Large Language Models (LLMs) have showcased remarkable proficiency in various information-processing tasks. They excel at data extraction, literature summarization, content generation, predictive modeling, decision-making, and system control. Moreover, Vision-Language Models (VLMs) and Multimodal LLMs (MLLMs), collectively referred to in this work as Cross-modal Language Models (XLMs), integrate multiple data modalities with language understanding, thereby advancing Autonomous Driving Systems (ADS). On the implemented Artificial Intelligence (AI) side, we analyze core techniques such as prompt engineering, supervised fine-tuning, reinforcement learning from human feedback, knowledge distillation, quantization and pruning, and safety alignment/verification, together with edge-aware deployment strategies. On the application of AI side, we map XLMs capabilities to the driving stack, including perception, prediction, planning, control, and human–machine interaction/vehicle-to-everything, and summarize how XLMs improve scene understanding, intent forecasting, decision-making, and closed-loop control by coupling natural-language reasoning with multimodal sensory inputs, such as panoramic images, Light Detection and Ranging (LiDAR), and radar. In this survey, we synthesize the state of XLMs for ADS: we review the relevant literature on ADS and XLMs, including their architectures, tools, and frameworks. We then compare deployment approaches across the driving stack and summarize datasets, simulators, and benchmarks for both open- and closed-loop evaluation. Finally, we analyze key challenges, such as grounding and hallucination, long-tail robustness, real-time and resource constraints, safety alignment and verification, and data governance and privacy, and outline research directions toward safe, efficient, and trustworthy XLM-enabled ADS.

Power consumption in mobile communication networks constitutes 20-40% of the operating expenditure. The energy footprint is especially high at the radio access network (RAN), where Base stations (BSs) account for 60-80% of network power usage. In this context, we propose in this paper a novel power coordination framework that efficiently utilizes multiple power sources including conventional grid power, renewable energy, and battery storage systems. The proposed model incorporates dynamic pricing schemes and considers environmental impact while maintaining operational cost efficiency. Using Mixed Integer Linear Programming (MILP), we develop a scheduling framework that optimizes when to charge batteries and utilize renewable energy sources for either BS operation or battery charging, aiming to reduce energy consumption costs. Our simulation results demonstrate that the proposed solution achieves significant cost reduction compared to traditional greedy power consumption methods while maintaining service quality. Moreover, it outperforms purely renewable energy-based solutions which, while cost-effective, suffer from service interruptions with a Mean Time Between Failures (MTBF) of 10 minutes. Also, our proposed solution achieves superior performance than heuristic methods, by obtaining optimal results in 2 seconds compared to 5 minutes for heuristic approaches. This work contributes to the development of more sustainable and economically viable mobile network operations without penalizing service quality.

The next generation radio access network (RAN), open RAN (O-RAN), has been introduced as an industry-level standard, supporting interoperability between different vendors' equipment and offering flexibility by including efficient network slicing with different functional split options in 5 G and beyond mobile networks. Nevertheless, strictly satisfying quality-ofservice (QoS) requirements while taking into account the energy consumption when placing network functions within the RAN and allocating resources for each network slice remains a key research problem. In this paper, we propose an O-RAN UserCentric Split Selection (O-RAN-UCSS) that aims at minimizing the deployment cost and power consumption, while considering infrastructure computation cost, link capacities as well as QoS requirements in a three-layer O-RAN architecture. The problem is formulated as an Integer Linear Problem (ILP), then solved using a Branch-and-Cut (B&C) algorithm in a realistic RAN scenario including eMBB and URLLC services. Simulation results show that our proposal outperforms the baseline approaches in terms of deployment cost, power consumption, and latency penalty at the expense of a reasonable increase of the computation time.

In the face of increasingly complex cyberattacks, particularly within 5G networks, accurately predicting the duration of an ongoing attack has become essential for effective attack mitigation. In this work, we tackle this issue by exploring the use of deep learning models to forecast cyber attack duration, thus enabling improved resource allocation and mitigation strategies. Using the diverse UNSW-NB15 dataset, our approach proposes data transformation and the creation of new key features, modeling the problem as a time-series forecasting one to predict the remaining attack time of ongoing attacks. Subsequently, several deep-learning models, suited for time-series data, have been designed. Based on automated hyperparameter tuning and feature engineering, we further refine the developed models. Through extensive simulations, we evaluate the performance of developed approaches in terms of mean absolute error (MAE). Obtained results indicate that the Transformer-based model outperforms other methods by achieving the lowest validation MAE with nearly 60 % reduction compared to the well-known Long Short-Term Memory (LSTM) model, thus showcasing its robustness in accurately predicting the duration of any type of attacks.

Fifth-generation (5 G) networks have the potential to enhance a wide range of services through improved Quality of Service (QoS) and efficient service differentiation (Network Slicing). However, for many projects such as FerroMobile (revitalizing secondary railway lines using hybrid road-rail vehicles), before deploying new 5G-based solutions, it is essential to evaluate real-world performance using emulators under realistic conditions: geographical locations, travel speeds, varying traffic loads, etc. This paper aims to fill the mobility support gap in current open-source 5G emulators, notably srsRAN and OAI. While these platforms offer robust 5 G emulation features, they do not natively support mobility scenarios in 5 G networks. To address this, we introduce and validate a new software component that integrates srsRAN with GNU Radio and Open5GS. Our solution can be extended to other emulators, particularly OAI, and includes automated scenario generation to streamline experimentation. By detailing a real-world application in the FerroMobile context, we demonstrate this software brick's practicality and adaptability.

