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Internet of Things (IoT) has emerged as a new technology to enhance many services and applications including the industrial sector; thus, enabling the Industry 4.0 among many others. IoT created many challenges as the services response time requirements have grown more strict and urged the need for faster means of communications and computation processing. To solve this problem, fog computing has emerged to serve IoT devices at closer distances than cloud computing. In this paper, we leverage the Unmanned Aerial Vehicles (UAVs) mounted cloudlets to provide computation offloading opportunities to IoT devices while meeting the latency constraints. We aim at deploying the minimum number of UAVs to serve the maximum number of IoT devices within their deadlines subject to communication and computation constraints. We first formulate the problem as a multi-objective optimization problem which can be shown to be mixed-integer non-linear program (MINLP) and is NP-hard. We then propose a heuristic sub-optimal approach based on genetic algorithm (GA) to decide on the minimum number of UAVs to be deployed and the IoT-to-UAV association. Simulation results demonstrated the efficiency of the proposed approach under different system parameters and constraints in minimizing the number of UAVs while maximizing the number of served IoT devices.

Due to the rapid advancement of technologies including the tremendous growth of multimedia content, cloud computing and mobile usage, conventional networks are not able to meet the demands. Software-Defined Networks (SDN) are considered one of the key enabling technologies providing a new powerful network architecture that allows the dynamic operation of different services using a common infrastructure. Despite their notable gains, SDNs may not be secure and are vulnerable to attacks. In this paper, we address the SDN vulnerabilities and present attack-specific feature selection to identify the features that have the most impact on anomaly detection. We first use the InSDN intrusion dataset that considers different attacks including Denial-of-Service (DoS), Distributed-DoS (DDoS), brute force, probe, web and botnet attacks. We then perform data pre-processing and apply univariate feature selection to select the features having the highest impact on the different attacks. These selected features can then be used to train the model which reduces the computational cost of modeling while keeping the high performance of the model. Detailed analysis and simulation results are then presented to show the predominant features and their impact on the different attacks.

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

In hazardous Internet of Things (IoT) environments with the unlinked control server, unmanned aerial vehicles (UAVs) swarm is leveraged to relay commands with strict reliability and latency requirements. This letter optimizes the number of UAVs and their positions and decides on the IoT-to-UAV associations and radio resource allocation while allowing IoT association with multiple UAVs using orthogonal frequency division multiple access. The problem is formulated as a mixed-integer nonlinear program and linearization is proposed. A clustering-based approach providing low-complexity sub-optimal solutions is presented. Simulation results demonstrate the significance of multiple paths utilization in achieving high reliability with minimal resources.

Due to the exploding traffic demands and the diversity of novel applications requiring extensive computation and radio resources, research has been active to devise mechanisms for responding to these challenges. Mobile edge computing (MEC) and device-to-device (D2D) computation task offloading are expected to play a major role in serving devices with limited capabilities, and thus enhance system performance. In this work, we propose a joint computing, communication and cost-aware task offloading optimization problem aiming at maximizing the number of completed tasks, while minimizing energy consumption and monetary cost in D2D-enabled heterogeneous MEC networks. Our proposed scheme allows partial offloading where a requester mobile terminal offloads different parts of its data task simultaneously to multiple peer mobile terminals (MTs), edge servers and cloud. We formulate and solve the optimal allocation strategy then decompose the problem into two sub-problems in an attempt to reduce its complexity. Furthermore, we propose a low-complexity algorithm that generates high performance results and can be applied for large-scale networks. Compared to conventional and state-of-the-art system models, results show the effectiveness of the proposed schemes and provide useful insights into the tradeoffs between the number of completed tasks, energy consumption and monetary cost.

Unmanned aerial vehicles (UAVs) are considered one of the most promising emerging technologies to support rescue teams in disaster management and relief operations according to UN and Red Cross reports. In this work, we consider a disaster scene with damaged communication infrastructure and leverage UAVs for efficient and accurate positioning of potential survivors through the seamless collection of the received signal strength indicators (RSSI) of their mobile devices. We assume the scene is divided into multiple regions or cells with varying levels of importance based on the damage degree or the population density for example, and, thus, requiring different localization effort to improve the achieved accuracy. We formulate and solve two complementary subproblems. The first subproblem identifies a minimal number of strategic positions, referred to as waypoints or scanning points, at which the UAV hovers to collect the required number of RSSI signals from all devices within each cell in the disaster scene. Cells assigned higher importance levels call for higher number of RSSI readings from their devices. The waypoints generated from the first subproblem are then input to the second subproblem that constructs an efficient UAV trajectory that traverses all waypoints. By the end of the UAV mission, the collected RSSI measurements are processed to localize the discovered devices while taking into account the wireless channel statistical variability. Simulation results are generated and analyzed to demonstrate the accuracy and effectiveness of the proposed solution approach in localizing an unknown number of mobile devices in disaster scenes with regions of varying importance levels. In addition, an experimental testbed is designed and implemented as a proof of concept to validate the practicality of implementing the proposed localization solution in a realistic setting.

