The growing complexity of next-generation telecommunication networks demands more intelligent and adaptive management approaches to address the diverse requirements of emerging services. This paper proposes a novel network slicing framework that integrates the Multi-Access Technology Real-Time Intelligent Controller (mATRIC) within the O-RAN system, enabling resource unification of 5G and Wi-Fi networks and efficient allocation. The framework dynamically adapts to unify the 5G and Wi-Fi access technologies. Specifically, we propose slice deployment policies based on two Deep Reinforcement Learning (DRL) algorithms: the on-policy Trust Region Policy Optimization (TRPO) and the off-policy Soft Actor-Critic (SAC) approaches, which aims to optimize slice deployments across varying service requirements. To evaluate the effectiveness of the proposed policies, we compare them against the on-policy Advantage Actor-Critic (A2C) and off-policy Deep Deterministic Policy Gradient (DDPG) methods. Extensive simulations demonstrate that the on-policy algorithms achieve superior performance in terms of slice acceptance ratios and resource utilization, highlighting their potential to enhance slice deployment in future heterogeneous networks beyond 5G and into 6G.

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

The emerging beyond fifth-generation (B5G) and envisioned sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam on an industrial floor and assist in offloading tasks generated at different workstations. In such scenarios, the potential simultaneous task offloading during multiple MR movements may cause an excessive edge computing burden for offloaded tasks. To jointly consider MR path planning and efficient task offloading strategy, a novel weighted-sum multi-objective optimization problem is proposed where the robot total travel time and aggregated computation workload are optimized jointly to provide Pareto efficient optimal non-dominated solutions. An extensive set of numerical investigations reveal that compared with the time-only and workload-only optimization schemes, the proposed multi-objective optimization scheme can reduce the total travel time and aggregated computation workload by 39.2%, 89.2%, respectively, while achieving a decrease of the aggregated computation workload by an average of 85% compared to the Vehicle Routing Problem (VRP) solution.

It is widely accepted that the integration of intelligent reflecting surfaces (IRSs) with unmanned aerial vehicles (UAVs), or drones, can significantly enhance wireless network coverage and end-user Quality of Service (QoS). However, drones’ hovering/flying time are limited by their board battery. In this paper, we propose the concept of robotic aerial IRSs (RA-IRSs), which are essentially drones that, in addition to incorporating IRS, embed an anchoring mechanism enabling them to grasp energy-efficiently onto tall urban landforms such as lampposts. By doing so, RAIRSs can substantially reduce their total energy consumption and offer service for multiple hours, or even days, a feat not possible with traditional UAV-mounted IRS (U-IRS). Utilizing this property, we demonstrate how RA-IRS can enhance network performance by dynamically changing their anchoring location to align with the spatio-temporal traffic demand. Our proposed methodology, developed through Integer Linear Programming (ILP) formulations, offers significant Signal-to-Noise Ratio (SNR) gains in highly heterogeneous traffic regions compared to fixed IRSs, effectively addressing urban coverage disparities. Numerical simulations show that RA-IRS consumes only $3.3 \%$ of the energy used by U-IRS over a 12-hour service duration. Additionally, RAIRS demonstrates superior traffic serviceability compared to fixed terrestrial IRSs. It efficiently handles more than twice the traffic demand in areas of high heterogeneity and achieves approximately $\mathbf{5 0 \%}$ greater signal quality gain. These outcomes underscore RAIRS’s enhanced adaptability and its effectiveness in improving coverage and QoS in complex urban environments.

Network slicing technology promises to be a critical enabler of fifth-generation (5G) and sixth-generation (6G) wireless networks, allowing the infrastructure to be divided into several virtual slices. Mobile network operators are focused on developing novel solutions to implement various slices to address diverse use cases and guarantee key performance indicators (KPIs) such as latency, resource utilization, and energy efficiency. Energy efficiency (EE) KPIs are particularly important during slice deployments to ensure a reduced carbon footprint. However, deploying slices with high energy efficiency presents significant challenges. This paper proposes an energy-aware design policy for deploying slices by optimizing energy consumption and deployment capacity. A deep reinforcement learning (DRL) approach is employed, utilizing an actor-critic architecture to train a learning network modeled with a pointer network structure and an attention mechanism. A search strategy is also proposed to refine learning parameters and determine the final design policy. Compared to two existing approaches, the proposed algorithms demonstrate improved performance in terms of energy efficiency and cumulative acceptance ratio. Specifically, the EE KPI achieved by the proposed approach is enhanced to 69.7%.

This paper proposes efficient ways for constructing the energy-delay Pareto front in cache-enabled integrated access and backhauling (IAB) heterogeneous wireless network. More specifically, an improved non-dominated sorting genetic algorithm II (NSGA-II) is proposed which is coupled with the operator parameter control ability based on reinforcement learning, to solve the energy-delay trade-off in the multi-objective optimization problem. To estimate the effectiveness of the proposed scheme, key performance indicators that cover the convergence and distribution of the Pareto front solution set are conducted and analyzed. A wide set of numerical investigations show that the proposed algorithm can provide a more evenly distributed result than the state of the art techniques with a 15% gain compared to the nominal case which is the weighted-sum method. Furthermore, and maybe more importantly, the undesirable large gaps between solutions in the Pareto front which are caused by the weighting coefficient choices are avoided. By enabling the operator parameter control ability, the exploration and exploitation process of the proposed algorithm can be balanced, which prevents the frequently faced problems of early convergence and being trapped at a local optimum in the genetic algorithm. The proposed technique can have significant implications in improving the avoidable choices regarding the network operation, and compared with the traditional NSGA-II, the proposed algorithm can provide a near-optimal solution set with 20% more diversity.

The emerging beyond fifth-generation (B5G) and envisioned sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). In this paper, we consider mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells for joint communication and sensing. In such scenarios, the potential close proximity of mobile robots connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate and service degradation in the network. To mitigate this effect an interference aware path planning algorithm is proposed by explicitly taking into account the achievable performance of both communication and sensing. More specifically, the proposed heuristic scheme aims to find paths with minimal interfering time for each mobile robot whilst improving the overall communication and sensing performance. A wide set of numerical investigations reveal that the proposed heuristic path planning scheme for the mmWave connected mobile robots can improve the overall achievable communication throughput and the sensing mutual information by up to 35.6% and 23.6% respectively compared to an interference oblivious scheme. Most importantly, those gains are attained without penalizing noticeably the total travel time of the MRs.

Mobile augmented reality (MAR) applications are starting to attract significant attention due to the enhanced capabilities stemming from both the network and the end devices that propel their realization. However, despite the progress on the end user devices, MAR applications are inherently hugely demanding in terms of computational and memory requirements since they combine, inter alia, video streams, computer generated images, intense computer vision algorithms and geolocation. To this end, edge cloud computing is envisioned as a key technology for supporting such applications where part of the computationally demanding algorithms could be offloaded to suitably selected edge clouds. Within that context the inherent user mobility should be considered to allow an efficient service continuum between edge and the end-terminal. To this end, in this paper, an optimal edge cloud resource MAR service decomposition is presented that takes explicitly into account the AR service composition as well as the inherent user mobility to proactively allocate resources to satisfy the required strict latency and frame accuracy requirements of MAR applications. In addition to the optimal decision making using mathematical programming, and as a mean to provide real-time decision making two advanced heuristic techniques are proposed. A Simulated Annealing based mobility aware AR algorithm (SAMAR) is developed to enhance computing efficiency and a Long Short-Term Memory (LSTM) neural network which is trained offline with optimal solutions. Numerical investigations reveal that significant gains can be achieved by the proposed schemes compare to a number of baseline previously proposed techniques.

Trajectory optimization of unmanned aerial vehicles (UAVs) operating as flying base stations (FBSs) evolved as a novel integration component in beyond 5G (B5G) networks and has recently received significant research attention. Notably, the vast majority of previous research has mainly concentrated on the case of a single terrestrial macro base station (BS) which is used as a depot for multiple FBSs. In this article, we focus on the more general use case where multiple FBSs located at different macro-BSs used as a depot serve ground users (GUs) at cluster points (CPs). To this end, we formulate the FBSs trajectory optimization problem using a mixed-integer linear programming (MILP) formulation with the aim to minimize the total travel time (TTT) of the FBSs in a multicell network in which their cell coverage or boundary is adjustable for the FBSs deployment; creating in that sense virtual cells for the FBSs. Furthermore, heuristic algorithms are proposed to provide competitive solutions and reduce the computational time in view of the curse of dimensionality of the original problem. Numerical investigations reveal that the proposed FBSs path planning optimization solutions decrease the TTT and increase the efficiency of offloading collected data for the FBSs deployment with gains up to approximately 23% and 19%, respectively, compared to nominal schemes that consider the predefined coverage range of the cells or no cell boundaries. Aside from the above, compared to previously proposed nominal strategies, the proposed schemes achieve an almost 27% improvement in terms of fairness (Jain’s index) on the FBS traveling time.