The Future Railway Mobile Communication System (FRMCS), based on 5G technology, is being developed to support high-speed train mobility and increasing data-rate demands within the limited$5-10 \text{MHz}$frequency bandwidth allocated to railway operators. The fluctuating channel conditions and limited radio resources present a significant challenge for the Radio Access Network (RAN). Railway communications are structured around application-specific requirements, categorized into three classes: critical (ultra-reliable train control and safety operations), performance (bandwidth-intensive services like video surveillance and infotainment), and business (on-board Wi-Fi). Existing RAN slicing solutions, are designed for public 5G Networks, assume wideband carriers or static quotas at slice level or user level, so they cannot guarantee application-level requirements or adapt to dynamic channel-quality indicator (CQI) swings in railways. That is why we propose HSRS (Distributed Hierarchical Slack-Reduction Slicing), which formulates this NPhard allocation problem as an integer linear program and applies a two-phase heuristic: 1) minimize SLA slack for critical; 2) maximize weighted throughput for performance and business slices. Simulations using SNR traces captured on real trains traveling up to$350 ~\text{km} / \mathrm{h}$demonstrate the improved application-level SLA satisfaction compared to baseline methods.

Multi-access Edge Computing (MEC) promises ultra-low end-to-end latency for$5 \mathrm{G} / 6 \mathrm{G}$services, but each additional edge site drives CAPEX and OPEX, making accurate dimensioning essential. Existing studies either optimize runtime placement on a pre-deployed edge tier or size a single layer under synthetic traffic, leaving operators without a principled way to decide where to build servers or how many. We propose a fourphase, measurement-driven workflow that (i) records live radio and transport delays, (ii) converts real applications into machinereadable profiles, (iii) derives tier-specific latency budgets, and (iv) greedily assigns every service to the cheapest network layer that still meets its Service Level Agreement. Applied to FerroMobile, an autonomous railway system with URLLC and eMBB workloads, evaluations demonstrate the relevance of such a framework and the ability of a multi-layer infrastructure to reduce costs while guaranteeing performance.

The Open radio access network (ORAN) supports the multiclass wireless services required in beyond 5th-generation (B5G) mobile networks. However, it also increases the threat surface, thus requiring enhanced cyberattack detection mechanisms. To do so, advanced Artificial Intelligence (AI) algorithms combined with RAN intelligent controllers (RICs) can be leveraged to detect cyberattacks, such as distributed denial-of-service (DDoS) attacks. Nevertheless, data privacy becomes a significant concern when using AI-based operations. To bypass this issue, secured Federated Learning (FL) can be leveraged. Specifically, training cyberattack detection models locally and securely communicating the models' data for aggregation would guarantee protection against eavesdropping. In addition, the usage of Peer-to-Peer (P2P) FL would allow to avoid the centralized FL's single point of failure. However, securing P2P FL with encryption/decryption or using the Secure Average Computation (SAC) would incur high communication costs that scale poorly with the number of FL clients. Hence, we propose in this paper a novel P2P FL strategy that guarantees secure FL, while significantly reducing the communication cost. Specifically, we incorporate client selection and transfer learning within the RIC-based P2P FL system to detect cyberattacks. Through experiments, we demonstrate our method's performances across different scenarios with both balanced and unbalanced dataset distributions. Finally, its superiority in terms of accuracy, robustness, and cost, compared to existing benchmarks, is illustrated.

Software-Defined Networking (SDN) provides the flexibility to dynamically manage network paths, facilitating efficient traffic flow and mutipath routing, thereby improving network resiliency to congestion. This study investigates three distinct SDN-enabled routing strategies: shortest path routing, Equal-Cost Multi-Path (ECMP) routing, and bandwidth-based multipath routing. The comparative result analysis across these three routing strategies revealed that bandwidth-based routing consistently outperforms Shortest Path and ECMP routing across key performance metrics. The study found that Bandwidth-based routing maintains lower delay and jitter, indicating its superior ability to manage network traffic efficiently even as data flow increases. Furthermore, it demonstrates a more modest increase in packet loss, underscoring its effective congestion management. These findings suggest that Bandwidth-based routing provides a more reliable and efficient network performance, particularly in high-traffic conditions, making it a preferable solution for SDN implementations seeking to optimize data flow and network stability.

Vehicle-to-Everything (V2X) communication standards, which mainly rely on the 5G New Radio (NR) technology, can be subject to attacks such as Distributed Denial of Service (DDoS), which flood the network with non-expected control information. This causes network performance degradation and leads to accidents involving vehicles and/or vulnerable road users. A potential approach to mitigate DDoS attacks is to isolate the hijacked vehicular users in sinkhole-type slices that contain a small amount of network resources. Nevertheless, DDoS attacks may be unpredictable since it can modify its communication protocol for example, which makes it difficult to determine the proper moment to release mitigated users from the sinkhole-type slices once the security breach ceases to exist. In such a context, we propose a Reinforcement Learning-based approach that evaluates multiple types of DDoS attacks on sinkhole-type slices and estimates the optimal time to keep a mitigated user in such a slice before releasing it. The proposed approach is trained and tested with a dataset collected from a SG-V2X testbed. Results show that our approach outperforms a benchmark of random actions, in terms of the mean cumulative reward and error over time.

5G networks are highly dynamic and non-homogeneous networks, making resource management more complex and vulnerable to different network attacks like DDoS, Port Scanning, etc. In addition, Network Slicing plays an important role in these networks in enabling a multitude of 5G applications, services, and use cases. In this context, we propose in this paper, a new approach to secure 5G network slices by developing a models' orchestrator providing an adaptive Machine-Learning (ML) models as-a-Service. Specifically, the proposed models' orchestrator is a cloud server that acts as a decision-making entity to offer on-demand adaptive ML models to detect potential attacks by tuning and adapting ML parameters and algorithms according to the characteristics of the requester devices/nodes and the real-time conditions of each network slice. We demonstrate the effectiveness of our approach through a series of experiments, by training different ML algorithms with different network slices properties. Results show that our approach provides a more efficient and effective way of securing 5G networks compared to traditional methods in terms of respecting the requirements raised by each slice/node of these networks.

