies. In this paper, we propose a new contact tracing method based on Online Social Network (OSN) platforms. In our design, contact detection occurs in real-time by means of traditional proximity approaches. Then, a likely future contact forecast is notified through OSN communities.

Named Data Networking (NDN) is attracting increasing attention from researchers and companies due to its characteristics and its promised results as a better alternative to the current TCP/IP Internet. Among these features are the use of names instead of addresses, and the use of caches in the nodes. Both have proved to be an excellent addition to network functionality that allow receiving information from a nearby location while relieving the pressure on the main servers. Yet, caches are still limited compared to the huge amount of data consumed. Most research has focused on finding and caching the most relevant data, to retrieve it in the future from the nearest point. Most research agrees that the data that needs to be stored is the one that is constantly requested by many consumers, and this theory has been generally effective in most research works. However, high data consumption levels are not always considered important, especially in academic or corporate environment. This is particularly true whenever the consumption of data associated to the institution’s own servers is very low compared to the other data, such as entertainment videos and private messages from social networking sites. Hence, the data that is stored and delivered in a short time is not essential for these institutions. Also, the servers that are discharged from the pressure are not affiliated with these institutions either. The existence of these cases proved by our study on real consumption data belonging to the Amar Telidji University of Laghouat in Algeria, where we found through simulations that only 4% of the overall traffic is associated with data belonging to the university itself. In this paper, we propose a new placement strategy named NBCC (Naive Bayes Classifier for Caching). The NBCC is used to cache the imported data by classifying the received content using a Multinomial Naive Bayes classifier that can classify the received data using only their names. The strategy is shown to be effective and provides the best results compared to other state-of-art strategies.

Unmanned Aerial Vehicles (UAVs) cloud have attracted wide attentions from both industrial and academic communities as a new paradigm offering flexible services. This new concept utilizes the recent technologies of mobile cloud computing. The collaboration between these technologies represents a fantastic vision of the future, where everything is connected to the Internet, thereby offering intelligent services and facilitating decision-making between cooperative UAVs. In this work, we study the UAVs clouds computing services selection strategies. Our objectives are to (1) provide a detailed review of the existing services' selection methods and to (2) propose a new Game Theoretic approach for Selection of Services in UAV Clouds (GTSS-U C) aiming to enable normal users to select the most suitable UAV-Service-Provider. Given that every service’ provider is characterized by specific features, limitations, and prices, a user must select the most suitable provider. The technique of selection is based on the Game Theory (GT) method and takes into account the user requirements and the UAVs provider qualities to find the most adequate provider. The results of the simulation conducted using NS-2 simulator advocate for the efficiency of our method in both network Quality-of-Service and Service gain.

In a jiffy after the outbreak of the 2019 novel coron-avirus, also called COVID-19 or SARS-CoV-2, the World Health Organization (WHO) considered it as a pandemic that threatens the demise of humanity. This quick decision was in conjunction with a real situation of biological inability to find a vaccine that can eliminate the virus or at least limit its spread. For that reason, technological intervention and cooperation are needed more than ever to face this pandemic. In this same context, we propose a novel system that deploys Vehicle-to-everything (V2X) technology and Blockchain in collaboration to face such a pandemic. Our proposal is centered on a triple-stage processing i) zone identification and classification based on vehicles' thermal cameras detection, ii) Blockchain-based information storage for enhanced patient medical information privacy, and iii) drones-based zone neutralization processes. Simulation results show that, thanks to the use of Blockchain technology, the network-related performance remain almost unchanged and hence, all Intelligent Transportation System (ITS) including inter-vehicles and inter-drones functionalities are not affected. In addition the additional overhead is very acceptable and does not exceed the 5 Kb in the worst case.

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.

The deployment of a swarm of cooperative UAVs applications for the execution of distributed tasks has increased attention from both academia and industry researchers. The use of a group of UAVs instead of one single UAV offers many advantages like extending the mission coverage, providing a reliable ad-hoc networks services, and enhancing the service performance, to name a few. However, due to the highly dynamic nature of the swarm topology, the coordination of a large number of UAVs poses new challenges to traditional inter-UAV communication protocols. Therefore, there is a need for the design of new networking protocols that can efficiently support the fast-pace and real-time requirements of a coordinated swarm navigation in various environments. In this paper, we propose SEMRP a Swarm energy-efficient multicast routing protocol for UAVs flying in group formations. The main purpose of SEMRP is to facilitate the control and information delivery between UAVs while minimizing inter-UAV packet loss, packet re-transmission, and end-to-end delay. In this study we show how SEMRP achieves these objectives by taking into account various Quality-of-Service parameters like the network throughput, the UAVs mobility, and energy efficiency to ensure a timely and accurate information delivery to all members of a UAV swarm. The results of the conducted simulation using NS-2 advocate for the efficiency of our proposal through its to two presented versions (SEMRP-v1 and SEMRP-v2) in term of reducing the total emission energy (at least by 10 dBm), optimizing the End-to-End Delay by 44%, and increasing the packet delivery ratio by more than to 22% compared to SP-GMRF protocol.