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various Mixed Integer Linear Programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, Convolutional Neural Networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the network resources (such as caching space and link bandwidth) competition among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time, whose computation time reaches around 28.9 ms, with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in pseudo real-time.

Mobile Augmented Reality (MAR) applications require significant computational and storage resources at the end devices or at edge clouds (EC) to, inter alia, support for the Augmented Reality Objects (AROs) that are amalgamated to the physical world. In this work, a MAR service is considered under the lenses of microservices where MAR service components can be decomposed and anchored at different locations ranging from the end device to different ECs to optimize the overall service and network efficiency. The novel content-aware aspect of the proposed solution allows for proactive caching of high probability 2D field of views (FoVs) of the AROs to be stored instead of caching the significantly larger and complex 3D original AROs. To this end, a joint optimization scheme (Optim) considering mobility and the trade-off between delay, storage capacity and FoV allocation is proposed. A nominal Long Short Term Memory (LSTM) deep neural network is further explored to provide efficient pro-active decision making in real-time. More specifically, the LSTM deep neural network is trained with optimal solutions derived from a mathematical programming formulation in an offline manner. A set of numerical investigations reveal that optimal decisions manage to outperform recently proposed schemes by 24.4% to 67.5% in delay under different weights, whilst the LSTM deep neural network is effective in providing competitive solutions as well as being amenable for real-time decision making,

To overcome the limited on-board battery issue of nominal airborne base stations (ABSs), we are exploring the use of robotic airborne base station (RABS) with energy neutral grasping end-effectors that are able to autonomously perch at tall urban landforms. Specifically, this paper studies a heterogeneous network (HetNet) assisted by a movable RABS as a small cell which connects to a macro base station (MBS) through a limited-capacity wireless backhaul link, which can be deemed as another major challenge. To exploit the potential gains that the mobility of RABS can bring in the system, the minimum rate among all users is maximized by jointly optimizing the RABS deployment, user association and subcarrier allocation. This problem is initially formulated as a binary polynomial optimization (BPO) problem. After reformulating it as a nonconvex quadratically constrained quadratic programming (QCQP), we propose a semidefinite relaxation (SDR) based heuristic method to capture a high-quality solution in polynomial time. Numerical results reveal that deploying a RABS as the small cell can improve the minimum data rate by 95.43% at most and 33.97% on average, and the developed SDR heuristic algorithm significantly outperforms the linear relaxation (LR) baseline method.

The integration of Unmanned Aerial Vehicle (UAV) as flying base station (FBS) assisted wireless communications has recently attracted significant attention. However, there is a need for efficient optimization trajectory techniques to allow for continuous service connectivity for FBSs operating at regions beyond single-hop backhauling to their depot (i.e., fixed macro base station). This work proposes an optimization trajectory planning framework to provide continuous service delivery of FBSs by utilizing aerial relay nodes which can also serve ground users (GUs). The proposed joint optimization framework of FBSs trajectories where a subset of them acts as relay nodes called relay-FBS (rFBS) provides tangible gains compared to previously proposed nominal techniques. More specifically, a wide set of numerical investigations reveals that the proposed framework reduces aggregate travel time (ATT) more than 14% for the entire fleet of FBSs, whilst for rFBS to enable continuous service support, this average gain is more than 38%.

Although airborne base stations (ABSs) mounted on drones show a significant potential to enhance network capacity and coverage due to their flexible deployment, the system performance is severely limited by the endurance of the on-board battery. To overcome this key shortcoming, we are exploring robotic airborne base station (RABS) with energy neutral grasping end-effectors able to autonomously perch at tall urban landforms. This paper studies the optimal deployment (fly to another grasping location or remain in the same one) and operation (active or sleep at an epoch) of RABS based on the spatio-temporal characteristics of underlying traffic demand from end-users. Specifically, an integer linear programming (ILP) is formulated by exploiting the coupling between these two decisions, that is, the RABS only needs to visit the locations where it is active. A Lagrangian heuristic algorithm is then proposed by exploiting the totally unimodular structure of the ILP formulation. A wide set of numerical investigations reveal that thanks to its mobility, a single robotic aerial small cell is able to outperform five (5) fixed small cells in terms of served user generated traffic within a 16 to 41 hours period.

Emerging 5G and beyond wireless industrial virtualized networks are expected to support a significant number of robotic manipulators. Depending on the processes involved, these industrial robots might result in significant volume of multi-modal traffic that will need to traverse the network all the way to the (public/private) edge cloud, where advanced processing, control and service orchestration will be taking place. In this paper, we perform the traffic engineering by capitalizing on the underlying pseudo-deterministic nature of the repetitive processes of robotic manipulators in an industrial environment and propose an integer linear programming (ILP) model to minimize the maximum aggregate traffic in the network. The task sequence and time gap requirements are also considered in the proposed model. To tackle the curse of dimensionality in ILP, we provide a random search algorithm with quadratic time complexity. Numerical investigations reveal that the proposed scheme can reduce the peak data rate up to 53.4% compared with the nominal case where robotic manipulators operate in an uncoordinated fashion, resulting in significant improvement in the utilization of the underlying network resources.

Deployment of small cells in dense urban areas dedicated to the heterogeneous network (HetNet) and associated relay nodes for improving backhauling is expected to be an important structural element in the design of beyond 5G (B5G) and 6G wireless access networks. A key operational aspect in HetNets is how to optimally implement the wireless backhaul links to efficiently support the traffic demand. In this work, we utilize the recently proposed Robotic Aerial Small Cells (RASCs) that are able to grasp at different tall urban landforms as wireless relay nodes for backhauling. This can be considered as an alternative to fixed small cells (FSCs) which lack flexibility since once installed their position cannot be altered, resulting in increased energy consumption and capital expenditure to cover large number of potential hot spot areas. More specifically, on-demand deployment of RASCs is considered for constructing a millimeter-wave (mmWave) backhaul network to optimize available network capacity using a network flow-based mixed integer linear programming (MILP) formulation. Numerical investigations reveal that for the same required achievable throughput, the number of RASCs required are 25% to 65% less than the number of required FSCs. This result can have significant implications in reducing required wireless network equipment (capex) to provide a given network capacity and allows for an efficient and flexible network densification.

Mobile Augmented Reality (MAR) applications are demanding in computing and caching resources to support efficient amalgamation of Augmented Reality Objects (AROs) with the physical world in multiple video view streams. In this paper, the MAR service is decomposed and anchored at different edge nodes to enable efficient processing of popular view streams embedded with AROs. More specifically, we augment the notion of content popularity not only to objects but also video view streams and as such popular view streams are cached in a proactive manner, together with preferred AROs, in adjacent edge caching locations to ensure an acceptable level of user experience during different mobility events. To achieve that, a joint optimization problem considering mobility, service decomposition, and the balance between service delay and the preference of view streams and embedded AROs is proposed. To tackle the curse of dimensionality a nominal Long Short Term Memory (LSTM) neural network is proposed, which is trained offline with optimal solutions and provide real-time decision making during inference. Evidence from a wide set of numerical investigations shows that, the proposed set of schemes that provide service decomposition outperform nominal schemes which are oblivious of user mobility and the inherent multi modality of the MAR service.

The emerging beyond 5G and envisioned 6G wire-less networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations whilst utilizing high directivity beamformers in mmWave small cells. In such scenarios, the potential close proximity of mobile robots connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate in the network. To resolve this issue, a novel mixed integer linear programming formulation is proposed where the trajectory of the mobile robots is considered jointly with the interference level at different beam sectors. Therefore, creating a low interference path for each mobile robot on the industrial floor. A wide set of numerical investigations reveal that the proposed path planning optimization approach for the mmWave connected mobile robots can improve the overall achievable throughput by up to 31% compared to an interference oblivious scheme, without penalizing the overall travelling time.

