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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.

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.

With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimise the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges with respect to the decision of cache contents. Here, a multi‐objective cache content strategy that aims to maximise the cache hitrate of SBSs in mobile edge networks (MENs) is proposed. The multi‐objective cache placement optimisation is formulated as a classification problem. Unlike previous work, mobility input attributes such as user locations, contact duration, communication ranges, contact probability between UTs and SBSs, etc. as well as content popularity and the correlation between these input attributes separating the decision space into two regions of cache and not cache are used.Stochastic gradient descent algorithm is used for the training of three supervised machine learning techniques: artificial neural network ANN, support vector machine (SVM), and logistic regression LR to define the hyperplane that separates the cache content decision space. Simulation results show that compared with the weighted‐sum approach, the SBSs cache hit rates increase on the average by 18.58%, 18.52%, and 18.2%, and the total energy consumption values decrease on the average by 33.49%, 53.19%, and 49.9% for ANN, SVM, and LR, respectively.

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.

Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve the network lifetime. In many CWSN applications, node mobility imposes challenges owing to changing network topology. Therefore, the design of a new medium access control (MAC) protocol that can handle node mobility as wellas energy harvesting is required. A cluster-based multihop MAC protocol (RMAC-M) is proposed that incorporates RF energy harvesting in a mobility-aware CWSN. Our protocol selects cluster heads using an algorithm based on an R-factor parameter consisting of residual node energy, residual node data and node speed, with appropriate weights. It then transmits data packages using a multitier super cluster head routing mechanism without the need for neighbour discovery. The multitier clustering and RFWEH mechanisms boost the energy performance of the network, increasing its lifetime. On the other hand, time slots allocated for RFWEH increase delay, thereby affecting system latency. Owing to its unique nature, the proposed algorithm has no comparable protocols in the literature. For the sake of completeness, RMAC-M is compared with well-known MAC protocols such as LEACHM and KoNMAC that do not have energy harvesting or mobility features. Simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, promising a self-sustainable network in terms of energy. Furthermore, despite the allocation of time slots for energy harvesting, critical network parameters such as throughput, packet loss and average delay remain within target levels.

Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve the network lifetime. In many CWSN applications, node mobility imposes challenges owing to changing network topology. Therefore, the design of a new medium access control (MAC) protocol that can handle node mobility as well as energy harvesting is required. A cluster‐based multihop MAC protocol (RMAC‐M) is proposed that incorporates RF energy harvesting in a mobility‐aware CWSN. Our protocol selects cluster heads using an algorithm based on an R‐factor parameter consisting of residual node energy, residual node data and node speed, with appropriate weights. It then transmits data packages using a multitier super cluster head routing mechanism without the need for neighbour discovery. The multitier clustering and RFWEH mechanisms boost the energy performance of the network, increasing its lifetime. On the other hand, time slots allocated for RFWEH increase delay, thereby affecting system latency. Owing to its unique nature, the proposed algorithm has no comparable protocols in the literature. For the sake of completeness, RMAC‐M is compared with well‐known MAC protocols such as LEACH‐M and KoNMAC that do not have energy harvesting or mobility features. Simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, promising a self‐sustainable network in terms of energy. Furthermore, despite the allocation of time slots for energy harvesting, critical network parameters such as throughput, packet loss and average delay remain within target levels.

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this article, an MEC scenario in the 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the nonorthogonal multiple access protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power–latency tradeoff, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation. Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power–latency tradeoff of the joint optimization of communication and computing resources 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.

With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimize the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges on which data to cache and where to cache these data. The process of deciding the cache contents involves multiple objectives regarding the content popularity, contact duration between UT and SBSs, communication ranges between UT and SBSs caches, and contact probability between UT and SBSs. In this paper, we propose a new strategy on cache placement decisions for mobile edge networks based on binary classification technique. The aim is to formulate the cache placement as a classification problem that is solved using machine learning techniques in order to define an optimal decision boundary on cache or not cache decisions. Simulation results show that the performance of cache placement algorithms using classifier based learning techniques can achieve higher hit rate than other algorithms.

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.

In this thesis, we proposed a Cluster-Based Cache Replacement (CBR) scheme for 5G Networks to reduce the backhaul traffic. We developed our scheme based on the understanding of the degradation of the performance of the cache placement algorithm. We expect that whenever file request pattern differs from the file popularity distribution, such as unpopular files become more popular or vice versa, the caching system should experience performance degradation. We address this problem by presenting a cache replacement scheme based on the idea of Least Frequency Used (LFU) replacement policy, but we consider only the recent request to avoid cache pollution. We evaluated the performance of CBR through simulation and compared its performance with LRU that is widely used as a cache replacement technique in practice. We simulated three different configurations of LRU scheme in a cluster-based mobile network model. Our simulation results show that the CBR outperforms LRU, where it reduces the miss ratio from 86% to 76% and the backhaul traffic from 3.67×105 to 3.47×105 MB with 10% of cache size. This superior performance it achieves by fewer replacement decisions and storing more files in the cache.

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.

