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Routing protocol plays a vital role in FANETs (Flying Ad-Hoc Networks) by facilitating communication between nodes and enabling the establishment of efficient routes. In a FANET, where the nodes can be drones, airplanes or other unmanned aerial vehicles, mobility is a key aspect that requires proper route management. Among the most well-known and widely used routing protocols in different types of networks is AODV (Ad hoc On-Demand Distance Vector). AODV is a reactive routing protocol specially designed for ad hoc networks, including FANET networks. It offers an efficient solution to establish dynamic routes between nodes when direct communication between a source node and the destination node is not possible. However, most of the studies available in the literature mainly focus on the comparison between different routing protocols. They often neglect to examine in detail the performance of the same routing protocol under different circumstances or conditions. This paper offers a thorough investigation into the influence of noise on the AODV protocol in FANET networks. Furthermore, we introduce a proposed enhancement to the AODV protocol, specifically designed to improve its ability to withstand the effects of noise. Additionally, we present simulation results that evaluate the performance of AODV in terms of throughput, End-to-End Delay, Packet Delivery Ratio (PDR), and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Achieving a fair usage of network resources is of vital importance in Slice‐ready 5G network. The dilemma of which network slice to accept or to reject is very challenging for the Infrastructure Provider (InfProv). On one hand, InfProv aims to maximize the network resources usage by accepting as many network slices as possible; on the other hand, the network resources are limited, and the network slice requirements regarding Quality of Service (QoS) need to be fulfilled. In this paper, we devise three admission control mechanisms based on Reinforcement Learning, namely, Q‐Learning, Deep Q‐Learning, and Regret Matching, which allow deriving admission control decisions (policy) to be applied by InfProv to admit or reject network slice requests. We evaluated the three algorithms using computer simulation, showing results on each mechanism's performance in terms of maximizing the InfProv revenue and their ability to learn offline or online. [ABSTRACT FROM AUTHOR]

Collision detection and avoidance between vehicles is one of the key services envisioned in the Internet of Vehicles. Such services are usually deployed at the multi-access edge computing (MEC) to ensure low-latency communication and thus guarantee real-time reactions to avoid collisions between vehicles. In order to maximize the coverage of the road and ensure that all vehicles are connected to an optimal MEC host (in terms of geographical location), the collision avoidance application needs to be instantiated on all the MEC hosts. This may add a burden on the computing resources available at the latter. In this article, we propose an AI-empowered framework that aims to optimize the computing resources at the MEC hosts. Our framework uses deep learning to predict the vehicle density to be served by a MEC host and derive the exact computing resources required by the collision detection application to run optimally. We evaluate the proposed framework using a real dataset representing vehicle mobility in a big city. Obtained results show the accuracy of our prediction model, and hence the efficiency of our resources assignment framework to exactly deduce the optimal computing resources needed by each instance of the application. [ABSTRACT FROM AUTHOR]

Vehicular cloud is a promising paradigm in which mobile vehicles can be both cloud users and service providers. It enables vehicles that have sufficient resources to act as mobile cloud servers by offering a wide range of services to user's vehicles. However, user vehicles need to discover provider vehicles along with their services, and request targeted services from them, especially with the high mobility of vehicles. Moreover, provider vehicles and their services are characterized by specific quality criteria and consumer vehicles need to select the most suitable services. In this paper, we design a new protocol that enables vehicles to discover and consume services of mobile cloud servers that are moving nearby. The protocol harnesses public buses, which provide a stability in terms of time (regular timetables) and space (fixed lines in an urban area), making them act as cloud directories, where provider vehicles can register their services. Public buses spread service registration information to the buses within the same bus line, and to buses of connected lines, as well. A consumer vehicle can discover the available services, along with their constraints, by querying the buses in their vicinity. To ensure service consumption, the buses provide also a routing protocol allowing communication between the provider and the consumer. Furthermore, we extend our protocol to enable, on the one hand, vehicle providers to select the most appropriate public bus for an efficient service registration, and users to select, on the other hand, the most satisfactory service, which satisfies both provider constraints and user preferences. Simulation results showed the efficiency of our protocol in terms of service discovery and service consuming delays, as well as its ability to select both the most suitable cloud directory and service provider for both providers and consumers, respectively. [ABSTRACT FROM AUTHOR]

Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of indoor parameters, enabling the improvement of the indoor occupants' thermal comfort. Numerical results show the efficiency of our prediction scheme, reaching an accuracy of 96%, as well as our genetic-based scheme in optimizing the indoor thermal parameters by 85%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is revolutionising our everyday life by connecting a massive number of smart objects. IoT is considered a key enabler of multiple emerging applications in several areas such as manufacturing, agriculture, healthcare, automotive, smart city, etc. However, as it becomes increasingly prevalent in our life, IoT brings a number of challenging new security risks. The first issue is related to device authentication, especially with the massive number of connected devices with different types (sensors, vehicles, drones, etc.). This issue can be exploited by attackers to break into IoT networks. In addition, IoT devices generate a huge amount of confidential data that must be processed and transmitted independently without human intervention, which cannot be performed by traditional centralized security systems. Besides, the isolation imposed between different networks and the number of messages used for device's association are considered the main limitations in the two recent works. To overcome these limits, we design a novel decentralized scheme leveraging a hybridization of two main types of blockchain, local and public, which ensures authentication of things before establishing communication and exchanging information between devices. Our solution removes the isolation between different networks, contributes in reducing the number of messages required for the association phase, and solves the time delay problem imposed by the PoW consensus mechanism that is used in public blockchain as well. We adopt the Ethereum blockchain in its two versions local and public to implement our solution, and evaluate its performance as compared to the existing literature. The obtained results proved its effectiveness in terms of execution time and energy consumption which are the same metrics used in the most recent works. [ABSTRACT FROM AUTHOR]

5G opens the door not only for new network services but also for a new business model involving new business actors. Particularly, the introduction of Network Slicing encourages the appearance of new stakeholders, namely Verticals or network slice tenants, Network Slice Providers, and Resource Providers. The Network Slice Provider sells end‐to‐end network slices (virtual end‐to‐end mobile network) to the Vertical, while leasing virtual and physical resources from Resource Providers to deploy and instantiate these end‐to‐end network slices. Consequently, a trusted relationship among these actors should be established and maintained. To fill this gap, in this paper, we propose a Blockchain‐based trust architecture that aims to: (1) create and negotiate the deployment of an end‐to‐end slice network; (2) manage automatically the service level agreements (SLA) established between the involved actors; while guaranteeing security and anonymous transactions. [ABSTRACT FROM AUTHOR]

Finding parking spots in urban areas poses significant challenges due to the ever increasing complexities of the transportation endeavor, resulting in issues like traffic jams, higher fuel consumption, and environmental pollution. In this regard, this paper introduces an innovative Cooperative parking search strategy that relies on agent decision-making and Vehicle-to-Everything (V2X) communication. This approach allows vehicles to share real-time information regarding their parking intentions and available spots, enabling agents (representing vehicles) to collectively make informed decisions and optimize the parking search process. The proposed strategy aims to enhance the efficiency of parking searches by facilitating real-time information exchange through V2X communication. To demonstrate its effectiveness, we conducted a proof-of-concept implementation using OMNeT++ simulator and SUMO traffic generator to simulate real-world conditions in Laghouat City. The simulation considers a variety of parameters, including recommended parking distance, traffic congestion, and parking space availability. Numerical results show that agent cooperation can reduce the overall parking search time for all drivers by up to 10%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$10\%$$\end{document} compared to alternative methods, including greedy and selfish approaches. [ABSTRACT FROM AUTHOR]