End-to-End network slicing is an emerging technology with huge potential to carry flexible resource provisioning for the next generation of mobile networks (5G). With the powerful SDN and NFV technologies, multiple and logical network slices of various services can be deployed to the same underlying infrastructure. Physical resources are flexibly configured for different use cases. Therefore, it is necessary to explore how to guide the implementation of network slices via a general design policy. In this paper, we propose a service-aware network slicing design policy for 5G use cases, not only to satisfy various service requirements for different applications but also to enhance resource efficiency with fluctuated traffic demands. Firstly, multiple design objectives of slices are presented to guarantee different services in diverse use cases. Then we formulate a deterministic optimization model of the network slicing design problem and further extend it into a robust version considering uncertain traffic demands. Besides, a heuristic algorithm is proposed to obtain a promising network slicing design policy with the low computational effort, which is inspired by the multiple-objective particle swarm optimization (MOPSO) algorithm. The effectiveness of the present formulations and algorithms is verified by a series of simulations.

Undoubtedly mobile augmented reality (MAR) applications are starting to attract significant attention due to enhanced capabilities from both the network and the end devices which can propel their realization. However, despite the progress on the end user devices, MAR applications are inherently extremely hungry in terms of CPU and memory requirements since they are performing, inter alia, intense computer vision algorithms. To this end, edge cloud computing is envisioned as a key and integrated technology for supporting such applications where part of the intensive algorithms are offloaded to suitably selected edge clouds. In this paper an edge cloud resource optimization framework is presented that takes explicitly into account user mobility to proactively allocate resources to satisfy the required strict latency requirements of MAR applications. Numerical investigations reveal that significant gains can be achieved by creating MAR offloading tasks using mobility awareness.

Caching of popular content closer to the mobile user can significantly increase overall user experience as well as network efficiency by decongesting backbone network segments in the case of congestion episodes. In order to find the optimal caching locations, many conventional approaches rely on solving a complex optimization problem that suffers from the curse of dimensionality, which may fail to support online decision making. In this paper we propose a framework to amalgamate model based optimization with data driven techniques by transforming an optimization problem to a grayscale image and train a convolutional neural network (CNN) to predict optimal caching location policies. The rationale for the proposed modelling comes from CNN's superiority to capture features in grayscale images reaching human level performance in image recognition problems. The CNN is trained with optimal solutions and numerical investigations reveal that the performance can increase by more than 400% compared to powerful randomized greedy algorithms. To this end, the proposed technique seems as a promising way forward to the holy grail aspect in resource orchestration which is providing high quality decision making in real time.

Unmanned Aerial Vehicles (UAVs) operating as Flying Base Stations (FBSs) are emerging as a mean of furthering network capabilities in beyond 5G networks especially in terms of capacity and coverage. In such UAV assisted 5G networks the bottleneck becomes the inherent limited energy availability of the FBSs. In this paper the focus is on the provision of trajectories with fair energy consumption (EC) allocation between different FBSs that utilize a single macro-base station (BS) as their depot to serve end users. Despite the recent significant research efforts in FBS trajectory optimization schemes little attention has been placed in studying fair trajectories in terms of, inter alia, energy consumption. To this end, a number of optimal (using linear and non-linear mathematical programming formulations) and heuristic algorithms are presented to create fair EC allocation trajectories for FBSs. Via a wide set of numerical investigations a systematic comparison is presented of the different fair EC trajectory optimization algorithms as with respect to Jain's fairness index, computational times and optimality gap.

Network Slicing is regarded as a promising tech-nology to provide required services for different application scenarios by means of supporting multiple slices to embed the underlying physical network. However, it is still a challenging task to design network slicing aiming at providing specific services for different types of tenants while making efficient use of physical resources. Thus, the paper attempts to tackle the problem by proposing a novel end-to-end network slicing design policy. We start from modeling the mathematical model of the physical network and network slicing requests by using the Formal Concept Analysis methodology. Then, multiple design objectives for various use cases are defined, and the relevant resource constraints are also provided. Moreover, we propose a meta-heuristic algorithm based on the Multi-Objective Particle Swarm Optimization for the network slicing design problem. Extensive simulations illustrate that the resource efficiency of our proposed algorithm is better than the existing algorithms in terms of both node and link resources utilization.

A crucial aspect when deploying Unmanned Aerial Vehicles (UAVs) to operate as flying base stations (FBSs) assisting 5G networks is path (trajectory) optimization. Even though this topic has received significant research attention for multiple UAVs located at the same base station (BS), the area of path optimization in the case of multiple UAVs located in different BSs serving user equipment (UE) or cluster points (CPs) in a multi cell environment received less attention. This paper addresses a mixed integer linear programming (MILP) formulation for FBS path optimization in terms of travel time considering a multi-cell environment which the BSs can act as the depots for multiple UAVs. Numerical investigations reveal that the proposed UAV path optimization approach can decrease the overall travel time for the deployment of the FBSs compared to other solutions that do explicitly optimize the case of multiple BSs and the UEs and/or CPs that belong within the coverage area of different BSs.

The exploding volumes of mobile video traffic call for deploying content caches inside mobile operator networks. With in-network caching, users' requests for popular content can be served from a content cache deployed at mobile gateways in vicinity to the end user. This inherently reduces the load on the content servers and the backbone of operator's network. In light of the increasing trend in virtualization of network functions, we propose a cost-effective caching as a service (CaaS) framework for virtual video caching in 5G mobile networks. In order to evaluate the pros and cons of our CaaS approach, we formulate two virtual caching problems, namely maximum return on investment(MRI) and maximum offloaded traffic (MOT). MRI aims at maximizing return on caching investment by finding the best trade-off between the cost of cache storage and bandwidth savings from caching video contents in the mobile network operator (MNO)'s cloud. Likewise, MOT aims to maximize the traffic offloaded from the MNO's core and backhaul within given budget constraints. More specifically, taking the popularity and size of video contents into account, MRI and MOT aim to find the optimal caching tables which maximize the ratio of transmission bandwidth cost to storage cost and the offloaded traffic for a given budget, respectively. We reduce the complexity of the proposed problem formulated as a binary-integer programming (BIP) by using canonical duality theory (CDT). Experimental results obtained using the invasive weed optimization (IWO) have shown significant performance enhancement of the proposed system in terms of return on investment, quality, offloaded traffic, and storage efficiency.

Empowered by computing at the network edge, data sensed from Internet of Things (IoT) devices can be processed by various IoT applications stored in the nearby cloudlets to reduce the response time between the IoT devices. Together with the recently wide- studied network slicing and Network Function Virtualization (NFV) paradigm, IoT edge computing function can satisfy the individual requirements on the flexibility, security as well as accessibility of the IoT applications. Although the significant research effort made on NFV architectures has received remarkable achievements recently, seldom work has put its emphasis on optimizing IoT edge computing when considering it as a service chain. Since edge computing is regarded to play as a critical role in the context of IoT paradigm to enhance network efficiency, we consider in this paper the dynamic VNF service chain allocating optimization for IoT edge computing. To this end, we detail an Integer Linear Programming (ILP) framework designed to VNF based IoT edge-chains and propose a heuristic scheme to reduce the computation difficulty for large size problems. At last, the attainable system performance of the proposed scheme has been validated by a wide set of investigations.

The explosive growth of mobile data traffic and the envisioned delay sensitive applications in 5G networks ranging from high definition video streaming with strict playout deadlines to multi modal tactile/haptic with kinaesthetic feedback that require some form of edge cloud cache support makes mobility a challenge. In this paper, we propose a Proactive Caching with Delay Guarantees (PCDG) approach to enhance the supporting of seamless mobility within 5G networks that are Information-Centric Networking (ICN)-aware. The proposed scheme is designed to cache contents into a set of potential edge clouds with delay guarantees and to achieve a trade-off among caching, redirection and missing cost. In particular, this approach consider the delay constraints in mobile network, especially the queuing delay in network links and edge clouds, which are modeled as M/M/1 and M/M/c queuing systems respectively. We formulate and linearize this problem as a Mixed Integer Linear Programming (MILP) model and compare the performance with other techniques. The result obtained from simulation reveal that the proposed PCDG scheme lead to a significantly lower total cost and higher satisfied probability albeit higher computational/complexity cost.