Vehicular-to-Everything (V2X) communication standards ensure reliable and high-performance data exchange among vehicles, pedestrians, and the roadside infrastructure. 5G New Radio (NR) is a crucial technology that enables Vehicle-to-Network (V2N) and Vehicle-to-Infrastructure (V2I) communications. In the security context, applications and network services that rely on these communication interfaces are subject to external attack sources like radio jamming that target the same control and data frequencies used by them. This causes system and network performance degradation and even Denial of Service (DoS) events, which could lead to traffic accidents involving vehicles and/or Vulnerable Road Users (VRUs). Radio jamming attacks can adopt a smart behavior by changing the targeted center frequency, bandwidth, duration, or time between two consecutive attack bursts over time. Given the context above, we propose in this paper a Deep Learning (DL)-based approach to detect radio jamming attacks on V2I/V2N communication interfaces. Our DL model is trained using a dataset collected from our 5G-V2X testbed. Results show that our DL model outperforms traditional ML algorithms and provides a detection accuracy of up to 96%, a false positive rate of less than 3%, and a detection time decrease of 39% minimum.

Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.

Future mobile networks (5G, Beyond 5G, 6G) are expected to be used in smart grids to enable new smart grid Protection, Automation, and Control (PAC) solutions. Specifically, new Fault location, Isolation, and service Recovery (FLISR) functions have been proposed to improve the reactivity and the coordination of the grid defense lines. However, both connectivity and energy consumption remain the major challenges. Indeed, protection assets need to communicate with FLISR functions with critical communication requirements ranging from best-effort to ultra-Reliable Low-Latency Communication (uRLLC) services. Besides these connectivity challenges, the energy consumption of modern telecommunication networks is highly critical. In this context, we present in this paper an energy-efficient framework for joint routing and link scheduling of FLISR traffic in 5G mobile networks. To achieve this, we formulate the problem as an integer linear program (ILP) and present an optimal solution to solve it. Our objective is to find the best trade-off between the achieved network throughput and energy consumption, while ensuring the latency constraint of FLISR traffic. Our approach is compliant with the Software-Defined Radio Access Network (SD-RAN) paradigm since it can be integrated as a control flow application on top of an SD-RAN controller. Through extensive simulations, we show that our proposed approach achieves significant gains in terms of energy consumption, flow acceptance and achieved network throughput, compared to baseline routing strategies.

Network slicing, where a single physical network is partitioned into several fit-for-purpose virtual networks with different degrees of isolation and quality of service (QoS), is a key enabler of 5G and beyond mobile networks. However, it is prone to security threats such as Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a solution based on Deep Learning (DL) that detects such attacks, and then creates a sinkhole-type slice with a small portion of physical resources to isolate and mitigate the attackers' action. Using our 5G prototype based on OpenAirInterface, we evaluate our approach by comparing several DL models in terms of detection accuracy, false positive rate, execution time, among other Machine Learning-related metrics. We also assess the performance of created 5G network slices in terms of benign/malicious users' throughput, as well as the processing time during the slicing operations. Results show that our approach is able to detect DDoS attacks in a timely manner with an accuracy of almost 97% and a false positive rate of less than 4%. We also show that our approach decreases the network throughput for the malicious users by a factor of 15, while maintaining a high network throughput for benign users.

The Internet of Things (IoT) is starting to integrate deeply into our daily lives thanks to the different services it provides. This technology has already made us more closely linked to the external environment through ubiquitous communication devices. However, even though this proximity has numerous benefits, it also has a significant security impact, where the cyber-attack surface has grown dramatically. In this regard, we present, in this paper, our results toward the development of a decision tree-based machine learning model for intrusion detection in Massive-IoT networks. The principal objective of this work is to provide a highly accurate detection model, while preserving resource consumption by developing a real prototype of the intrusion detection system. To this end, we first propose and apply our pre-processing methodology on the well-known Avast IoT-23 dataset, allowing us to reach a high detection rate with 99.99% of accuracy and just 1804KB of the model’s size. Then, we propose a new machine learning model based on the decision tree classifier and deploy it in a real environment with malicious attack traffic. Obtained results show that our proposed model allows 88% of real-traffic-based precision rate and up to 90% of specificity.

In this demo, we present a 5G prototype for attacks detection and mitigation in sliced networks leveraging Machine Learning (ML). Our prototype, based on OpenAirInterface, allows creating network slices on demand and managing physical resources dynamically according to the users’ behavior, while considering the inputs from a northbound Software Defined Network (SDN) application. We focus here on Distributed Denial of Service (DDoS) attacks, where one or multiple malicious users generate attacks on the 5G Core Network. Based on our developed ML module, we show that our prototype is able to detect such attacks, then automatically creates a sinkhole-type slice with a small portion of physical resources, and isolates the malicious users within this slice to mitigate the attackers’ action. We demonstrate the effectiveness of our approach by showing the decrease in the network throughput for the malicious users by a factor of 15, while maintaining a high network throughput for benign users.

5G networks are expected to provide a variety of services over the same physical infrastructure equipped with a combination of radio and wired transport networks and leveraging network virtualization and Software Defined Networking (SDN). In particular, network slicing is envisioned as a promising solution to enable optimal support for heterogeneous services sharing the same infrastructure. To this end, we design and implement, in this paper, an SDN based architecture for end-to-end network slicing which proactively and dynamically adapts radio slices to the transport network slices. The developed architecture enables the creation, modification and continuity of radio and transport network slices while considering their resource and Quality-of-Service (QoS) requirements. It leverages Machine Learning for predicting radio slices capacities, improving network resource utilization, and predicting congestion within each network slice. We also formulate this network slicing problem as a Linear Program (LP) aiming to minimize the total network delay. Finally, we propose an efficient heuristic algorithm with low time complexity and high estimation accuracy to solve large problem instances. Experimental results using the OpenAirInterface (OAI) platform, FlexRAN, ONOS SDN Controllers and OpenvSwitch demonstrate the efficiency of our approach in terms of guaranteeing low latency and high network throughput.