The Vehicle-as-a-Resource is an emerging concept that allows the exploitation of the vehicles’ computational resources for the purpose of executing tasks offloaded by passengers, vehicles, or even an Internet-of-Things devices. This article revolves around a scenario where a roadside unit located at the edge of a hierarchical multitier edge computing subnetwork resorts to the utilization of idle vehicles computational resources through a fog-enabled substructure yielding a cost-effective computational task offloading solution. In this context, scheduling the offload of these tasks to the appropriate vehicles is a challenging problem that is subject to the interaction of major role-playing parameters. Among these parameters are the variability of vehicles availability and their computational power, the individual tasks’ weighted priorities and their deadlines, the tasks required computational power as well as the required data to upload/download. This article proposes an infrastructure-assisted task scheduling scheme where the roadside unit receives computational tasks from different sources and schedules these tasks over a computationally capable vehicle residing within the roadside unit’s range. The aim is to maximize the weighted number of admitted tasks while considering the constraints mentioned above. Compared to other works, this article broaches a more realistic scenario by considering a more accurate computational task and system model. Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. After proving the NP-hardness of the scheduling problem, we formulated the problem as a mixed-integer linear program. A Dantzig–Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solve

This paper investigates the data-delivery latency in the context of intermittent vehicle-to-UAV (V2U) communications. Precisely, a V2U communication scenario is considered where vehicles opportunistically establish connectivity with passing by UAVs for a limited period of time during which these vehicles transmit data packets to in-range UAVs serving as flying base stations and, in turn, are responsible for delivering these packets to backbone networks and/or routing them over the Internet. A mathematical framework is established with the objective of modeling the vehicles’ OnBoard Units’ (OBUs’) buffers as single-server queueing systems. The established queueing model will allow for the evaluation of the V2U communication system in terms of the average data packet delivery delay. Extensive simulations are conducted with the objective of asserting the validity and accuracy of the proposed queueing model as well as providing further insights into the delay sensibility to various system parameters.

The success in transitioning towards smart cities relies on the availability of information and communication technologies that meet the demands of this transformation. The terrestrial infrastructure presents itself as a preeminent component in this change. UAVs empowered with artificial intelligence are expected to become an integral component of future smart cities that provide seamless coverage for vehicles on highways with poor cellular infrastructure. We introduce UAVs cell-free network for providing coverage to vehicles entering a highway that is not covered; however, UAVs have limited energy resources and cannot serve the entire highway all the time. Furthermore, the deployed UAVs have insufficient knowledge about the environment; therefore, it is challenging to control a swarm of UAVs to achieve efficient communication coverage. To address these challenges, we formulate the trajectories decisions making as a Markov decision process where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. Finally, simulations results are provided to verify our findings.

The unprecedented increase in the number of smart connected devices invoked a plethora of diverse applications with different performance requirements stipulating various network management strategies. Ultra-reliable low-latency communication (URLLC), one of the promised 5G dimensions, is expected to enable mission-critical applications while adhering to the heterogeneity of their quality metrics. At its core, URLLC rests on the notion of providing stringent reliability and latency requirements, in which guaranteed network availability becomes a necessity. Network slicing (NS) is one of the key paradigms that can offer performance guarantees through customized network management of software defined networking (SDN). However, unlocking URLLC with network slicing based mechanisms requires careful demultiplexing of the network into various slices and proper assignment of traffic flows generated by ultra-reliable low latency applications over those slices. Within each slice, multiple disjoint paths may be selected to ensure the reliability requirement of the assigned application while meeting its latency constraint. Hence, we study, in this paper, the joint problem of forming end-to-end network slices, mapping URLLC applications to corresponding slices and assigning their traffic flows over multiple disjoint paths; then formulate it as a mixed integer program. Due to its complexity, we decompose the problem into two subproblems; end-to-end disjoint paths and traffic flow assignment for ultra-reliable low latency applications. Simulation results are presented for various scenarios to demonstrate the performance and scalability of the proposed decomposition approach as compared to the general formulation.

With the advances in wireless technologies, there has been tremendous increase in user demand for resource-intensive mobile Internet services. This has been coupled with the limited capabilities of mobile devices and the high level of user expectations in terms of both quality of experience (QoE) and cost. An attractive enhancement technique is to utilize the co-existence of multiple interfaces in wireless devices to connect simultaneously to different access networks including cooperation over device-to-device (D2D) links. To this end, we present in this work optimized user-centric traffic splitting strategies in heterogeneous networks to achieve a high QoE level for video on demand applications with low cost and energy consumption for end users. We formulate a multi-objective optimization problem considering different pricing models with prediction whereby a user can estimate the links’ performance for future time slots and make suitable decisions accordingly. We evaluate the proposed strategies and demonstrate their effectiveness using parameters determined via experimental measurements to provide an evaluation under realistic operational conditions. Results provide useful insights on the tradeoffs between energy consumption, cost and quality of experience.