In emergency medical services, the lag time between injury and treatment is one of the most critical parameters with respect to patient survivability. Ambulance services aim to maximize the likelihood of prompt medical treatment to prevent death and/or potential non-reversible damages. The emerging Tactile Internet has a vital role to play on that frontier by allowing next generation of ambulances to be equipped with advanced haptic/tactile devices to allow pre-hospital treatment/diagnosis or even remote surgery while en route. In this paper we propose a novel reliable multi-modal e- health high mobility service optimization framework for ambulances utilizing mobile edge clouds to efficiently transport real time patient information to the hospital. The main challenge of the proposed e-health service is to guarantee the heterogeneous QoS requirements of all involved data flows between the ambulance and the medical personnel. To this end, we formulate the service configuration problem as an optimization problem. In addition, a set of low- complexity algorithms are proposed to provide competitive solutions in real-time. A comprehensive set of numerical investigations are presented to characterize the attainable system performance of the proposed schemes.

The potential price for enabling network slicing in multi-tenant virtualized mobile networks is the underutilization of the scarce wireless and/or network resources. One way to increase overall network utilization would be to allow inter-tenant sharing, i.e., sharing of resources between different network slices. To this end, this work tries to shed further light into this issue by discussing different possible degrees of network sharing together with the associated linear integer mathematical programs that allows to investigate upper bounds on the achievable performance improvement. Furthermore, in order to realize real time and adaptive sharing of resources a scale-free heuristic is also presented that is amenable for real-time implementation. Based on the depth of the aforementioned multi-tenant sharing, we propose a sharing scheme with two options named Tight Coupling (TX) and Loose Coupling (LX). Under the 3GPP baselines and certain assumptions, a set of numerical investigations have been carried out demonstrating significant gain in aggregated network throughput and per user throughput compared to traditional fully isolated network slicing method.

As a new way to design, deploy, and manage network services, network functions virtualization (NFV) decouples the network functions, from one or more physical network infrastructures and black boxes so they can run in software. It therefore comes as no surprise that NFV originated from service providers, who were looking to improve the deployment of new network services to support their revenue and growth objectives. Within the NFV ecosystem, high availability, and low latency are one of the key quality of service (QoS) benefits that service providers can expect from the 5G Cloud and the NFV networks to make delay-critical services such as remote surgery a reality. Therefore, network services should be placed, chained, and routed through the network considering users/tenants stringent QoS and service-level agreement requirements. To this end, routing and placement optimization plays a major role in improving network performance and the overall network cost. In this paper, we study the problem of virtual network functions (VNFs) placement and routing across the physical hosts to minimize overall latency defined as the queuing delay within the edge clouds and in network links. In that respect, this paper takes a holistic view by considering not only VNFs chaining and placement problem but also considering the flows routing aspect since these two problems are inter-related and have a major impact on network latency.

Notwithstanding the significant attention that network function virtualization architectures received over the last few years, little attention has been placed on cases where proactive caching is considered within a service chain. Caching algorithms have been developed independently from virtual network function (VNF) chaining schemes, and as we explain in detail in this paper such operation might lead to sub-optimal overall network and service performance. Since caching of popular content is envisioned to be one of the key adopted technologies in emerging 5G networks to increase network efficiency and overall end user perceived quality of service, we explicitly consider the interplay and subsequent optimization of caching-based VNF service chains. To this end, a mathematical programming framework is proposed tailored to VNF caching chains and, in addition, we detail a scale-free heuristic algorithm to provide competitive solutions for large network instances since the problem itself can be seen as a variant of the classical NP-hard uncapacitated facility location problem. A wide set of numerical investigations is presented for characterizing the attainable system performance of the proposed schemes.

Device-to-Device (D2D) communication is expected to enable a number of new services and applications in future mobile networks and has attracted significant research interest over the last few years. Remarkably, little attention has been placed on the issue of D2D communication for users belonging to different operators. In this paper, we focus on this aspect for D2D users that belong to different tenants (virtual network operators), assuming virtualized and programmable future 5G wireless networks. Under the assumption of a cross-tenant orchestrator, we show that significant gains can be achieved in terms of network performance by optimizing resource sharing from the different tenants, i.e., slices of the substrate physical network topology. To this end, a sum-rate optimization framework is proposed for optimal sharing of the virtualized resources. Via a wide site of numerical investigations, we prove the efficacy of the proposed solution and the achievable gains compared to legacy approaches.

Over the last years, network virtualization has become one of the most promising solutions for sustainability towards the ongoing increase of data demand in mobile networks. Within that context, the virtual network embedding problem has recently been studied extensively and many different solutions have been proposed; but, mainly these studies have focused on wired networks. The main purpose of this paper is to provide an optimization framework for optimal virtual network embedding, including a heuristic algorithm with low computational complexity, by explicitly considering the effect of supporting the actual user mobility, assuming the emerging Distributed Mobility Management (DMM) scheme as well as a traditional Centralized Mobility Management (CMM) scheme. In addition to that, service differentiation is introduced, giving higher priority to time-critical over-the-top (OTT) services compared to more traditional elastic Internet applications. The performance of the proposed framework is compared to mobility agnostic greedy algorithms as well as virtual network embedding algorithms from the literature. Numerical investigations reveal that the effect of user mobility has a significant impact on the design of virtual networks. Additionally, the mobility aware scheme can provide tangible gains in the overall performance compared with the previous proposed schemes that do not take explicitly into account the effect of user mobility.

Network virtualization has become one of the most prominent solutions that can efficiently deal with the dramatic increase of data demand in mobile networks. In order to allow multiple virtual networks to coexist in the same substrate network, the need for development of efficient virtual network embedding algorithms and techniques is imperative. The main purpose of this paper is to provide an optimization framework for virtual network embedding that minimizes the end-to-end delay. The proposed scheme takes also into account the actual user mobility effect in order to allow efficient mapping for mobile networks. In addition, it provides service differentiation allowing delay sensitive services to use the formed virtual networks with the minimum possible delay in comparison to elastic services. The performance of the proposed algorithm is evaluated and compared to existing optimal shortest path virtual network embedding algorithms. The numerical results reveal that the proposed algorithm converges to an optimal solution and its performance can achieve significant improvement in comparison to shortest path optimization algorithms.

The exploding volumes of mobile video traffic call for deploying content caches inside mobile operators network. With in-network caching, users' requests for popular content can be served from a content cache deployed at mobile gateways in vicinity to the end user, therefore considerably reducing the load on the content servers and the backbone of operator's network. In practice, content caches can be installed at multiple levels inside an operator's network (e.g., serving gateway, packet data network gateway, RAN, etc.), leading to an idea of hierarchical in-network video caching. In order to evaluate the pros and cons of hierarchical caching, in this paper we formulate a cache provisioning problem which aims to find the best tradeoff between the cost of cache storage and bandwidth savings from hierarchical caching. More specifically, we aim to find the optimal size of video caches at different layers of a hierarchical in-network caching architecture which minimizes the ratio of transmission bandwidth cost to storage cost. We overcome the complexity of our problem which is formulated as a binary-integer programming (BIP) by using canonical duality theory (CDT). Numerical results obtained using the invasive weed optimization (IWO) show that important gains can be achieved, with benefit-cost ratio and cost efficiency improvements of more than 43% and 38%, respectively.

Information-Centric Networking (ICN) or Content-Centric Networking (CCN), as it also known for, has recently been proposed as an evolutionary framework to propel the current Internet network architecture towards a content oriented design. Mobility-aware caching can be deemed as indispensable for improving the experience of mobile users. To this end, we propose a proactive caching with redirection (PCWR) approach for enhancing the support of seamless mobility within ICN networks as well as the Quality of Service (QoS). To achieve that, we model and formulate the underlying problem as an Integer Linear Programming (ILP) problem aiming to derive the optimal balance between content caching and redirection. Via a wide set of numerical investigations, we compare the proposed scheme with various previously defined caching schemes in order to explore the inherent trade-offs. The results reveal that the proposed framework lead to significant gains in network cost compare to other techniques. Moreover, the investigations shed further light into how network cost gain is impacted by the proportion of caching and redirection cost.

The goal of expected 5G networks is to bring ultra high data rates to mobile users. To realize this, a novel Control and User plane split (C/U) communication network paradigm that deploys a large number of small base stations within the coverage area of a macro cell has been considered. However, an emerging problem for such architecture is the increasing complexity in control network load balancing and hand over events. Such problems for control plane has received little attention and it is the focus of this paper. We propose an optimal solution to the mentioned problems and discuss the related performance via numerical investigations.