Vehicular Ad-hoc NETworks (VANET) have revolutionised the intelligent transportation systems. Indeed, they enable vehicles to communicate with each other and with the infrastructure, and they facilitate several vital applications in real-time. Several trust models have been proposed to ensure a secured VANET and the authenticity, integrity and reliability of information exchanged in the network. The concept of trust models is to introduce and implement trust explicitly in the vehicular nodes. In this paper, we propose a lightweight evaluation methodology to evaluate trust models, specifically designed for VANET. Our study focused on the three categories of VANET trust models (TMs): Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Combined Trust Models (CTM). Our simulations evaluate the efficiency of the trust models and compare them against several trust related parameters: false positive rate, precision and accuracy level in detecting malicious nodes. Since the scope of this research work is to evaluate the performance of TMs under adversary conditions, we have considered on-off attacks which can act as man-in-the-middle to drop and delay transmitted packets.

Named Data Networking (NDN) is a promising candidate for future internet architecture. It is one of the implementations of the Information-Centric Networking (ICN) architectures where the focus is on the data rather than the owner of the data. While the data security is assured by definition, these networks are susceptible of various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). IFAs overwhelm an NDN router with a huge amount of interests (Data requests). Various solutions have been proposed in the literature to mitigate IFAs; however; these solutions do not make a difference between intentional and unintentional misbehavior due to the network congestion. In this paper, we propose a novel congestion-aware IFA detection and mitigation solution. We performed extensive simulations and the results clearly depict the efficiency of our proposal in detecting truly occurring IFA attacks.

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

Measuring vehicles density and distribution in urban environments is an important task. The results of such estimation are highly required for different applications such as road lights configuration, congestion control, and also inter-vehicle data routing. This task, which is known as crowd sensing, is mostly based on smartphone-assisted sensing. However, in urban environments the multiple kinds of obstacles make it hard and mostly inaccurate especially for RoadSide Units (RSUs) low density cases. Furthermore, the assumption that all vehicles are collaborative and honest can lead to unexpected and unwanted situations. To address the above mentioned problems, we propose in this paper a trust-aware crowd sensing technique based on Unmanned Aerial Vehicle (UAV) for vehicular urban environments. Considering the real traffic information and the distribution of dishonest nodes in the network gathered by UAVs, our proposed solution provides a global view to both vehicles and RSUs, which can be used for different applications such as: finding the shortest and most trusted possible path to messages' final destinations, and also for the intelligent congestion control. Our simulation results show that our solution offers instant crowd and trust information over which in addition to the high detection ratios, also high packet delivery ratios with low network overhead are achieved.

Ensuring the required trustiness among communicating peers is an important task in Vehicular Adhoc Networks (VANETs), especially for safety-related applications where the margin of error is extremely undesired. Most the safety applications are a kind of decision aided system, and final decision is always taken by humans. Thus, in addition to securing inter-vehicle communication, the human factor must be also considered. With the appearance of 5G technology it became possible to connect VANET to any other network including Online Social Networks (OSNs). In this paper, we took advantage of this possibility to connect VANET and OSN, for the purpose of estimating the drivers honesty based on their OSN profiles. Afterward, we combined both inter-vehicle and OSN-based trust to compute the overall trust about the different vehicles and their drivers. Conducted simulation show that our proposal offers more than 5% detection ratio than the classical inter-vehicle solution. Furthermore, it also reduced the detection error ratio by about 3% with a reduced standard deviation for both detection and error ratios.

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.

This paper introduces the concept of a new service for social Internet of Vehicles (IoV)-based clouds called incentives-based vehicle witnesses as a service (IVWaaS), which employs vehicles moving on the road as the witnesses to designated events. Specifically, we focus on two key enablers, a new secure and privacy preserving service framework as well as a new incentive mechanism to promote the wide adoption of the aforementioned social service. In IVWaaS, when confronted any events, the vehicles in the vicinity with mounted cameras collaborate with other roadside cameras to take pictures of the site of interest around them, and send the pictures to the cloud infrastructure anonymously so that the privacy of the vehicles can be preserved. To stimulate active participation from the users, we also introduce a new privacy-aware incentives mechanism called privacy-aware proportionate receipt collection, in which the contributors are credited according to their contribution to the service and can claim their incentives in a privacy-aware fashion. Service providers can also use the stored pictures as “on-demand picture service.” Other law enforcement agencies can obtain the stored pictorial information and use it as forensics in the investigations.

Considered as a primordial component of Cooperative Intelligent Transportation Systems (C-ITS), Vehicular Ad Hoc Network (VANET) plays a weighty role to facilitate different on-road applications, most of which primary rely on multi hop communications. To ensure reliability and security of such communication, it is very pertinent to guarantee that the most proper and trustworthy vehicles are selected as relays. This paper takes up this challenge by introducing a weighted probabilistic and trust-aware strategy called WeiSTARS, to ensure high delivery ratios within reduced delays. Simulation results conducted using NS2 tool depicted that our technique enhances the delivery ratio by more than 8% with around 30% less delay compared to both GytAR and GPSR routing protocols even in the presence of high ratios of dishonest vehicles.