The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this paper, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This work minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles.

One of the core novel features of 5G networks is the support of ultra-reliable low latency communications (URLLC). This will pave the way for a plethora of mission critical applications in diverse sectors including health, security, gaming, and manufacturing, among others, through guaranteed premium grade reliability and delay; yet, this requires the development of innovative mechanisms that can address the existing challenges. In this work, we focus on the simultaneous utilization of multiple paths via different wireless interfaces and networks in order to deliver data from source to destination in a timely manner and with high reliability. We formulate the problem as a mixed integer program and generate an optimized strategy to efficiently utilize the resources among the different paths. Simulation results are presented for various scenarios to quantify performance gains with respect to other alternative approaches.

Vehicular network is the enabler of various services for transportation means including safety and infotainment applications. It can better deal with the highly dynamic nature of roads and highways. Meanwhile, the existing infrastructure of the cellular networks is unable to cope with the heavy traffic imposed mainly by videos. Thus, the cellular operators may leverage Roadside Untis (RSU) to offload video traffic and provide better quality of experience for vehicle customers. In this paper, we propose a pricing model that motivates RSU owners to cooperate with the cellular operators in order to offer better quality of experience (QoE) and offload substantial part of videos traffic from the cellular networks. The pricing model also motivates the vehicle owners to cooperate with the RSU to deliver contents to each other and aims to maximize RSU revenue and provide a wide range of QoS levels to the content providers in order to give them a degree of freedom to choose the suitable level of quality for their customers and based on their allocated budgets. We prove this problem is hard to solve, then we propose a lightweight greedy methods as alternative solutions. The conducted results show the efficiency of the proposed solutions and their ability to obtain results similar or so close to the optimal solutions.

The paradigm of Internet of Things (IoT) is transforming physical environments into smart and interactive platforms to offer a wide range of innovative services supported by the evolution towards 5G networks. A major class of emerging services relies on highly intensive computations to make real-time decisions with ultra-low latency. Edge computing has been established as an effective approach to reduce the latency overhead of cloud computing and effectively augment the computational capabilities of IoT devices. In this work, we leverage the mobility and agility of Unmanned Aerial Vehicles (UAVs) as mobile edge servers or cloudlets to offer computation offloading opportunities to IoT devices. In particular, we consider the joint problem of optimizing the number and positions of deployed UAV cloudlets in 3D space and task offloading decisions with cooperation among UAVs, in order to provision IoT services with strict latency requirements. We formulate the problem as a mixed integer program, and propose an efficient meta-heuristic solution based on the ions motion optimization algorithm. The performance of the meta-heuristic solution is evaluated and compared to the optimal solution as a function of various system parameters and for different application use cases. It is shown to achieve near-optimal performance with low complexity and, thus, can efficiently scale up to large IoT network scenarios.

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets. Among the emerging markets, Internet of Things (IoT) use cases are standing out with the proliferation of a wide range of sensors that can be configured to continuously monitor and transmit data for intelligent processing and decision making. Devices in such scenarios are normally extremely energy-constrained and often exist in large numbers and can be located in hard-to-reach areas; the fact that necessitates the design and implementation of effective energy-aware data collection mechanisms. To this end, we propose the utilization of Unmanned Aerial Vehicles (UAVs) to collect data in dense wireless sensor networks (WSNs) using projection-based Compressive Data Gathering (CDG) as a novel solution methodology. CDG is utilized to aggregate data en-route from a large set of sensor nodes to selected projection nodes acting as cluster heads in order to reduce the number of needed transmissions leading to notable energy savings and extended network lifetime. The UAV transfers the gathered data from the cluster heads to a remote sink node, e.g., a 5G cellular base station, which avoids the need for long range transmissions or multihop communications among the sensors. Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected cluster head nodes based on projection-based CDG with minimized UAV trajectory distance. We formulate a joint optimization problem and divide it into four complementary subproblems to generate close-to-optimal results with lower complexity. Moreover, we propose a set of effective algorithms to generate solutions for relatively large-scale network scenarios. We demonstrate the superiority of the proposed approach and the designed algorithms via detailed performance results with analysis, comparisons and insights.

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, Multi-access Edge Computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC Resource Provisioning and Workload Assignment for IoT services (RPWA) as a Mixed Integer Program (MIP) to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the Delay Aware Load Assignment (DALA) sub-problem and the Mobile Edge Servers Dimensioning (MESD) sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters.