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This paper considers an Ultra-Dense Heterogeneous Network (UDHN) consisting of cache-enabled Unmanned Aerial Vehicles (UAVs) and Internet of things Mobile Devices (IMDs) receiving their requested contents via the Power Domain Non-Orthogonal Multiple Access (PD-NOMA) protocol. Employing the Fast Global K-Means (FGKM) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as non-legitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is non-concave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the Interior-Point Method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional Most Popular caching (MPC) strategy substantially.

This paper considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet of Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the three-dimensional (3D) UAV placements, transmit power, and cloud resources. This represents a challenging, non-convex and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems namely, two-dimensional (2D) UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global K-means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.

This article addresses a real-time monitoring mechanism of vital signs of patients in a three-tier Internet of Medical Things-based software-defined-wireless body area network. The challenging issues are energy efficiency, interference, delay, emergency conditions, and reliability. We propose a two-tier scheduling algorithm in which Walsh Hadamard codes are employed to avoid the interference and decrease the delay and energy consumption in tier I. To schedule the transmission of different patients in tier II [i.e., the transmissions between hubs and access points (APs)], we propose a fair nonorthogonal multiple access-based scheduling algorithm, which jointly considers the channel state, energy consumption, and delay. Some processing tasks of the proposed algorithm are executed by local edge servers connected to APs and managed by a central SD-controller in tier III. Consequently, the transmission delay and energy consumption considerably decrease and the effective throughput increases. The algorithm takes the precedence of some information over other sensed data into account by employing the emergency index. The simulations results illustrate the advantages of the proposed algorithm in terms of energy consumption, network delay, and effective throughput, and the superior performance over other benchmark schemes.

This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.

The realization of mobile edge computing (MEC) over emerging fifth (5G) generation of wireless systems arises as a driving-force in the future of cloud computing. In order to cope with the volume, variety, and velocity of the IoT traffic while making optimal use of the network infrastructure, a synergistic exploitation of MEC and 5G should be put forward to support advanced resource management applications. In this paper, we propose the use of joint parallelism between wireless and cloud domains to efficiently respond to mobile data deluge. We review the literature, discuss the enabling network architecture, potentials, challenges, and open issues related to the realization of such level of parallelism. We present and evaluate two design examples — parallel computation offload method (PCOM) and parallel transmission and storage method (PTSM)—which outline the benefits of parallelism for computation-hungry and storage-hungry applications, respectively. Results of our optimization formulation show that PCOM and PTSM are able to make an efficient use of the network resources and support a heavy instantaneous workload by means of the parallelism.

Emerging 5G systems will feature a closer collaboration between mobile network operators and cloud service providers to meet the communication and computational requirements of modern mobile applications and services in a mobile cloud computing (MCC) environment. In this article, we show how the marriage between heterogeneous wireless networks (HetNets) and multiple clouds (a collection of which is referred to here as Intercloud) stands out as an effective response for the mobile data deluge. First, we review the building blocks of a HetNet and an Intercloud as well as the resource management entities in both domains. Second, we outline how they might be orchestrated to better support the task offloading process. Third, we identify the key design criteria and challenges related to interoperation between an Intercloud and a HetNet. We then formulate a revenue sharing approach for a coalition between a mobile network operator and cloud service providers. The approach achieves the maximum revenue for the coalition by optimally associating the users to the clouds through the base stations. Next, the concept of Shapley value is applied to individualize the contribution of each player based on the maximum revenue for the optimal user association. Numerical results illustrate the benefits of the coalition for all players.

In this paper, we apply the cross-entropy optimization (CEO) to the problem of joint multiple relay assignment and source/relay power allocation in green cooperative cognitive radio (GCCR) networks. We use shared-band amplify-and-forward relaying for cooperative communication in this problem. The proposed joint multiple relay assignment and source/relay power allocation jointly performs relay assignment and power allocation in GCCR while optimizing two conflicting objectives: The first one is to maximize the total rate, and the second one is to minimize the greenhouse gas emissions in GCCR networks. This multiobjective optimization problem is a nonconvex combinatorial optimization problem and is NP-hard. We use a Monte-Carlo-based CEO algorithm to solve this nonconvex problem. The CEO has a simplistic model, and its robustness in avoiding local minima/maxima makes it a suitable candidate for solving complex combinatorial optimization problems. We present simulation results that verify the effectiveness of the proposed CEO method for joint multiple relay assignment and source/relay power allocation.

In the past decade, cognitive radio and cooperative communication techniques have been proposed in the literature for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with cooperative communication can further improve the future wireless network performance. Efficient resource allocation in cooperative cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this article, a survey of resource allocation in cooperative CRN is presented. We discuss the taxonomy of objectives and protocols used in the literature for resource allocation in cooperative CRN. This paper also highlights the use of power control, cooperation types, network configurations and decision types used in cooperative CRN. Finally, directions for future research are outlined.