Cloud computing is being embraced more and more by telecommunication operators for on-demand access to computing resources. Knowing that 5G Core reference architecture is envisioned to be cloud-native and service-oriented, we propose, in this paper, offloading to the cloud, some of 5G delay-tolerant Network Functions and in particular the Network Data Analytics Function (NWDAF). The dynamic selection of cloud resources to serve off-loaded 5G-NWDAF, while incurring minimum cost and maximizing utilization of served next generation Node-Bs (gNBs) requires agility and automation. This paper introduces a framework to automate the selection process that satisfies resource demands while meeting two objectives, namely, cost minimization and utilization maximization. We first formulate the mapping of gNBs to 5G-NWDAF problem as an Integer Linear Program (ILP). Then, we propose an algorithm to solve it based on branch-cut-and-price technique combining all of branch-and-price, branch-and-cut and branch-and-bound. Results using pricing data from a public cloud provider (Google Cloud Platform), show that our proposal achieves important savings in cloud computing costs and reduction in execution time compared to other state-of-the-art frameworks.

Network slicing is proposed as a new paradigm to serve the plethora of 5G services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of 5G services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end- user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.

Thanks to advanced techniques such as carrier aggregation (CA), alongside with the emergence of the LTE on the 5Ghz band (LTE-U), it has become possible to boost the performance of existing cellular networks. However, LTE-U threatens the performance of co-existing Wi-Fi systems operating over the 5Ghz bands. We will investigate in this paper, an effective coexistence mechanism, that aims at creating a common ground between LTE-U's QOS satisfaction, and the WIFI's performance. We will use a poly-game generator approach, where a set of games is predicted by a neural network, to solve the issue.

Next Generation 5G Radio Access Network (NGRAN) is envisioned to integrate the slicing approach to build a flexible network supporting diverse use-cases with customized architectures, features and services. RAN processing functional splits have been standardized to add new deployment design capabilities and enhance cost efficiency. A further challenge consists in how to meet the multitude use-case's requirements while considering different design models in the physical infrastructure. Current related works are tackling the slice embedding problem from a cell-centric perspective. However, to achieve greater flexibility and better resource utilization, a user-centric approach should be more exploited. In this paper, we propose a SLICE-HPSO scheme that jointly harnesses radio, processing and link resources at the user level to build multiple user slices on top of the physical infrastructure. Our proposal is tailored to different user quality-of-service requirements and to the diverse functional splits resource requests. SLICE-HPSO is in compliance with the 3GPP and optimizes further the heterogeneous resource usage while meeting the scalability requirement.

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.

Computation offloading manages resource-intensive and mobile collaborative applications (MCA) on mobile devices where much processing is replicated with multiple users in the same environment. In this article, we propose a novel hybrid multicast-based task execution framework for multi-access edge computing (MEC), where a crowd of mobile devices at the network edge leverage network-assisted device-to-device (D2D) collaboration for wireless distributed computing (MDC) and outcome sharing. The framework is socially aware in order to build effective D2D links. A key objective of this framework is to achieve an energy-efficient task assignment policy for mobile users. Specifically, we first introduce the socially aware hybrid computation offloading (SAHCO) system model, which combines of MEC offloading and D2D offloading in detail. Then, we formulate the energy-efficient task assignment problem by taking into account the necessary constraints. We next propose a Monte Carlo Tree Search based algorithm, named, TA-MCTS for the task assignment problem. Simulation results show that compared to four alternative benchmark solutions in literature, our proposal can reduce energy consumption up to 45.37 percent.

Virtualization is getting unprecedented attention from Mobile Network Operators (MNOs) as it provides agility in deployment, especially when coupled with the Cloud that offers inherent elasticity and load-balancing of resources. MNOs have to ensure operational excellence by meeting several objectives. In this context, we propose in this paper, a framework for optimizing the mapping of next Generation Node-Bs (gNBs) to Software-Defined 5G Core (5GC) delay tolerant Network Functions (NFs). These NFs are considered to be deployed as a Virtual Machine (VM) pool, or containers, in order to minimize cloud computing cost, processing power and at the same time maximize network load. First, we formulate this problem as an integer linear program, while taking into account multiple constraints including Virtual Central Processing Unit (vCPU) capacity, central processing load limits and integrality of mapping relations between gNBs and 5GC NFs. Then, we propose an algorithm to solve large problem instances based on Branch, Cut and Price (BCP) combining all of “Branch and Price”, “Branch and Cut” and “Branch and Bound” frameworks. We present several schemes reflecting different optimization goals that the MNO can foster: virtualization cost, power minimization, network load or all. Simulation results demonstrate the good performance of our proposed algorithm to solve the gNBs-VM pool mapping for all evaluated schemes, while also emphasizing the advantages of a particular one (EWoS-333 for Equal Weight optimization Scheme) that can decrease virtualization cost by almost one order of magnitude compared to a static selection scheme, while considering the other two objectives.

Recently Mobile Edge Computing (MEC) promises a great latency reduction by pushing mobile computing and storage to the network edge. MEC solutions allows the intensive applications to be computed in nearby servers at the edge. In this work, we envision a multi-user WiFi-based MEC architecture. We tackle the problem of joint task assignment and resource allocation. The main objective of our scheme is to minimize the energy consumption on the mobile terminal side under the application latency constraint. Based on extensive simulations conducted in NS3 while considering real input traces, we show that our approach outperforms the related prominent strategies in terms of: i) energy consumption and ii) completion delay.

The centralization of network intelligence enabled by Software Defined Networking (SDN), and the recent breakthroughs of Machine Learning (ML), paved the way to address a variety of network challenges. Quality-of-Service (QoS)-aware routing is one of the important challenges in SDN-based networks, especially when multiple flows coexist in the same network. Optimizing network performances (i.e., end-to-end delay, throughput) must be achieved in order to enhance the performance of QoS-aware routing. Neural Networks and Reinforcement learning are ML breakthroughs that can tackle this important challenge. To this end, we propose, in this paper, an efficient rules placement algorithm based on Deep Reinforcement Learning (DRL) and traffic prediction. Our proposal aims to dynamically collect the optimal path from the DRL agent and predict future traffic demands using the well-known prediction method LSTM. To do so, we first formulate the flow rules placement as an Integer Linear Program (ILP) that aims to minimize the total network delay. Then, we propose a simple yet efficient heuristic algorithm to solve the formulated ILP problem with low time complexity and high estimation accuracy. The proposed algorithm interacts with the DRL agent to get the optimal path and the traffic prediction module in order to avoid congestion. The obtained results using ONOS controller and OpenvSwitch revealed the efficiency of the proposed approach in decreasing both network latency and packet loss, and rising the network throughput.