Cell association in cellular networks is an important aspect that impacts network capacity and eventually quality of experience. The scope of this work is to investigate the different and generalized cell association (CAS) strategies for Device-to-Device (D2D) communications in a cellular network infrastructure. To realize this, we optimize D2D-based cell association by using the notion of uplink and downlink decoupling that was proven to offer significant performance gains. We propose an integer linear programming (ILP) optimization framework to achieve efficient D2D cell association that minimizes the interference caused by D2D devices onto cellular communications in the uplink as well as improve the D2D resource utilization efficiency. Simulation results based on Vodafone's LTE field trial network in a dense urban scenario highlight the performance gains and render this proposal a candidate design approach for future 5G networks.

The Device-to-Device (D2D) communication principle is a key enabler of direct localized communication between mobile nodes and is expected to propel a plethora of novel multimedia services. However, even though it offers a wide set of capabilities mainly due to the proximity and resource reuse gains, interference must be carefully controlled to maximize the achievable rate for coexisting cellular and D2D users. The scope of this work is to provide an interference- aware real- time resource allocation (RA) framework for relay- aided D2D communications that underlay cellular networks. The main objective is to maximize the overall network throughput by guaranteeing a minimum rate threshold for cellular and D2D links. To this direction, genetic algorithms (GAs) are proven to be powerful and versatile methodologies that account for not only enhanced performance but also reduced computational complexity in emerging wireless networks. Numerical investigations highlight the performance gains compared to baseline RA methods and especially in highly dense scenarios which will be the case in future 5G networks.

Supporting effective load balancing is paramount for increasing network utilization efficiency and improving the perceivable user experience in emerging and future cellular networks. At the same time, it is becoming increasingly alarming that current communication practices lead to excessive energy wastes both at the infrastructure side and at the terminals. To address both these issues, this paper discusses an innovative communication approach enabled by the implementation of device-to-device (d2d) communication over cellular networks. The technique capitalizes on the delay tolerance of a significant portion of Internet applications and the inherent mobility of the nodes to achieve significant performance gains. For delay-tolerant messages, a mobile node can postpone message transmission-in a store-carry and forward manner-for a later time to allow the terminal to achieve communication over a shorter range or to postpone communication to when the terminal enters a cooler cell, before engaging in communication. Based on this framework, a theoretical model is introduced to study the generalized multihop d2d forwarding scheme where mobile nodes are allowed to buffer messages and carry them while in transit. Thus, a multiobjective optimization problem is introduced where both the communication cost and the varying load levels of multiple cells are to be minimized. We show that the mathematical programming model that arises can be efficiently solved in time. Furthermore, extensive numerical investigations reveal that the proposed scheme is an effective approach for both energy-efficient communication and offering significant gains in terms of load balancing in multicell topologies.

The Device-to-Device (D2D) communication principle is envisaged to become the key enabler of direct localized communication between mobile nodes that will propel a plethora of novel location-based services. On parallel efforts, virtualization of the Radio Access Network (RAN) is rising as a primal technology for emerging and future wireless networks in which multiple mobile network providers can dynamically share underlying radio resources based on the physical infrastructure. In essence, this work shortens the gap between these two important areas by proposing a set of optimization problem formulations to extend previous works on RAN virtualization and explicitly provide resource slicing for D2D communications. To this end, we aim to yield upper bounds on network performance by devising optimal D2D resource slicing via mathematical programming formulations. In addition, sub-optimal low complexity algorithms, amenable to practical (real-time) implementation, are detailed. Via a wide set of numerical investigations, we show that the proposed solution achieves significant gains in terms of system throughput compared to previous related resource slicing techniques which are D2D oblivious.

Over the last years, network virtualization has become one of the most promising solutions for sustainability towards the ongoing increase of data demand in mobile networks. The problem of efficiently forming a virtual network has been studied extensively during the past years and many different solutions have been proposed but these studies have mainly focused on wired networks. The main purpose of this paper is to provide an optimization framework for optimal virtual network embedding by explicitly considering the effect of the actual user mobility, assuming a Distributed Mobility Management (DMM) scheme. In addition, service differentiation is introduced, giving higher priority to time-critical over-the-top (OTT) services compared to elastic Internet applications. The performance of the proposed framework is compared to greedy heuristics algorithms that are mobility agnostic and numerical investigations reveal that the effect of mobility has an important role to play in the design of virtual networks. Additionally, the mobility aware scheme can provide tangible gains in the overall performance compared with the previous proposed schemes that do not take into account the effect of user mobility.

A key characteristic of the emerging and future wireless networks (aka 5G) is the enabling of direct Device-to-Device (D2D) communications between mobile nodes. D2D communication will not only provide ultra-low power, reduced latencies and increased spatial capacity in such networks but will also unlock a plethora of new location-aware applications. Since these D2D links will be mainly controlled by the network, it is crucially important to reduce the overall overhead and also allow for mitigated traffic imbalance by controlling a potentially large number of D2Ds. In this paper, an optimization framework is introduced to firstly reduce signalling load and latency in network control-based D2D links and secondly to balance the load among neighbouring cells. To this end, under the assumption of future small cell-based ecosystems, a set of integer linear programs is introduced for D2D links that fall within the coverage area of different neighbouring base stations (BSs) with the aim to equilibrate load across cells and reduce signalling overhead by associating D2D links with only one BS. Through a wide set of numerical investigations, the benefits of the proposed schemes are evaluated showing significant performance gains in terms of load balancing and cost-based resource block (RB) allocation for orchestrating D2D links.

Network virtualization which will, inter alia, allow for dynamic network sharing, has turned into one of the most prominent solutions that can efficiently deal with the upcoming increase of data demand in mobile networks. In order to allow multiple virtual network operators to share the same substrate infrastructure, the need for the development of efficient virtual network embedding algorithms and techniques is imperative. The main purpose of this paper is to provide a robust optimization framework for shortest path virtual network embedding, where the traffic demands as well as the user mobility are uncertain (stochastic) rather than deterministic parameters. The proposed algorithm takes into account the effect of the actual users' mobility in order to allow efficient mapping for mobile wireless networks. The performance of the proposed algorithm is evaluated and compared with existing shortest path based virtual network embedding algorithms that are based on deterministic optimization problems, i.e., using nominal values on the demand. Numerical results reveal that the proposed algorithm can achieve virtual network embedding with adjustable robustness where the trade-off between conservatism and utilization of resources can be efficiently managed and controlled.

Relaying technologies can help address the capacity and energy-efficiency challenges faced by cellular networks as a result of the rapid increase in mobile data consumption. A non-negligible portion of such consumption corresponds to delay tolerant services. This delay tolerance offers the possibility for opportunistic networking to exploit contact opportunities between mobile devices in order to reduce the impact of data traffic on the cellular capacity and energy-efficiency without sacrificing the end-user quality of service. In this context, this paper investigates the use of opportunistic forwarding in MCN-MR (Multi-hop Cellular Networks with Mobile Relays) to reduce energy consumption in the case of delay tolerant services. The study proposes to exploit context information provided at a low cost by the cellular infrastructure to efficiently select the forwarding node in a two-hop MCN-MR scenario. The proposed solution results in significant energy savings compared to traditional single-hop cellular communications and other forwarding solutions reported in the literature.

In this position paper, we identify the potential of an integrated deployment solution for energy efficient cellular networks combining the strengths of two very active research themes: software defined radio access networks (SD-RAN) [1] and decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency [2]. While SD-RAN envisions a decoupled centralized control plane and data forwarding plane for flexible control, the BCG2architecture calls for decoupling coverage from capacity and coverage is provided through always-on low-power signaling node for a larger geographical area; capacity is catered by various on-demand data nodes for maximum energy efficiency. In this paper, we identify that a combined approach bringing in both specifications together can, not only achieve greater benefits, but also facilitates the faster realization of both technologies. We propose the idea and design of a signaling controller which acts as a signaling node to provide always-on coverage, consuming low power, and at the same time also hosts the control plane functions for the SD-RAN through a general purpose processing platform. Phantom cell concept is also a similar idea where a normal macro cell provides interference control to densely deployed small cells [3], although, our initial results show that the integrated architecture has much greater potential of energy savings in comparison to phantom cells.