The beamforming antenna technology is a promising solution to many challenges facing wireless ad hoc networks. Beamforming antennas have the ability to increase the spatial reuse, improve the transmission reliability, extend the transmission range and/or save the power consumption. If they are effectively used, they can significantly improve the network capacity, lifetime, connectivity and security. However, traditional Medium Access Control (MAC) protocols fail to exploit the potential benefits due to the unique characteristics of wireless ad hoc networks with beamforming antennas. To that end, numerous MAC protocols have been designed over the years to harness the offered potential. In this paper, we survey the literature on MAC protocols proposed for wireless ad hoc networks with beamforming antennas during the last decade. We discuss the main beamforming-related challenges facing the medium access control in ad hoc networks. We present taxonomy of the MAC protocols proposed in the literature based on their mode of operation and the mechanisms used to address the challenges. In addition, we provide a qualitative comparison of the protocols highlighting their features, benefits and requirements. Finally, we provide directions for possible future work.

Utilizing smart beamforming antennas in contention-based wireless mesh networks has proven to provide a significant increase in the network capacity. Nevertheless, their effectiveness in providing additional QoS guarantees is still under-explored. In this paper, we develop a framework for analyzing conflicts on wireless links considering both the physical interference and the operation of the underlying directional MAC protocol. Based on a novel taxonomy, we classify the link conflicts into several categories and propose a novel colored conflict graph abstraction to model the network interference pattern in the presence of contention-based directional MAC protocols. Using the proposed colored conflict graph, we derive a closed form expression for the probability of successful transmission over a wireless link given that all the bandwidth requirements are satisfied. Based on our analysis, we formulate the bandwidth-guaranteed routing problem as an optimization problem. Since the problem is NP-hard, we present a heuristic algorithm for joint routing and admission control to find single-path bandwidth-guaranteed routes. Using extensive simulations, we demonstrate the accuracy of our conflict analysis and the ability of the proposed algorithm to provide QoS guarantees along with efficient channel utilization.

Beamforming antennas promise a significant increase in the spatial reuse of the wireless medium when deployed in multihop wireless networks. However, existing directional Medium Access Control (MAC) protocols with the default binary exponential backoff mechanism are not capable of fully exploiting the offered potential. In this paper, we discuss various issues involved in the design of MAC protocols specific for beamforming antennas. Based on our discussion, we argue that the traditional binary exponential backoff mechanism limits the possible spatial reuse and aggravates some beamforming-related problems such as deafness and head-of-line blocking. To grasp the transmission opportunities offered by beamforming antennas, we design an Opportunistic Directional MAC (OPDMAC) protocol for multihop wireless networks. The OPDMAC protocol employs a novel backoff mechanism in which the node is not forced to undergo idle backoff after a transmission failure but can rather take the opportunity of transmitting other outstanding packets in other directions. This mechanism minimizes the idle waiting time and increases the channel utilization significantly and thereby enables OPDMAC to enhance the spatial reusability of the wireless medium and reduce the impact of the deafness problem without additional overhead. Through extensive simulations, we demonstrate that OPDMAC enhances the performance in terms of throughput, delay, packet delivery ratio, and fairness. To further improve its performance, we discuss and evaluate the benefits of carefully choosing some protocol parameters instead of using the default values commonly used by other directional MAC protocols.

Handover performance is very important when evaluating IP mobility protocols. If not performed efficiently, handover delays, jitters, and packet loss directly impact application performance. We propose a new architecture for providing efficient handover, while being able to coexist with other protocols. We propose a paradigm for multicast-based micromobility (M&M), where a visiting mobile is assigned a multicast address to use while moving within a domain. The multicast address is obtained using algorithmic mapping, and handover is achieved using multicast join/prune mechanisms. This paper outlines a framework for the design and evaluation of micromobility protocols. We define a suite of protocols (called candidate access router set) to enable multiple-access routers to receive traffic for the mobile node. By changing the number of such routers, timing, and buffering parameters, the protocol may be fine-tuned for specific technologies (e.g., 802.11) and handover scenarios. Extensive NS-2 simulations are used to compare M&M with other micromobility schemes-cellular Internet protocol (CIP) and handoff-aware wireless access Internet infrastructure (HAWAII). For proactive handover scenarios, our results show that M&M and CIP show lower handover delay and packet reordering than HAWAII. M&M, however, handles multiple border routers in a domain, where CIP fails. Also, for scenarios of reactive handover and coverage gaps M&M clearly outperforms CIP and HAWAII.