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Internet of Things (IoT) finds its applications in home, city and industrial settings. Current network is in transition to adopt fog/edge architecture for providing the capacity for IoT. However, in order to deal with the enormous amount of traffic generated by IoT devices and to reduce queuing delay, novel self-learning network management algorithms are required at fog/edge nodes. Active Queue Management (AQM) is a known intelligent packet dropping techique for differential QoS. In this paper, we propose a new AQM scheme based on Deep Reinforcement Learning (DRL) technique and introduce scaling factor in our reward function to achieve the trade-off between queuing delay and throughput. We choose Deep Q-Network (DQN) as a baseline for our scheme, and compare our approach with various AQM schemes by deploying them at the interface of fog/edge node. We simulated them by configuring different bandwidth and round trip time (RTT) values. The simulation results show that our scheme outperforms other AQM schemes in terms of delay and jitter while maintaining above-average throughput, and also verifies that AQM based on DRL is efficient in managing congestion.

In this paper, a collaboration scheme between a high-altitude platform (HAP) and several unmanned aerial vehicles (UAVs) for wireless communication networks is investigated. The main objective of this study is to maximize the total downlink throughput of the ground users by optimizing the UAVs’ three-dimensional (3D) placements and user associations. An optimization problem is formulated and a separate genetic-algorithm-based approach is proposed to solve the problem. The K-means algorithm is also utilized to find the initial UAV placement to reduce the convergence time of the proposed genetic-algorithm-based allocation. The performance of the proposed algorithm is analyzed in terms of convergence time, complexity, and fairness. Finally, the simulation results show that the proposed HAP-UAV integrated network achieves a higher total throughput through joint user association and UAV placement schemes compared to a scheme with a single HAP serving all users.

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear non-convex optimisation problem. The authors first transform the energy-efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees ε-optimal solution. A proof of convergence is also given for the ε-optimal algorithm. The proposed ε-optimal algorithm provide a practical solution for power allocation in energy-efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.

We study the energy-efficient power allocation techniques for OFDM-based cognitive radio (CR) networks, where a CR transmitter is communicating with CR receivers on a channel borrowed from licensed primary users (PUs). Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the cognitive secondary user (SU) cause mutual-interference. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. Our goal is to allocate subcarrier power for the SU so that the energy efficiency metric is optimized as well as the mutual-interference on all the active PU bands are below specified bounds. We show that the green power loading problem is a fractional programming problem. We use Charnes-Cooper transformation technique to obtain an equivalent concave optimization problem for what the solution can be readily obtained. We also propose iterative Dinkelbach method using parametric objective function for the fractional program. Numerical results are given to show the effect of different interference parameters, rate and power thresholds, and number of PUs.

Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to-device (D2D) communication. In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented. An illustration of the research in cache development for wireless networks that apply intelligent and learning techniques in a specific domain in their design is presented. We summarize the challenges that face the design of caching system in MENs. Finally, some future research directions are discussed for the development of cache placement and cache access and delivery in MENs.

Cooperative diversity techniques have received a lot of attention recently due to their ability to provide spatial diversity in fading wireless environment without the requirement of implementing multiple antenna on the same device. It increases link reliability, provides higher capacity, reduces transmit power, and extends transmission range for the same level of performance and modulation rate. In this paper, we study the achievable gain of cooperative communications from a wireless cross-layer point of view in multi hop networks. We propose two routing algorithms applicable for wireless ad hoc networks. First, we propose an edge node based on a greedy cooperative routing (ENBGCR) algorithm, where we modify the geographic routing algorithm to incorporate the cooperative transmission and extend the coverage range of the nodes. The main objective of ENBGCR algorithm is to minimize the number of hops that messages transverse to reach their destination. Then the energy-efficient cooperative routing algorithm is proposed to minimize the end-to-end total transmission power subject to end-to-end target data rate. Simulation results for both algorithms show that the proposed strategies have great improvement in terms of delay and power saving respectively for the same quality of service requirement as compared to traditional algorithms.

We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with an objective of maximising energy-efficiency is a constraint non-linear non-convex fractional programming problem. The optimisation needs to satisfy the primary users interference constraints and secondary users outage constraints. The authors present the optimal power allocation in DF relaying by transforming the constraint non-linear non-convex fractional power allocation problem into a concave fractional programme by using Charnes–Cooper transformation. The authors also present an iterative algorithm that uses parametric transformation and guarantees ε-optimal convergence. The convergence of the iterative algorithm is proved and numerical results obtained for cooperative cognitive radio network are presented with different network parameter settings.

The explosion of connected devices and data transmission in the Internet of Things (IoT) era brings substantial burden on the capability of cloud computing. Moreover, these IoT devices are mostly positioned at the edge of a network and limited in resources. To address these challenges, edge cloud-distributed computing networks emerge. Because of the distributed nature of edge cloud networks, many research works considering software defined networks (SDNs) and network–function–virtualization (NFV) could be key enablers for managing, orchestrating, and load balancing resources. This article provides a comprehensive survey of these emerging technologies, focusing on SDN controllers, orchestration, and the function of artificial intelligence (AI) in enhancing the capabilities of controllers within the edge cloud computing networks. More specifically, we present an extensive survey on the research proposals on the integration of SDN controllers and orchestration with the edge cloud networks. We further introduce a holistic overview of SDN-enabled edge cloud networks and an inclusive summary of edge cloud use cases and their key challenges. Finally, we address some challenges and potential research directions for further exploration in this vital research area.

The network-centric applied research team (N-CART) is continuing its work on an ambitious project known as the network-enabled powered wheelchair adaptor kit (NEPWAK). It introduces techniques for modifying and using powered wheelchairs as mobile platforms enabling communication and remote control. The wheelchair is equipped with a laptop computer, a CCD camera and a wireless network interface card (NIC) for 802.11b Internet access. The laptop acts as a server allowing network clients to gain access through a custom control interface on the chair. The remote controlling client receives a video and audio feed from the chair and sends control signals for maneuvering. While traveling, the chair is able to change its network association from one access point (AP) to another within the same subnet-the process is known as handoff. However, there is no inter-network handoff mechanism presently available in IP networks. This restricts the mobility of the wheelchair to within the coverage area of the subnet APs. This paper shows that the Internet engineering task force’s (IETF) network layer mobility protocol—Mobile IP suffers from large handoff latencies that can hinder communication between the client and the wheelchair during handoff. Mobile IP alone is not a sufficient solution for a mobile telebotic system such as NEPWAK. An interesting solution to the handoff latency problem comes from the Fast-handover protocol described in Section 4.4 with simulation results in Section 6.2.