Network traffic classification provides an essential contribution in network administration functions and network management such as QoS, security, and billing. Those functions need a timely and accurate detection of specific traffics. Current network traffic classification methods offer supervised and unsupervised learning capabilities for network traffic prediction or classification. Classical machine learning classifiers that use a single classification model suffer from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. These challenges in individual-based learning models have created a need for hybrid learning. Recently, hybriddeep learning has shown a significant role in traffic forecasting and classification due to its efficiency. However, a tradeoff between the aggregate models and the classification accuracy presents a substantial challenge in network traffic classification problems. In this paper, we have suggested two hybrid models that combine the Convolutional Neural Network (CNN) along with the Recurrent Neural Network (RNN) models, inclusive of the Gated recurrent unit (GRU) and Long Short-Term Memory (LSTM), to improve traffic classification accuracy. The efficiency of the suggested models has been evaluated by comparing them with various individual-based models using real network traffic traces. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification.

In the era of secure communication and the constantly changing pattern of internet applications, traditional packet classification methods fail to achieve the accuracy needed for diverse network management functions. Recently Machine Learning (ML) techniques have been used to design viable packet classification solutions. However, due to the complexity and dynamic feature of internet traffic, efficient packet classification is still challenging for various machine learning algorithms. In this paper, we propose the adoption of feature selection methods through dimensionality reduction to enhance the classifiers' performance. We evaluated the performance of four well-known classifiers, including K-nearest neighbour (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) with and without feature selection. We used two feature selection methods, including principal component analysis and Autoencoder. Experimental analysis is performed on real network traffic datasets with binary and multi-class categories. We assessed each classifier's performance using precision, recall, f-score, accuracy, and ROC. Experimental results show that the Precision, Recall, and F-score for the Multi-class problem are improved by 4.7 %, 6%, and 9%, respectively, after adopting either PCA or Autoencoder methods. The classification accuracy is also improved by up to 13%. We can also conclude that Autoencoder performed better for the KNN and LR, while PCA achieved comparable results for both the SVM and DT classifiers.

Cognitive Wireless Sensor Networks (CWSN) are known to be energy constrained because they are required to perform cognitive functions in addition to sensing, which reduces the lifetime of CWSN. RF Wireless Energy Harvesting (RFWEH) is known to improve network lifetime by compensating energy losses through harvesting gain without any additional hardware. However, the allocation of time slots for energy harvesting results in degradation of network parameters such as latency and throughput. In this paper, we propose an energy harvesting MAC protocol (R-MAC) for CWSN that balances network energy losses with harvested energy gains, thereby resulting in a self sustainable CWSN. This is done such that both latency and throughput remain close to predefined target values. R-MAC employs TDMA and relies on clustering for creation and dissemination of the schedule. We also propose an algorithm to add harvesting slots based on a recursive regression technique to achieve the energy balance. Using simulation we compared its performance with the well known cognitive KoNMAC protocol, after incorporating energy harvesting in it. The simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, resulting in an energy self sufficient network while maintaining throughput and latency within acceptable limits.

To cope with the pressing need of running content-rich multimedia applications and real-time services, Quality of Service (QoS) support has become a vital component in today’s wireless networks. Using beamforming antennas in multi-hop wireless networks can potentially spare more network resources which can be utilized to provide additional QoS guarantees. However, these opportunities are still under-explored. In this chapter, we develop a framework to analyze conflicts between wireless links in the presence of contention-based directional MAC protocols. Based on a novel taxonomy, we classify the link conflicts and propose a novel colored conflict graph abstraction to model the interdependencies between wireless links. Our analytical framework can be utilized as a basis to design several interference-aware and QoS-aware schemes for contention-based multi-hop wireless networks with beamforming antennas. 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.

Directional antenna can boost the performance of wireless networks in terms of coverage, connectivity, spatial reuse and capacity compare to the omni-direction antenna. To meet the ever- increasing demands for high data rate; industry, academia and research groups are investigating several areas of wireless technology to improve the capacity of the system. Directional beamforming antennas, directional multi-hop routing, quality of service aware support and the use of millimeter wave spectrum are some of the key areas received considerable attention in the research community. Although all of the above-mentioned technologies provide potential improvement, they also have technical challenges to address for further improvement. In this chapter we present some open issues and research directions for future generation WLAN systems.

Millimeter wave (mmWave) with large unlicensed spectrum is considered the most promising frequency band for next generation multi gigabit per second (Gbps) communication. To address emerging applications and services that demand very high throughput, IEEE 802.11ad and IEEE 802.11ay operating on 60 GHz mmWave band are the two key probable wireless local area network (WLAN) standards. IEEE 802.11ay standard still under the development phase and many of the research groups from academia and industry are working on the task group. IEEE 802.11ad is the amendment of 802.11 to provide data rate of up to 7 Gbps. It operates at frequency band 60 GHz coexisting with 2.4 and 5 GHz and is the newest member of IEEE 802.11 family. In this chapter we first provide a brief overview of mmWave communication focusing on WLAN. We then discuss some novel techniques adopted in IEEE 802.11ad and IEEE 802.11ay standards. Finally, we present three key medium access control (MAC) protocols to overcome the challenges of the of directional mmWave communications.

In this chapter, we provide a concise overview on beamforming antennas. This chapter is devoted for interested readers from the networking community who do not have background related to this technology. The goal is to provide sufficient information that can help the reader fully understand the MAC and routing research reported in the subsequent chapters. It is worthy to note that we do not intend here to discuss all the physical layer aspects of the beamforming antenna technology. Rather, we present some fundamentals of the antenna theory, categorization, comparison of different variants of smart antennas and in particular the beamforming antennas technology of both sub 6 GHz and mmWave. Readers who are interested in additional details are referred to [2, 3, 8].