Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services. During ...the last decade, there has been a growing focus of research in the UAV's assistance paradigm as a fundamental concept resulting in the constant improvement between different kinds of ground networks and the hovering UAVs in the sky. Recently, the wide availability of embedded wireless interfaces in the communicating entities has allowed the deployment of such a paradigm simpler and easiest. Moreover, due to UAVs' controlled mobility and adjustable altitudes, they can be considered as the most appropriate candidate to enhance the performance and overcome the restrictions of ground networks. This comprehensive survey both studies and summarizes the existing UAV-assisted research, such as routing, data gathering, cellular communications, Internet of Things (IoT) networks, and disaster management that supports existing enabling technologies. Descriptions, classifications, and comparative studies related to different UAV-assisted proposals are presented throughout the paper. By pointing out numerous future challenges, it is expected to simulate research in this emerging and hot research area. To the best of our knowledge, there are many survey papers on the topic from a technology perspective. Nevertheless, this survey can be considered as the first attempt at a comprehensive analysis of different types of existing UAV-assisted networks and describes the state-of-the-art in UAV-assisted research.
Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly ...benefit IoT applications. Different from traditional applications, IoT applications, such as environmental monitoring, smart navigation, and smart healthcare come with new requirements, such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.
The TCP/IP stack plays an important role in terms of data transmission, traffic control and address assignment in the Internet of Vehicles (IoV), which has seen phenomenal growth in recent years. ...However, with the increasing technical requirements and daily demand in IoV, the drawbacks of traditional TCP/IP protocols, e.g., weak scalability in large networks, low efficiency in the dense environment and unreliable addressing in high mobility circumstance, become non-trivial especially in vehicular environments. Fortunately, the emerging Named Data Networking (NDN) technology provides a good choice to address the above issues in vehicular environments. Specifically, the introduced content store module in NDN which caches the sent/received contents, can greatly improve the networking performance by suppressing redundancy as well as enriching diversity. With aforementioned motivations, we thoroughly studied previous works on Vehicular Named Data Networking (VNDN) with emphasis on content caching, and then demonstrate the feasibility and necessity for employing NDN in vehicular environments with the help of content caching. Subsequently, we further strengthened the importance of cache selection and replacement strategies in VNDN framework, which is positioned to meet the challenges of data transmission efficiency and resource consumption by leveraging in-network vehicular caching. After that, we engaged in an in-depth survey on the existing cache selection and replacement schemes in VNDN, with their applicability compared. Next, further challenges during caching design were elaborately analyzed considering the specific characteristics of VNDN. Finally, we highlighted the potential research directions which may shine lights on the promising efforts to improve the performance of VNDN content caching.
Social-Internet of Things (S-IoT) fortifies the relationship between computing entities for efficient utilization of resources. S-IoT accentuates on building convivial circles between the devices, ...which sanction sharing of information without compromising own performance. Trust and privacy are the two factors responsible for the maintenance of these social circles. Trust establishes faith between the entities and privacy supports abstraction of device information. There exist a plethora of approaches, which fixate on trust and privacy in social networks. However, the subsisting solutions rely on a trust scoring system and utilize a single centralized server for these calculations. Such scoring systems are manipulable by eavesdroppers, which engender erroneous reputation leading to high trust values. Considering the desideratum of an efficient trust and privacy-preserving solution, this paper proposes a novel solution in the form of fission computing. The proposed approach relies on the edge-crowd integration for maintenance of trust and preservation of privacy rules in S-IoT. The proposed solution uses crowdsources as mini-edge servers and entropy modeling for defining trust between the entities. Fission managers are responsible for maintaining the privacy rules, which operate for every S-IoT application. The proposed approach is analyzed through numerical simulations by using a safe network as a performance benchmark. Further, the article presents a case study on the detection of fake news sources in S-IoT as an application of the proposed approach.
•Novel architecture for S-IoT by using edge-crowdsourcing.•Formation of a light-weight distributed query system by using “Fission Computing”.•Low complex solutions for trust relaying and privacy preservation by using entropy modeling.•Simulative and theoretical validation for trust and privacy preservation.•Application case study for detecting false news sources in S-IoT.
Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile ...data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.