5G will serve heterogeneous demands in terms of data-rate, reliability, latency, and efficiency. Mobile operators shall be able to serve all of these requirements using shared network infrastructure's resources. To this end, we propose in this paper a framework for resource orchestration for 5G network slices implementing four Quality of Service pillars. Starting from traffic classification, demands are marked so that they are best served by dedicated logical virtual networks called Network Slices (NSs). To optimally serve multiple NSs over the same physical network, we then implement a new dynamic slicing approach of network resources exploiting Machine Learning (ML). Indeed, as demands change dynamically, a mere recursive optimization leading to progressive convergence towards an optimum slice is not sufficient. Consequently, we need an initial well-informed slicing decision of physical resources from a total available resource pool. Moreover, we formalize both admission control and slice scheduler modules as Knapsack problems. Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees as an ML based approach for both classification and prediction, outperform other alternative solutions in terms of prediction accuracy and throughput.

5G radio access networks are expected to leverage the Cloud environment for building a cost effective network infrastructure. Advanced mechanisms with functional split are introduced to split the RAN functionalities into centralized and distributed locations. This novelty has brought more flexibility to RAN deployment but is still conditioned to the radio resource availability. In this paper, we propose a new orchestration framework for joint radio and functional split scheme on user basis. Specifically, we address the orchestration of heterogeneous resources in a multi-sited Cloud-RAN infrastructure. The key idea behind our proposal is to optimize jointly the functional split and End-to-End resource allocation in order to achieve an enhanced throughput satisfaction and a low deployment cost. Results show that our approach E2E-US optimizes the heterogeneous resource usage. Indeed, the appropriate user radio load and functional split are dynamically and jointly selected, which outperforms cell centric functional split approaches.

The unlicensed band was found to be useful in terms of users applications QoS Regarding the downlink throughput. The unlicensed band, once used exclusively by the military and medical industry, will soon be exploited for the public interests. The large bandwidth guaranteed by the 5GHZ frequency band will help reduce the data transmission latency. It is for these reasons that the LTE-U (Long Term Evolution on Unlicensed band) was developed and will play a key role in the wireless networks of the 2020's. Its deployment, however, will be at the expense of other existent wireless networks technologies, specifically the Wifi. Indeed once the Wifi networks deployed alongside with the LTE-U, their users applications will be constantly interrupted, since the LTE-U is a dominant technology Regarding the channel access. This issue will cause a degradation of a large part of those deployed users in the heterogeneous networks regarding the QoS. Which is why it is important to figure out a solution that allows Wifi-exploiting applications in heterogeneous networks to serve their users with a minimum value of QoS. In this publication, we will detail the important steps towards a coexistence between the Wifi and the LTE-U, using the cooperative game theory, and more specifically the canonical coalition game, for a fairer channels/sub-channels allocation. The paper also solves the time allocation problem between the access points and the small cells, thanks to the bankruptcy game, which is an entitlement problem involving the allocation of a given amount of a perfectly divisible time among the wireless nodes. The publication will also show that the proposed solution provides a higher average user's throughput than the Bargaining Game solution, whether the user is in the Wifi or the LTE-U network.

The high proliferation of mobile devices, deploying a myriad of application, entails an explosion of mobile traffic. Due to their resource-limitation constraint, mobile devices resort to offload computational tasks on Cloud servers and improve, hence, resource usage. Unfortunately, the conventional Mobile Cloud Computing (MCC) solution involves high transmission latency. Inspired by the visions of IoT and 5G communications, recently Mobile Edge Computing (MEC) promises a great latency reduction by pushing mobile computing and storage to the network edge (i.e., base stations and access points). The key challenge of MEC solution is to find an efficient assignment of tasks with local or remote devices while minimizing energy consumption and latency. In this paper, we propose a new joint task assignment and resource allocation approach in a multi-user WiFi-based MEC architecture. The main novelty of our work is that optimal offloading decision is jointly performed with the radio resource allocation. The objective of our scheme is to minimize the energy consumption on the mobile terminal side under the application latency constraint. To do so, we first formulate our problem as a new Integer Program (IP) while considering both delay and device computation constraints. Then, we propose a new strategy named Joint Offloading and Resource allocation in WiFi-based MEC architecture (JOR-MEC) to solve it. Based on extensive network simulations conducted with NS3 simulator while considering real input traces, we show that our proposal outperforms the related prominent baseline strategies in terms of: i) energy consumption and ii) completion delay.

The next 5G infrastructure is expected to serve a multitude of services with heterogeneous requirements, which might be potentially managed by multiple Mobile Virtual Network Operators (MVNOs) that share the same network infrastructure. The new emerging technologies, such as i) Software Defined Networking (SDN), ii) Network Function Virtualization (NFV) and iii) Network Slicing, where physical resources are partitioned and allocated in an isolated manner to a set of services or to MVNOs according to a specific Service Level Agreement (SLA), are seen as the key enabling approaches to fulfill the diversity of requirements of 5G services in a cost-effective manner. In this paper, we design and prototype a network slicing solution, which we have developed in a Cloud-RAN (C-RAN) infrastructure based on the Open Air Interface (OAI) platform and FlexRAN SDN controller. The aim of our work is to validate the feasibility of the prototype in handling the creation and configuration of network slices on-demand, taking into account some requirements that are elaborated from SDN-based slicing applications. By means of emulations, we show that our prototype reacts well to the inputs coming from the SDN application and is finally capable of providing isolation among multiple slices in a dynamic fashion.