In this paper, store-carry and forward (SCF) decision policies for relaying within the cell are developed. The key motivation of SCF relaying stems from the fact that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. More specifically, we show how the actual mobility of relay nodes can be incorporated as an additional resource in the system to achieve savings in the required communication energy levels. To this end, we provide a mathematical programming formulation on the aforementioned problem and find optimal routing and scheduling policies to achieve maximum energy savings. By investigating structural properties of the proposed mathematical program we show that optimal solutions can be computed efficiently in time. The tradeoffs between energy and delay in the system are meticulously studied and Pareto efficient curves are derived. Numerical investigations show that the achievable energy gains by judiciously storing and carrying information from mobile relays can grow well above 70 percent for the macrocell scenario when compared to a baseline multihop wireless relaying scheme that uses shortest path routes to the base station.

This paper's central aim is to address the issue of resource management at the Local Mobility Anchors (LMA) in the Proxy Mobile IPv6 (PMIPv6) networks. A class-based admission control is proposed to improve the bottleneck effect caused by triangular routing in PMIPv6, where resource units are rationed amongst different classes of traffic according to their QoS requirements. The PMIPv6 network is modeled as an M/M/m/m tandem queuing network with two types (classes) of arrival process and an analytical model is presented. Performance of our proposed admission control scheme is evaluated through simulation and results are compared to the case where no distinction in terms of resource unit allocation between classes of traffic was considered.

The increased usage of smartphones and the rich ecosystem of Internet applications are having a severe effect on the recharging cycles of devices due to the increased levels of energy consumption and limitations of battery technology. As the infrastructure of hotspots for Wi-Fi and TVWS (TV White Space) interfaces have become ubiquitously available in urban areas, the energy usage in modern devices for transmitting a fixed amount of data could differ drastically due to the significant difference on the achievable data rates on these radios. As a result, techniques that can reduce the energy cost for Internet applications with emphasis on the client side would effectively increase the battery lifetime of digital devices. To this end, we introduce a strategy that deploy roadside infrastructure Wi-Fi Access Points (AP) and determine available spectrum (TVWS) at a given location to assist the data delivery for mobile devices according to corresponding on-line service features of mobile applications. In this framework, we incorporate a probabilistic analysis for spectrum availability and cell residence time of vehicle mobility. Numerical investigations reveal that the proposed set of schemes could increase the battery lifespans by up to 25%.

The proliferation of Internet like applications in mobile devices has led to a significant increased amount of energy consumption resulting in frequent recharging cycles of the smartphones. Recent studies reveal that users may want to access the same popular video content multiple times which can be energy inefficient if it is always streamed to the users. To this end, we study different policies in terms of whether or not content should be stored in the device by taking into account the probability of re-using the content, the energy consumption of DRAM (if it is to be stored) and the energy consumption to stream the content again by taking also into account mobility information. The results reveal that a combined scheme based on probabilistic analysis is essential for increasing the lifetime of digital devices, especially for the long-term energy efficiency on the wireless downlink transmission.

Regenerative (or non-regenerative) wireless relays have recently emerged as a key technology for future and emerging mobile wireless networks, since they allow quick network roll out and improved capacity and coverage of existing networks. Nonetheless, the usefulness of extra capacity (and/or coverage) afforded by relay nodes is largely wasted insofar as the network load is small. This is because the base station (BS) can leverage the spare radio resources to serve the users. Hence, in time periods of low utilization wireless relays can be potentially switched off so that the overall energy consumption of the network is reduced. In this paper dynamic decision rules are derived to switch off relay stations with a view to increase the energy efficiency of mobile relay-aided cellular networks. Numerical investigations reveal that the proposed schemes provide significant benefits in terms of energy consumption compared to the standard “always-on” policy for relay nodes.

In this paper the problem of delay tolerant message data transmission scheduling over Cognitive Radio (CR) enabled wireless networks is considered with special emphasis on high mobility users (vehicles). The proliferation of delay tolerant applications (social networking updates, emails, updates over the air to mention just a few) can drive efficient utilization of available spectrum by Secondary Users (SU) under a CR enabled cellular network. Under the inherent stochasticity of available of transmission opportunities, the challenge is to select an optimized time duration to launch the data transmission in order to minimize the overall energy cost while satisfying the information delay constraint (with focus on delay-tolerant messages). To this end, a novel relaying scheme to deal with the problem of energy-delay trade-off, based on optimal stopping programming (OSP), is presented, whilst exploring different candidate solutions for message transmission. Additionally, we also factor energy consumption of DRAM that is used to store the message at the terminal and show its pivotal role on the different store carry and forward data transmission schemes.

Cellular networks are currently facing significant challenges as mobile Internet access adoption continues to grow over the subscriber base. The challenge that network operators are facing is that Internet data traffic consumes considerably more resources than voice calls, necessitating in that respect significant capacity enhancements. Further, this rise in system utilization has caused a considerable increase in the energy consumption expenditure of both the access network components and the user terminals; in the former case causing a significant increase in cost, for the second case a reduction in the usability of battery operated terminals and for both cases a peak in the carbon footprint of telecoms equipment. Significant research effort has been placed recently in finding innovative solutions to support this boost in data usage demand. In this paper we detail a message forwarding strategy for cellular networks whereby capitalizing on the inherent delay tolerance of Internet type services and utilizing the mobility of nodes, intelligent information forwarding decisions can be made to achieve substantial reductions in the communication energy consumption. We devise optimal look-ahead strategies in which information on either the uplink/downlink is communicated only at the best locations within the system's coverage area to achieve a required performance target. Both analytical and experimental results are presented, showing that significant reductions on the aggregate energy consumption levels can be achieved by the proposed technique.

With the tremendous increase in mobile data traffic, system capacity considerations are no longer the primary and only concern to optimize for in cellular networks. The step increase in utilization of cellular networks not only has shorten the recharging cycle of mobile terminals but has further caused a considerable rise in the operators' energy bill. It has therefore become imperative for the sustainable proliferation of such systems to reduce the energy waste and maintain low operation energy cost. As we discuss in the sequel, mechanical relaying is purposefully envisioned to achieve the required performance gains that need to be realized in order to keep up with the exponential increase in data traffic demand. Via mechanical relaying, mobile nodes are able to postpone message communication while in transit and initiate communication only when found at locations within the cell with favorable channel gains. We show that such a scheme offers the possibility to realize innovating relaying strategies that reduce many-fold the system energy consumption and increase the resource utilization efficiency. In this work, both centralized and decentralized solutions that employ mechanical relaying are considered.

Various algorithms have recently been proposed to enhance the Quality of Service (QoS) in Wireless Mesh Networks (WMNs). In this respect, we investigate joint Call Admission Control (CAC) and routing in order to provide quality of service (QoS) in wireless mesh networks. Joint association of each mesh client with a mesh access point and multi-hop backhaul routing to the Gateway, determine the availability of resources as to admit or reject a flow.We formulate a joint optimization problem, which maximizes the total revenue from all the carried connections in the network while taking into account the bandwidth constraints of access and backhaul links. In this regard, firstly we derive an optimal pricing policy that considers the complexity of the problem and the connection dropping probability. Then a CAC algorithm is presented using the pricing model. Due to its complexity, the proposed problem cannot be dealt with using the exact methods; therefore a sub-optimal solution is presented through incorporating the meta-heuristic search algorithm i.e batch based simulated annealing to the CAC algorithm.

Abstract Proxy Mobile IPv6 is a network based mobility management solution and was proposed to address the shortcomings of the existing set of mobility management protocols. The presence of local mobility anchors (LMA) in PMIPv6 networks can lead constrained routing and create areas of bottleneck. This is due to all traffic to the mobile nodes (MNs) in the PMIPv6 domain having to flow to and from the LMA. Future IP networks will support a wide variety of sessions ranging for high QoS Video conferencing to FTP and P2P download. Providing the same level of mobility support for all of these sessions can lead to the resources of LMA being drained. To avoid this, we propose the QoS aware Dynamic Route Optimization scheme where the network identifies the lower QoS sessions and establishes a binding update with the correspondant node (CN) rather than with the LMA. To achieve this a flow based binding approach is followed. The level of congestion in the network is also considered as part of the mechanism. To ensure optimal performance of the proposed mechanism, routing policies are proposed. Simulation results show considerable benefit to the network in terms of reduction in blocking probability as well as reduction in tunnelling overhead cost in comparisons to no route optimization (RO) and adaptive route optimization (ARO) being deployed in the PMIPv6 networks. Copyright © 2009 John Wiley & Sons, Ltd.