An intelligent transportation system (ITS) is a cyber physical system (CPS) that is well recognized due to its distinctive applications such as passenger safety, enhanced traffic efficiency, and ...infotainment services. However, very recently, these CPSs have witnessed a rapid increase in the number of autonomous vehicles (AVs), and the count is expected to increase significantly in the years to come. Under such scenarios, the conventional ITS solutions would fall short due to numerous technical limitations such as reduced flexibility, poor connectivity, limited scalability, and lack of adequate intelligence. In this direction, the cloud computing paradigm would also lag behind in meeting the following expected challenges: high mobility, minimal latency, realtime services, and high quality of service (QoS). Software defined networking (SDN) and edge computing (EC) are expected to be promising solutions for modern ITS. SDN allows efficient management of networks by virtue of its decoupled data and control planes. Moreover, EC is an extension of cloud computing that allows data processing, storage, and management at the edge of the network. Thus, these promising paradigms can address the network and data processing challenges of AV networks in rea time. Further, distributed mobility management (DMM) with improvised routing capabilities in the considered setup can bring added advantages in the form of zero tunneling overhead, full-fledged control of flow table entries, QoS-driven routing decisions, and efficient interplay between the edge and the cloud. Keeping in view the potential benefits of SDN, EC, and DMM in future AV networks, this work presents a composite framework with a distributed SDN-DMM approach in ITS ecosystems. Further, conventional DMM has been improved by stacking the Optimized Routing Decision module on it. This coupling yields a mobility-aware and QoS-driven (MobQoS) SDN framework for AVs that handles both the mobility and QoS challenges of the underlying vehicular networks. Additionally, it is also designed to handle the interplay between the edge and the cloud in an optimized manner using the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Numerical comparisons depict that the proposed MobQoS outperforms the existing state of the art in terms of overall communication latency and bandwidth utilization. The performance assessment has been carried out using NS-3 and SUMO wherein an overall improvement of 15.95 percent has been reported in terms of end-to-end delay.
Due to their promising applications and intriguing characteristics, Unmanned Aerial Vehicles (UAVs) can be dispatched as flying base stations to serve multiple energy-constrained Internet-of-Things ...(IoT) sensors. Moreover, to ensure fresh data collection while providing sustainable energy support to a large set of IoT devices, a required number of UAVs should be deployed to carry out these two tasks efficiently and promptly. Indeed, the data collection requires that UAVs first make Wireless Energy Transfer (WET) to supply IoT devices with the necessary energy in the downlink. Then, IoT devices perform Wireless Information Transmission (WIT) to UAVs in the uplink based on the harvested energy. However, it turns out that when the same UAV performs WIT and WET, its energy usage and the data collection time are severely penalized. Worse yet, it is difficult to efficiently coordinate between UAVs to improve the performance in terms of WET and WIT. This work proposes to divide UAVs into two teams to behave as data collectors and energy transmitters, respectively. A Multi-Agent Deep Reinforcement Learning (MADRL) method, called TEAM, is leveraged to jointly optimize both teams' trajectories, minimize the expected Age of Information (AoI), maximize the throughput of IoT devices, minimize the energy utilization of UAVs, and enhance the energy transfer. Simulation results depict that TEAM can effectively synchronize UAV teams and adapt their trajectories while serving a large-scale dynamic IoT environment.
Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication. Clustering can solve the issues of vehicular ad hoc network (VANET); however, ...due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either cluster head or cluster member duration. Moreover, the absence of the intelligent use of mobility parameters, such as direction, movement, position, and velocity, results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a new robust and dynamic mobility-based clustering algorithm junction-based clustering for VANET (JCV) is proposed in this paper. In contrast to previous studies, transmission range, moving direction of the vehicle at the next junction, and vehicle density are considered in the creation of a cluster, whereas relative position, movement at the junction, degree of a node, and time spent on the road are considered to select the cluster head. The performance of JCV is compared with two existing VANET clustering algorithms in terms of the average cluster head duration, the average cluster member duration, the average number of cluster head change, and the percentage of vehicles participating in the clustering process. The simulation result shows JCV outperforms the existing algorithms and achieved better stability.
Owing to the explosive expansion of wireless communication and networking technologies, cost-effective unmanned aerial vehicles (UAVs) have recently emerged and soon they will occupy the major part ...of our sky. UAVs can be exploited to efficiently accomplish complex missions when cooperatively organized as an ad hoc network, thus creating the well-known flying ad hoc networks (FANETs). The establishment of such networks is not feasible without deploying an efficient networking model allowing a reliable exchange of information between UAVs. FANET inherits common features and characteristics from mobile ad hoc networks (MANETs) and their sub-classes, such as vehicular ad hoc networks (VANETs) and wireless sensor networks (WSNs). Unfortunately, UAVs are often deployed in the sky adopting a mobility model dictated by the nature of missions that they are expected to handle, and therefore, differentiate themselves from any traditional networks. Moreover, several flying constraints and the highly dynamic topology of FANETs make the design of routing protocols a complicated task. In this paper, a comprehensive survey is presented covering the architecture, the constraints, the mobility models, the routing techniques, and the simulation tools dedicated to FANETs. A classification, descriptions, and comparative studies of an important number of existing routing protocols dedicated to FANETs are detailed. Furthermore, the paper depicts future challenge perspectives, helping scientific researchers to discover some themes that have been addressed only ostensibly in the literature and need more investigation. The novelty of this survey is its uniqueness to provide a complete analysis of the major FANET routing protocols and to critically compare them according to different constraints based on crucial parameters, thus better presenting the state of the art of this specific area of research.
Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based ...on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem in the context of using CAN bus for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, this paper aims to concentrate on the application of data analysis and statistical learning techniques. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques under consideration in this paper outperform other techniques that have been used previously.
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