Network slicing is considered a key technology for the upcoming 5G system, enabling operators to efficiently support multiple services with heterogeneous requirements, over a common shared infrastructure. In this demo, we present a prototype for managing network slices in the Cloud Radio Access Network (C-RAN), which is considered the reference network architecture for 5G. Our prototype deals with the spectrum slicing problematic and aims at efficiently sharing the bandwidth resources among different slices, while considering their requirements. The prototype makes use of the Open Air Interface (OAI) platform and a specific Software Defined Network (SDN) controller, known as FlexRAN. By using real smart-phones, we run experiments on stage to validate the feasibility of the prototype in configuring multiple slices on-demand, driven by the input of a northbound application.

In Software Defined Networking, due to the significant bandwidth and latency requirements, predicting available resources (i.e., latency, bandwidth) is crucial for enhancing performances, resource utilization and power consumption of the data plane. In this paper, we propose an efficient rules placement algorithm based on predictive network latency using online learning. Our proposal aims to dynamically predict the latency for updating the flow rules in network devices. To do so, we first formulate the flow rules placement as an Integer Linear Program (ILP) that aims to minimize the total network delay. Then, we propose a simple yet efficient heuristic algorithm to solve the formulated ILP problem with low time complexity. Experimental results using ONOS controller and Mininet show the efficiency of our proposal in decreasing network latency, packet loss and rising the network throughput.

The massive adoption of Cloud technology in mobile access networks has driven the operators and vendors to work together in order to make Radio Access Network (RAN) ecosystem more agile. In this context, the virtualization of network functions is the cornerstone of a successful Network Function Virtualization (NFV) environment. However, the stringent requirements of RAN functions make their deployment in a Cloud infrastructure more complex and prone to performance issues. In this respect, this paper puts forward a novel approach that adopts an agile orchestration of fine-grained RAN network functions in order to achieve higher flexibility and improve performances. Specifically, we address the orchestration of baseband processing network functions in a multi-sited Cloud infrastructure. To do so, we propose a user-centric solution, denoted UCS-CRAN, that optimizes the split of the baseband units, while considering both the requirements of its processing network functions and the capabilities of Cloud infrastructure. Based on extensive simulations, the results show that our proposal optimizes both processing and bandwidth usage while minimizing the energy consumption compared to cell-centric, distributed and centralized Cloud-RAN approaches.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU's throughput by up to 9 times and 22% in comparison to these both schemes.

Mobile devices are equipped with limited processing power and battery charge. A mobile computation offloading framework is a software that provides better user experience in terms of computation time and energy consumption, also taking profit from edge computing facilities. This article presents User-Level Online Offloading Framework (ULOOF), a lightweight and efficient framework for mobile computation offloading. ULOOF is equipped with a decision engine that minimizes remote execution overhead, while not requiring any modification in the device’s operating system. By means of real experiments with Android systems and simulations using large-scale data from a major cellular network provider, we show that ULOOF can offload up to 73 percent of computations, and improve the execution time by 50 percent while at the same time significantly reducing the energy consumption of mobile devices.

Mobile computation offloading has recently attracted much interest and first offloading solutions have been developed. However, the relevant technical challenge of how to automatically determine offloadable sections of Android applications has not been adequately investigated so far. This paper proposes an innovative task selection algorithm that can parse an Android application autonomously and classify all the methods based on their offloadability by adopting a fine grained and multi-steps analyzer. The reported experimental results show the effectiveness of our solution when applied to the top 25 most downloaded Android apps on the Google Play store, by showing its accuracy in identifying off loadable methods and demonstrating the potential benefits of automated mobile computation offloading.

Computation offloading has already shown itself to be successful for enabling resource-intensive applications on mobile devices. Moreover, in view of mobile edge computing (MEC) system, mobile devices can offload compute-intensive tasks to a nearby cloudlet, so as to save the energy and enhance the processing speed. However, due to the varying network conditions and limited computation resources of cloudlets, the offloading actions taken by a mobile user may not achieve the lowest cost. In this paper, we develop a dynamic offloading framework for mobile users, considering the local overhead in the mobile terminal side, as well as the limited communication and computation resources in the network side. We formulate the offloading decision problem as a multi-label classification problem and develop the Deep Supervised Learning (DSL) method to minimize the computation and offloading overhead. Simulation results show that our proposal can reduce system cost up to 49.24%, 23.87%, 15.69%, and 11.18% compared to the “no offloading” scheme, “random offloading” scheme, “total offloading” scheme and “multi-label linear classifier-based offloading” scheme, respectively.

As Mobile Network Operators (MNOs) are shifting towards Cloud- Radio Access Network (C-RAN), they have to upgrade their infrastructure to not only support higher processing capacities but also to be more resilient. We consider the problem where a MNO is faced with the choice of selecting virtualized Baseband Units (BBUs) from various cloud service providers, that are each characterized with distinct failure probabilities and prices. We propose to solve the BBU selection problem, formulated as an Integer Linear Program (ILP) subject to BBU capacity and virtualization cost using the Branch- and- Price algorithm. We present several schemes depicting which optimization goal the MNO can foster the most: BBU processing power minimization, resiliency, traffic handling or all. Simulation results demonstrate the good performance of our algorithm to solve the BBU selection problem for all schemes, while also emphasizing the advantages of a particular one that can realize more than 10% in virtualization cost savings.

In this paper, we address the problem of downlink resource allocation and admission control for an Orthogonal Frequency Division Multiple Access (OFDMA)-based Cloud Radio Access Network (C-RAN). Specifically, we formulate the resource allocation and admission control for mobile users in C-RAN as an optimization problem, subject to constraints on mobile users data rate requirements, maximum transmission power and fronthaul links capacity. By dropping the non-linear constraint and reformulating the problem linearly using the framework of the well-known big-M method, we propose a two-stage algorithm that can efficiently solve it. To satisfy the strict timing requirement of wireless communications in such a system, a time constraint was added to our algorithm. Numerical results demonstrate the good performance of our proposal in terms of number of accepted users and total transmission power, when compared with state-of-the-art methods used for the control admission task in C-RAN.