We bring to the fore a novel load balancing technique based on delay tolerant message forwarding that relies on the store-carry and forward paradigm by utilizing the mobility of vehicular nodes in multi-cell wireless networks. Considerations are made not only on the achievable load balancing performance but also on the en route energy consumption. That accounts for the optimal trade-offs between load balancing, communication energy consumption and message delivery delay in the cellular network. A mathematical program is formulated for finding optimal forwarding decision policies for the proposed store-carry and forward (SCF) relaying scheme for load balancing. Furthermore, a low complexity on-line algorithm for the proposed network setup is derived and its performance is compared with the optimal solution. To sharpen the understanding of the proposed message forwarding techniques, a wide set of numerical investigations are presented revealing that by trading-off message delivery delays, the variance of the load across cells can be dramatically reduced.

Abstract-In this paper, we propose a dynamic OFDMA based subcarrier/power allocation scheme, which aims to balance the aggregate rate and the achieved TCP throughput of competing TCP flows. The proposed allocation utilizes the theoretical TCP throughput which can be accomplished in the end-to-end path. In doing so, the TCP aware scheme attempts to minimize the gap between the allocated rate and the theoretical upper bound under the system constraints. Such a technique can be of significant importance since due to its popularity, TCP is commonly used for streaming video or other ultimedia applications. Numerical investigations reveal that the proposed approach, provides more balance towards the TCP throughput, and under some considerations significantly increase the fairness among competing TCP flows over end-to-end paths of different characteristics. In addition to that, it also manages to avoid starvation of TCP flows with poor channel conditions.

The integration of mobile relays in cellular networks has been recently envisioned as a way of increasing system capacity, coverage and reducing communication energy consumption. In we studied how store carry and forward (SCF) relaying within the cell can be utilized as an underlay message forwarding mechanism to achieve system wide energy savings. In this paper, by deriving optimal routing policies to reduce either the total energy consumption or the total transmit power, we show that the SCF relaying scheme reduces considerably inter-cell interference. We detail the factors affecting the maximum interference reductions, including the delay tolerance of elastic data traffic and flow characteristics of vehicles in the road network which in this case are used as mobile relay nodes.

A plethora of modifications to transmission control protocol (TCP) have recently been proposed, a major aim being to improve its performance over wireless links. Two schools of thought have emerged: the first investigates changes to the transport-layer protocol, whereas the second explores the potential to enhance the characteristics of lower layers to improve the end-to-end performance of TCP. This study focuses on the latter, and, in contrast to most research in this area, which thus-far has concentrated on a single TCP flavour, examines the case where different TCP flavours are competing over a wireless link. To this end, the authors present and assess a cross-layer solution to adapt the coding rate at the link-layer based on the detected TCP flavour, to maximise fairness among TCP flows. Through both analysis and simulation, the authors show that the proposed scheme considerably improves the fairness among different TCP flavours that compete over a wireless link. Furthermore, the proposed approach has minimal detrimental effect on the aggregate throughput of TCP flows.

To minimize the large handover delays associated with mobile IPv6 numerous micro-mobility protocols were proposed. In this paper, we consider the mobility agent (MA) based family of micro-mobility protocols such as hierarchical mobile IPv6 and proxy mobile IPv6 and focus on the issue of the bottlenecks that MAs can lead up to under high network load. We propose a Dynamic QoS aware route optimization with the aim of reducing the bottlenecks around MAs. We exploit the fact that not all classes of IP traffic require low handover latency support; for example, P2P traffic, web browsing, etc. do not require high QoS and are delay tolerant. Hence, such traffic classes can be forced to establish a direct connection with the CN and the routing policies at edge routers can ensure these traffic classes bypass the MA. The number of traffic classes that are to bypass the MA increases dynamically according to the congestion of the network. Results show that by implementing the dynamic route optimization the network utilization increases and the average packet delay in the network are reduced.

In this paper we address the issue of store-carry and forward (SCF) relaying within a cell in a mobile network. The proposed scheme can be considered as a generalization of various multihop wireless relaying schemes, where storing and carrying an information message by a mobile relay node is not allowed. The key motivation of utilizing SCF relaying within the cell is that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. In that respect, we consider a novel multihop cellular architecture to achieve energy savings both at the network side (i.e., Base Stations) and at the user terminals. The proposed scheme makes use of mobility information of relays (vehicles) while roaming inside the cell to device flows that could achieve maximum energy savings. We show that under SCF relaying large energy savings can be achieved by tolerating a controlled delay over the initiated service.

This paper describes the architecture of a self-configuring multi-hop mobile network and analyse the performance of the ad-hoc routing protocols. A testbed is deployed to analyse the performance of the ad-hoc routing protocols in a self-configuring multi-hop environment. The testbed comprises of a set of mobile nodes and an Internet gateway. The mobility of the nodes and the resulting network topology are emulated. During the experiments, the mobile node move virtually around a specified area and communicated with the gateway by sending UDP packets. The routing between the MNs and the Internet gateway is established by the ad-hoc routing protocols namely AODV and OLSR. Based on the measurements obtained during the experiments, some of the well known performance metrics are calculated.

A significant body of recent research has analyzed the problematic behavior of TCP over wireless links, and a plethora of modifications to TCP have been proposed in order to increase its performance in such contexts. Two schools of thought have emerged: the first proposes changes to the end-to- end protocol, while the second explores the potential to enhance lower layers as a means to improve the end-to-end performance of TCP. This paper focuses on the latter, and in contrast to most research in this area, which thus-far has concentrated on a single TCP flavor, examines the case where different TCP flavors are competing over a wireless link. To this end, we present and assess a cross-layer solution that involves the adaptation of lower layer characteristics (i.e., the coding rate) based on the detected TCP flavor, in order to maximize the fairness among TCP flows. Through extensive numerical investigations, we show that the proposed scheme considerably improves the fairness over wireless links among different TCP flavors. Our approach also has a minimal effect on the aggregate throughput of the TCP flows, and in cases where the packet error rate is very low, has a small positive effect on throughput.

A range of flavors of TCP are already in existence, and further flavors are being introduced in order to, for example, cope with the packet loss characteristics of wireless links. Moreover, the proliferation new wireless standards and the relative performance differences among them have been mushrooming in recent years. Given the increasingly heterogeneous nature of the Internet, mechanisms do not usually exist for a server to specifically select an appropriate TCP flavor for each individual download. In this paper, we therefore present and assess a cross-layer solution for a node (e.g. a base-station) to quickly adapt lower-layer characteristics (the coding rate and local ARQ retransmissions threshold) based on the detected TCP flavor, in order to optimize the end-to-end performance of the download for that utilized flavor. We demonstrate that the proposed scheme has considerable potential to improve the overall download throughput, while placing no burden on the server and requiring no changes to existing TCP implementations.

A family of mathematical programs for both the un- capacitated and capacitated joint gateway selection and routing (U/C-GSR) problem in wireless mesh networks are presented. We detail a reformulation using the shortest path cost matrix (SPM) and prove that it gives the optimal solution when applied to the uncapacitated case but can lead to an arbitrary large optimality gap in the capacitated case. Furthermore, an augmented mathematical program is developed where link capacities are allowed to take values from a discrete set depending on the link distance. In this case, the multi-rate capabilities of WMNs (via, for example, adaptive modulation and coding) can be modeled. Evidence from numerical investigations shows that using the SPM formulation realistic network sizes of WMNs can be solved.

We propose a framework that produces robust schedules in collision-free medium access schemes. We demonstrate the approach on the STDMA link scheduling problem that seeks to minimize the frame length using the physical interference model and stochastic link gains. By using conservative link gain values as opposed to average values in the SINR-target constraints, we show that the proposed approach produces shorter schedules when timeslots required for retransmission are taken into account. We derive properties on the expected frame length and provide bounds on the probability of SINR constraint violation and on the number of timeslots needed for retransmission.

The rise in the number of mobile wireless devices accessing IP based communication networks has fuelled the need for efficient mobility support for seamless communication. As a consequence a profusion of mobility protocols were proposed. The mobility agent based family of protocols have risen in popularity due to the simplicity in implementing them compared to per host based protocols. The mobility agents located within wireless access networks hide the movement of the mobile nodes by acting as an anchor point through which all the packets traverses to and from the mobile node. The presence of these mobility agents will result in bottlenecks of high congestion within the access network. This paper provides a formal definition to study this effect of mobility agents on wireless access networks. Using maximum concurrent flow problem we model the access network as a multicommodity flow problem and study the throughput of the network with and without the presence of the mobility agents. From the results it can be confidently concluded that by implementing local mobility solutions based on agents, the wireless access network capacity is reduced and the number and location of the agents within the access network have direct impact on the loss.

