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We propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajectory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy management.

Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution.Index Terms-VANET, Stability of communication path, SVRMr. Laroui is corresponding author.Various routing protocols that can be used in wireless networks are proposed in the literature [8]. VANET-based solutions are expected to furnish methodical and proven solutions for innovative, and resource-efficient as in other wireless communication protocol [9]. These protocols can broadly be classified into three main categories: the first category is the proactive routing protocols, which aim to construct the routing tables before the request is made. A proactive routing protocol identifies the topology of the network at all times, for example, Destination Sequenced Distance Vector routing (DSDV). The second category is the reactive routing protocols, that consist of building a routing table only when a node receives a request. Protocols under this umbrella do not know the network topology; they determine the path to access a node of the network due to the demand of request, for example, Ad hoc On-Demand Distance Vector (AODV). Finally, the last category is the hybrid routing protocols. A hybrid protocol combines the two previously discussed categories: proactive and reactive concept. It uses a proactive protocol to get information about the nearest neighbors (maximum neighbors with two jumps). Beyond this predefined area, the hybrid protocol uses reactive protocol techniques to search for routes. This type of protocol adapts well to large networks.The main characteristics of VANET networks are the high mobility of vehicles, where each vehicle has a range of communication to provide communication directly with the destination vehicle if they are in the same range. Otherwise, a multi-hop communication needs to be established to allow communication with the destin...

Service offloading poses interesting challenges in current and next-generation networks. The classical network optimization algorithms are still painstakingly tune heuristics to get a sufficient solution. Classical approaches use data as input in order to output near-optimal solutions. These techniques show exponential computational time and deal only with small network scale. Therefore, we are motivated by replacing this tedious process with recent learning techniques to learn the behavior of the classical optimization algorithms while enhancing both the quality of service and satisfying the resources requirements of next-generation applications. Deep reinforcement learning (DRL) and machine learning (ML) can improve service offloading and network caching. An optimal service offloading in virtual mobile edge computing (SO-VMEC) use case algorithm is proposed using integer linear programming (ILP). Moreover, a service offloading protocol is presented to support the use case. We leverage software defined networking (SDN) and network function virtualization (NFV) concepts to control and virtualize network components. Then, a DRL-based offloading is proposed to deal with dense Internet of Things (IoT) networks. Extensive evaluations and comparison to state of the art techniques are carried out. Results show the efficiency of the proposed algorithms in terms of service offloading, resource utilization, and networking.

Edge Computing and Network Function Virtualization (NFV) concepts can improve network processing and multi-resources allocation when intelligent optimization algorithms are deployed. Multiservice offloading and allocation approaches pose interesting challenges in the current and nextgeneration vehicle networks. The state-of-the-art optimization approaches still formulate exact algorithms, and tune approximation methods to get sufficient solutions. These approaches are data-centric that aim to use heterogeneous data inputs to find the near optimal solutions. In the context of connected and autonomous vehicles (CAVs), these techniques show an exponential computational time and deal only with small and medium scale networks. Therefore, we are motivated by using recent Deep Reinforcement Learning (DRL) techniques to learn the behavior of exact optimization algorithms while enhancing the Quality of Service (QoS) of network operators and satisfying the requirements of the next-generation Autonomous Vehicles (AVs). DRL algorithms can improve AVs service offloading and optimize edge resources. An Optimal Virtual Edge Autopilot Placement (OVEAP) algorithm is proposed using Integer Linear Programming (ILP). Moreover, an autopilot placement protocol is presented to support the algorithm. Optimal allocation and Virtual Network Function (VNF) placement and chaining of the autopilot, based on several new constraints such as computing and networking loads, network edge infrastructure, and placement cost, are designed. Further, a DRL approach is formulated to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Extensive simulations and evaluations are carried out. Results show that the proposed allocation strategies outperform the state-of-the-art solutions and give better performance in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated autopilots.

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We propose to utilize rideshare taxis as infrastructure for both communication and computation. Rideshare overlays become hence Mobile Edge Nodes. End-users utilize near rideshare taxis as edge servers to receive video chunks for live video streaming. The set cover problem (SCP) is used to formulate the rideshare taxis coverage optimization inside the city. It provides the maximum number of rideshare taxis that cover end-users routes which guarantee the efficiency of communication services. Simulation results show that the proposed architecture dramatically enhances the quality of service and the overall communication performance in terms of execution time and energy consumption.

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