Dishonesty detection in mobile and wireless networks is a task that typically relies on watchdog techniques. However, these medium overhearing-based techniques are prone to cause a high energy consumption. In an attempt to address this problem, several proposals adopted trust management as an alternative solution that is able to overcome the shortcomings of cryptography-based solutions when facing inside attacks. Unfortunately, these trust-based solutions remain mostly unable to reduce energy consumption. In this paper we propose a distributed time division-based monitoring strategy to achieve the required security levels while optimizing the energy consumption. Our proposal accounts for both trust and link duration among honest peers to fairly divide the monitoring period, and takes advantage of the periodically exchanged hello messages to make this solution fully distributed. Simulations results evidence the energy efficiency achieved by our proposal, especially for high density scenarios (>120 nodes) where the consumption becomes stable and does not increase with the number of nodes, while ensuring high detection ratios of malicious nodes (>85%).

Cooperative intelligent transportation systems (CITS), mainly represented by Vehicular Ad Hoc Networks (VANETs), were developed to enhance safety on roads before being generalized to support other comfort and efficiency applications. Most VANET applications, including safety ones, are based on multi-hop communications. Hence, a certain trustworthiness should exist among vehicles to ensure a reliable and trusted communication excluding dishonest peers from all network operations. In this paper we propose an hierarchical trust establishment solution able to cope with VANET applications and their requirements. Our solution is based on a three-level architecture, which enables it to adapt to the communication scenario and the required security level. Simulation results conducted done NS2 tool evidence that our solution is able to reach almost optimal attacker detection ratios (more than 90%) even in the presence of a significant number of attackers in the network (25%) while reducing the wrong relay decision ratios.

Abstract Trust establishment over vehicular networks can enhance the security against probable insider attackers. Regrettably, existing solutions assume that the attackers have always a dishonest behavior that remains stable over time. This assumption may be misleading, as the attacker can behave intelligently to avoid being detected. In this paper, we propose a novel solution that combines trust establishment and a risk estimation concerning behavior changes. Our proposal, called risk‐aware trust‐based architecture, evaluates the trust among vehicles for independent time periods, while the risk estimation computes the behavior variation between smaller, consecutive time periods in order to prevent risks like an intelligent attacker attempting to bypass the security measures deployed. In addition, our proposal works over a collaborative multi‐hop broadcast communication technique for both vehicle‐to‐vehicle and vehicle‐to‐roadside unit messages in order to ensure an efficient dissemination of both safety and infotainment messages. Simulation results evidence the high efficiency of risk‐aware trust‐based architecture at enhancing the detection ratios by more than 7% compared with existing solutions, such as T‐CLAIDS and AECFV, even in the presence of high ratios of attackers, while offering short end‐to‐end delays and low packet loss ratios. Copyright © 2016 John Wiley & Sons, Ltd.

Delivering data through the most reliable and trusted path is essential for any kind of network. Moreover, in highly mobile and dynamic networks such as VANETs, the problem is more complex since every node requires, at least, a previous knowledge about its own neighborhood to select the most adequate path. In addition, the open communication medium causes other problems that any routing protocol must manage, without forgetting the VANETs' sensitivity to delay. In this paper we propose a trust-based routing protocol for vehicular urban environments called TROUVE. The proposed protocol aims at finding the shortest and most trusted path to destination taking into account the real traffic information and the distribution of dishonest nodes in the network. In this study, simulation results show the effectiveness of our protocol in terms of data delivery and end-to-end delay. We also show that, even in the presence of a high number of misbehaving nodes, our protocol offers equally good results.

Trust management is one of the major issues for secure communication in vehicular network. Most of the existing trust models are identity-based and use excessive periodic exchange between vehicles to build a decision about trustiness of participating vehicles. Malicious data can be minimized in such models using identities reputation. In general, the nature and the quality of the data are not taken into account, despite some models which may revoke messages based on their nature and their type. In this work, we propose a new trust model for VANETs, which based on the early detection of attacks by relying on the opinion of the last forwarder and delayed verification of the exchanged messages. In our scheme, we developed an intrusion detection module which perform such operation and evaluate the trustiness of received messages. We introduce a new concept of companion vehicles which is used to filter out and select the most trusted nodes among neighboring vehicles, to be used as relays in the forwarding procedure. The deployment of this mechanism allow us to prevent vehicles identified as probable dishonest nodes from participating in the network. In this study, we simulation results show the effectiveness of our trust model in detecting dishonest nodes as well as malicious messages that are sent by honest or dishonest nodes, after a very low number of messages exchange. We also show that in the worst case scenario, our trust model offers equally good results.