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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.

In Ad Hoc networks, route failure may occur due to less received power, mobility, congestion and node failures. Many approaches have been proposed in literature to solve this problem, where a node predicts pre-emptively the route failure that occurs with the less received power. However, these approaches encounter some difficulties, especially in scenario without mobility where route failures may arise. In this paper, we propose an improvement of AOMDV protocol called LO-PPAOMDV (Link Quality and MAC-Overhead aware Predictive Preemptive AOMDV). This protocol is based on new metric combine two routing metrics (Link Quality, MAC Overhead) between each node and one hop neighbor. Also we propose a cross-layer networking mechanism to distinguish between both situations, failures due to congestion or mobility, and consequently avoiding unnecessary route repair process. The LO-PPAOMDV was implemented using NS-2. The simulation results show that our approach improves the overall performance of the network. It reduces the average end to end delay, the routing overhead, and increases the throughput and packet delivery fraction of the network.

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.

Mobile ad-hoc network is a collection of dynamically organized nodes where each node acts as a host and router. Mobile ad-hoc networks are characterized by the lack of preexisting infrastructures or centralized administration. So, they are vulnerable to several types of attacks, especially the Blackhole attack. This attack is one of the most serious attacks in this kind of mobile networks. In this type of attack, the malicious node sends a false answer indicating that it has the shortest path to the destination node by increasing the sequence number and decreasing the number of hops. This will have a significant negative impact on source nodes which send their data packets through the malicious node to the destination. This malicious node drops received data packets and absorbs all network traffic. In order overcome this problem, securing routing protocols become a very important requirement in mobile ad-hoc networks. Multi-path routing protocols are among the protocols affected by the Blackhole attack. In this paper, we propose an effective and efficient technique that avoids misbehavior of Blackhole nodes and facilitates the discovery for the most reliable paths for the secure transmission of data packets between communicating nodes in the well known Ad hoc On-demand multi-path routing protocol (AOMDV). Our proposed technique is implemented and simulated using the well known ns 2.35 simulator. We also compared the performance of the three routing protocols AOMDV, AOMDV under Blackhole attack (BHAOMDV) and the proposed solution to counter the Blackhole attack (IDSAOMDV). The results show the degradation of performance of AOMDV under attack, it also present similarities between normal AOMDV and the proposed solution by isolating misbehaving node which has resulted in increase the performance metrics to the standard values of the AOMDV protocol.