Transmission control protocol (TCP), the almost universally used reliable transport protocol in the Internet, has been engineered to perform well in wired networks where packet loss is mainly due to congestion. TCP throughput, however, degrades over wireless links, which are characterized by a high and greatly varying bit error rate and by intermittent connectivity. Over such wireless links, the performance achieved by TCP can be improved through the use of cross-layer algorithms at the link-level, which interact with the TCP state machine. In this paper, a TCP-aware dynamic ARQ algorithm is therefore proposed, which utilizes TCP timing information to prioritize ARQ packet retransmissions. Numerical investigation of the proposed algorithm demonstrates the performance improvements that can be attained through this approach, in comparison with TCP-agnostic link-layer approaches.

The problem of multi-rate power-controlled collision-free scheduling in spatial time division multiple access (STDMA) wireless mesh networks is formulated as a mathematical program utilising cross layer information. As these mixed integer linear programs are intractable (${\cal NP}$-hard problems), optimal collision-free schedules can be found only for topologies consisting of a few nodes. To this end, approximation algorithms that are based on linear programming relaxation and randomised rounding are studied. The proposed framework, which aims to maximise the spatial timeslot reuse under predefined signal-to-interference noise ratio thresholds, is suitable for providing centralised scheduling in the mesh mode of the IEEE 802.16 standard. Performance aspects of the approximation algorithms under different scenarios are investigated.

Mobile ad hoc networks (MANETs) are unpredictable by nature. Providing any kind of reliability for quality of service (QoS) in such networks is challenging. Quantifying available resources accurately, avoiding interference with ongoing QoS traffic and adapting to QoS violations caused by nodes' mobility are the main concerns for the design of an efficient admission control protocol in MANETs. Adaptive admission control (AAC), a novel admission control protocol which uses robust and accurate resource estimation and prediction techniques for relevant admission decisions has been proposed. Furthermore, AAC uses statistical QoS provision to counteract the QoS threatening mobility. Through simulations, we show that our proposed scheme outperforms existing approaches in terms of correctness and overall performance.

Providing Quality of Service in Mobile Ad Hoc Networks (MANET) is a challenging task due to the unreliability of the wireless medium and mobility. QoS routing is used to find QoS available paths that map QoS requirements, based on network information acquired during the route discovery process, and/ or using periodic monitoring. If the QoS demand goes beyond the network capacity, the call admission procedure generally rejects any incoming QoS request, thus, avoiding quality deterioration of existing QoS flows. However, channel conditions are likely to change and might threaten the QoS provision at any time. Therefore, a strategy is needed to reinstate QoS guarantees when at risk. Whenever Best-Effort traffic deterioration is not applicable or useless, a QoS flow has to be sacrificed for the sake of others. The selection of the victim QoS flow expresses a need for conflict resolution policies. This paper proposes and analyses different conflict resolution policies and their impact on QoS provision in Ad Hoc networks.

In this paper, a family of integer linear programs is formulated for performing collision free scheduling in Spatial-TDMA wireless mesh networks. We extend previous formulations for power aware STDMA scheduling to include discrete power transmission and multi-rate support via adaptive constellation selection. Despite the theoretical attractiveness of these (mixed) integer linear programs, STDMA scheduling problems are in general intractable (NP-hard problems). Thus, the practicality of providing optimal solutions is rather limited. To this end, we study approximation algorithms that are based on linear programming relaxation and randomized rounding. Based on these approximation algorithms we focus our study on the trade-off between optimality of the solution and feasibility. Conducted numerical investigations aim to vindicate the claim regarding the strength of randomized algorithms. In that respect the performance of the approximation algorithms under different scenarios, such as the number of active links and number of nodes in the mesh network, is investigated.

When an ad hoc network is connected to the Internet, it is important for the mobile nodes to detect available gateways providing access to the Internet. We discuss the problem of interworking and integration between the ad hoc and radio access networks. Furthermore, we propose a signalling mechanism to discover and select the optimum gateway when more than one gateway is available. A gateway is considered to be optimum if the path via this gateway provides better quality of service such as total path bandwidth or overall delay. An optimum gateway selection scheme should consider both the ad hoc and radio access networks to provide better QoS support. To achieve this selection, QoS information from both sides of the network needs to be communicated across the network. Further research is required to adapt the proposed mechanism to be applied for wireless sensor networks

Due to the unreliable nature of the wireless medium as well as risks of route failure (mobility), supporting quality of service (QoS) in ad hoc networks is a rather challenging task. In this paper, we propose AQuoS, an exclusive framework which provides and ensures QoS in mobile ad hoc networks (MANETs). Compared to existing work, to our knowledge, AQuoS is the only framework which assume different node capacity limits along with considering multi-hopping and carrier sensing interferences for admission control. To extend our contribution, we also developed a new approach which deals with unexpected lost of QoS guarantees. The low complexity of our scheme along with its flexibility makes it highly scalable for MANETs. Through simulations, we show that AQuoS outperforms SWAN protocol, especially when congestion arises

In this paper a power and rate control scheme for downlink packet transmission in CDMA networks is proposed. Under the assumption of stochastic packet arrivals and channel states the base station transmits to multiple mobile user at any time instant within rate and power capacity constraints. The objective is to maximize system throughput, while taking into account the queue length distribution over a time horizon. We are interested in optimal rate allocation policies over time and thus we formulate the problem as a discrete stochastic dynamic program. This dynamic program (DP) is exponentially complex in the number of users which renders it impractical and therefore we use an approximate dynamic programming algorithm to obtain in real time sub-optimal rate allocation policies. Numerical results reveal that the proposed algorithm increased the performance (in terms of a number of different measured parameters such as average queue size) of at least 3.5 times compared to a number of different baseline greedy heuristics

A link adaptation schemes is proposed to improve the efficiency to support DiffServ traffic over CDMA air interface. To differentiate the resource allocation between DiffServ PHBs and between the in/out profile packets within each AF class, two-level differentiation is done in the proposed scheme: inter-class differentiation and infra-class differentiation. Through this interaction between the two levels, a better QoS balance between classes is achieved and therefore a higher capacity is gained as shown by our numerical evaluation.

The paper presents a generic algorithm for supervisory control of periodical updates by monitoring mobility and traffic load in ad hoc networks. We discuss the specifications of a proposed mobility metric for mobile ad hoc network protocol evaluation that captures both longevity and rate of link changes. We apply our proposed mobility metric in conjunction with traffic load information as input parameters to design a generic algorithm that controls the rate of wireless link state updates to improve the efficiency of proactive protocols. We describe the application of the proposed controller to improve the efficiency of proactive routing and Internet gateway discovery protocols.

Abstract With IP technology being in the epicenter of future wireless networks, IP‐centric resource management is currently receiving an increasing research attention. In this paper, a power and rate adaptation semantic is proposed that integrates quality of service (QoS) information from the IP layer of the differentiated services (DiffServ) architecture together with lower layer criteria in order to optimize packet transmission over the wireless interface. An optimization problem is formulated based on the output of a time sliding window three color marker (TSWtcm) of the ingress DiffServ node in the radio access network (RAN), where color aware power and rate control are performed. The critical impetus of the proposed approach besides minimization of the transmitted power is mainly twofold. Firstly, to achieve the required per‐class aggregate data rate, while prioritizing and ensuring QoS of in‐profile packets and secondly, to increase aggregate power gains by penalizing out‐of‐profile packets. The seminal aspect of the proposed scheme is that tangible power gains can be achieved by differentiating transmission of conformant and non‐conformant packets while at the same time the power gains within each AF classes can be utilized to enhance the performance of in‐profile traffic. To buttress the case, both theoretical and simulation results are meticulously presented that depict architectural and performance related aspects of the proposed scheme. Copyright © 2005 John Wiley & Sons, Ltd.

When an ad hoc network is connected to the Internet, it is important for the mobile nodes to detect the available gateways providing access to the Internet. Therefore, a gateway discovery mechanism is required. The two main approaches for discovering Internet gateways are the reactive and the proactive one. This paper compares the performance of these approaches in various scenarios by means of simulation. We show that the proactive approach performs better in the simulated scenario.

This paper presents the emerging requirements users are imposing upon the evolving world of heterogeneous 4G mobile/wireless networks through their perception of final services. The mapping proposed in this paper groups together these user requirements in three main and distinguishable categories: service provision, connectivity, and adaptability and reconfigurability, by describing system concepts for each category from user terminal to network and services/applications.