Computation offloading manages resource-intensive and interactive applications on mobile devices where much processing is replicated with multiple users in the same environment. In this paper, we consider the scenario where duplicated computation tasks are processed on specific mobile users and computation results are shared through Device-to-Device (D2D) multicast channel. Our goal is to find an optimal network partition for D2D multicast offloading, in order to minimize the overall energy consumption at the mobile terminal side. To this end, we first propose a D2D multicast-based computation offloading framework where the problem is modelled as a combinatorial optimization problem, and then solved using the concepts of from maximum weighted bipartite matching and coalitional game. Note that our proposal considers the delay constraint for each mobile user as well as the battery level to guarantee fairness. To gauge the effectiveness of our proposal, we simulate three typical interactive components. Simulation results show that our algorithm can significantly reduce the energy consumption, and guarantee the battery fairness among multiple users at the same time.

With the exponential growth of small cells in the next generation of mobile networks, the Macrocell User Equipments (MUEs) located in the vicinity of the Femtocell Access Points (FAPs) suffer from high downlink interferences. The hybrid access mode of FAPs has shown promising features to mitigate these interferences but the FAPs prioritize the closed suscribers group (CSG) users over the public users. In this paper, we propose a framework for macrocell-femtocell cooperation in order to protect harmed public users whether it is from a prioritized serving FAP or from interfering neighbouring femtocells. When the harmed MUE connects to a nearby FAP, we model the interference management and resource allocation problem as a game with coalition structure (GCS) where the paired FAP and MUE form a priori union. Two coalitional values are identified and compared to compute the payoff of the GCS, namely the Solidarity-Shapley value and the Weighted Owen value. For the MUEs who failed to connect to the nearby FAP, a canonical game is played with the Solidarity value as imputation, to protect the harmed MUEs from powerful FAPs in the game. We show through extensive simulations that the combination of theses schemes compared to other solutions and access modes of the art, shows the best performances for the MUEs and Femtocell user equipments (FUEs) of the system in term of throughput and fairness.

Cloud-Radio Access Network (C-RAN) is a new emerging technology that holds alluring promises for Mobile network operators regarding capital and operation cost savings. However, many challenges still remain before full commercial deployment of C-RAN solutions. Dynamic resource allocation algorithms are needed to cope with significantly fluctuating traffic loads. Those algorithms must target not only a better quality of service delivery for users, but also less power consumption and better interference management, with the possibility to turn off RRHs that are not transmitting. To this end, we propose in this paper a dynamic two-stage design for downlink OFDMA resource allocation and BBU-RRH assignment in C-RAN. Specifically, we first model the resource and power allocation problem in a mixed integer linear problem for real-time fluctuating traffic of mobile users. Then, we propose a Knapsack formulation to model the BBU-RRH assignment problem. Simulation results show that our proposal achieves not only a high satisfaction rate for mobile users, but also minimal power consumption and significant BBUs savings, compared to state-of-the-art schemes.

Computation offloading has already shown itself to be successful for enabling resource-intensive applications on mobile devices. However, in view of immersive applications, the offloaded tasks could be duplicate when multiple users are in the same environment. In this paper, we consider the scenario that multiple mobile users offload duplicated computation tasks to a set of nearby Femto-Cloud called Small Cell cloud enhanced e-NodeB (SCceNB), and share the computation results among them. Our goal is to find an optimal offloading and sharing strategy to minimize the overall energy consumption at the mobile terminal side. To this end, we propose a cooperative call graph to model the problem. Based on the derived call graph, we present a distributed algorithm that combines notions from 0-1 programming and coalitional game to solve it, while considering the delay constraint for each mobile user as well as the computation ability and memory constraints of each SCceNB. Simulation results show that our proposal can reduce energy consumption up to 39.73%, 34.37%, and 19.54% compared to the “total offloading” scheme, the “no offloading” scheme, and the “optimal offloading without sharing” scheme, respectively.

In this paper, we investigate intelligent handoff decisions in femtocell networks, with the goal of achieving a seamless femtocell-to-femtocell handoff procedure that minimizes unnecessary handoffs and provides a good quality of connection to mobile users. To this aim, we propose a promising handoff scheme called Optimized Handoff based on Mobility Prediction and Call Admission Control (OHMP-CAC). Our proposal is based on a next cell prediction module using an efficient Hidden Markov Model (HMM) predictor on one hand, and a Call Admission Control (CAC) algorithm with service differentiation for applications with and without QoS constraints, on the other hand. To gauge the effectiveness of our proposal, we conducted extensive simulations based on real human mobility traces. Results show that OHMP-CAC outperforms existing approaches in terms of the number of handoffs, the dwell time, and the handoff decision time, while providing a connection with excellent quality of signal to femtocell user equipments (UE).

In this paper, we propose a new approach for optimizing the handoff decision in femtocell networks using Hidden Markov Model. To do so, we formulate the handoff problem as an optimization problem whose objective is to find the best Femtocell Access Point (FAP) assignment strategy that minimizes the number of unnecessary handoffs while maintaining a good quality of wireless communications. We have used Hidden Markov Model prediction tools to predict the target FAP by observing the geographic positions of the mobile. To evaluate the effectiveness of our proposal, we conducted extensive simulations. Results show that our proposed approach minimizes the number of handoffs by up to 7 times and enhances the dwell time in the FAP by up to 42% in comparison with others handoff decision making strategies commonly used in related cellular modeling works.

In this paper, we propose a new 2-Dimensional Underwater Barrier Deployment Algorithm (2D-UBDA) ensuring the barrier detection of toxic substances in a river. Our objective is to guarantee a full detection of chemical pollutant sources, while minimizing the deployment cost. To achieve this, first 2D-UBDA determines the potential deployment areas within a predefined target field installation and this, for each pollution source, by using a 3D-propagation model of a substance to predict its molarity in any point within the river. Then, based on an integer linear programming algorithm, 2D-UBDA selects the minimum number of sub-areas in which chemical sensors will be deployed by taking into consideration the intersections between the potential deployment zones of all pollution sources located upstream of the target field installation. To validate our proposal, the Pamplonita river located in Amazon rainforest is used as a case of study. Based on extensive simulations, 2D-UBDA outperforms the basic deployment strategies in terms of number of chemical sensors and successful detection of pollutant.

Femtocells are emerging as a key technology to improve coverage and network capacity in indoor environments. When femtocells use different frequency bands than macrocells (i.e., split-spectrum approach), femto-to-femto interference remains the major issue. In particular, congestion cases in which femtocell demands exceed the available resources raise several challenging questions: how much a femtocell can demand? how much it can obtain? and how this shall depends on the interference with its neighbors? Strategic interference management between femtocells via power control and resource allocation mechanisms is needed to avoid performance degradation during congestion cases. In this paper, we model the resource and power allocation problem as an operations research game, where imputations are deduced from cooperative game theory, namely the Shapley value and the Nucleolus, using utility components results of partial optimizations. Based on these evaluations, users' demands are first rescaled to strategically justified values. Then, a power-level and throughput optimization using the rescaled demands is conducted. The performance of the developed solutions is analyzed and extensive simulation results are presented to illustrate their potential advantages. In particular, we show that the Shapley value solution with power control offers the overall best performance in terms of throughput, fairness, spectrum spatial reuse, and transmit power, with a slightly higher time complexity compared to alternative solutions.

Recent studies have shown that the energy consumption of wireless access networks is a threat to the sustainability of mobile cloud services. Consequently, energy efficient solutions are becoming crucial for both local and wireless access networks. In this paper, we propose a flow-based management framework to achieve energy efficiency in campus networks. We address the problem from the dynamic perspective, where users come and leave the system in an unpredictable way. Specifically, we propose an online flow-based routing approach that allows dynamic reconfiguration of existing flows as well as dynamic link rate adaptation, while taking into account users' demands and mobility. Our approach is compliant with the emerging software defined networking (SDN) paradigm since it can be integrated as an application on top of an SDN controller. To achieve this, we first formulate the flow-based routing problem as an integer linear program (ILP). As this problem is known to be NP-hard, we then propose a simple yet efficient ant colony-based approach to solve the formulated ILP. Through extensive simulations, we show that our proposed approach is able to achieve significant gains in terms of energy consumption, compared to heuristic solutions and conventional routing solutions such as the shortest path (SP) routing, the minimum link residual capacity routing metric (MRC), and the load balancing (LB) scheme. In particular, we show that the energy consumption can be reduced by up to 7%, 35%, 44%, and 49% compared to Greedy-OFER, MRC, SP, and LB, respectively, while ensuring the required quality of service (QoS).

With the massive adoption of cloud-based services, high energy consumption and carbon footprint of cloud infrastructures have become a major concern in the IT industry. Consequently, many governments and IT advisory organizations have urged IT stakeholders (i.e., cloud provider and cloud customers) to embrace green IT and regularly monitor and report their carbon emissions and put in place efficient strategies and techniques to control the environmental impact of their infrastructures and/or applications. Motivated by this growing trend, we investigate, in this paper, how cloud providers can meet Service Level Agreements (SLAs) with green requirements. In such SLAs, a cloud customer requires from cloud providers that carbon emissions generated by the leased resources should not exceed a fixed bound. We hence propose a resource management framework allowing cloud providers to provision resources in the form of Virtual Data Centers (VDCs) (i.e., a set of virtual machines and virtual links with guaranteed bandwidth) across a geo-distributed infrastructure with the aim of reducing operational costs and green SLA violation penalties. Extensive simulations show that the proposed solution maximizes the cloud provider's profit and minimizes the violation of green SLAs.

Cars cruising for parking adds a non negligible amount to traffic congestion and CO2pollution. Hence, good parking management policies are required to reduce such discomfort. In this paper, we address this problem from two sides. First, we consider how local parking authorities called parking coordinators (PC) can optimize the distribution of the slots they manage through a full cooperation between them. Second, we model the problem as a congestion game, where vehicles act as players who will eventually choose the best parking garage for them, while minimizing the whole network cost. We study the effectiveness of both schemes (i.e, PC-aware and game theoretic approaches) in various contexts and compare them with the reference centralized model as well as a greedy approach. Simulation results show that our proposals provide high request satisfaction ratio, close to the optimal baseline approach and outperform the greedy method up to 30%, while ensuring a fair distribution of slots through the network.

The last few years have witnessed an increase in energy consumption in Information and Communication Technology (ICT). Naturally, energy efficient solutions are becoming crucial for both local and wireless access networks. In this paper, we propose a new framework to support energy efficient management in Wireless Mesh Networks (WMNs). A key distinguishing feature of our solution is its online flow-based routing approach since existing flows are dynamically consolidated or even re-routed at fixed intervals according to live arrival and departure of mesh clients. The proposed solution is compliant with emerging Software Defined Networking (SDN) paradigm since it relies on a central controller to monitor and manage the network. To achieve this, we first formulate the problem as an integer linear program (ILP). As this problem is known to be NP-hard, we then propose a simple yet efficient Ant Colony-based approach to solve the formulated ILP problem. Through extensive simulations, we show that our proposed approach is able to achieve significant gains in terms of energy consumption, compared to conventional routing solutions such as the Shortest Path (SP) routing, the Minimum link Residual Capacity routing metric (MRC) and the load balancing (LB) scheme. Specifically, we show that our approach reduces the energy consumption by up to 13%, 20%, and 52%, compared to MRC, SP and LB, respectively, while achieving the required QoS.

In this paper, we study the wireless sensor network deployment for water pollution monitoring in the Amazon rainforest rivers. Our objective consists in minimising the number of deployed geographical field installations along the river, while ensuring the detection of the substance spilled in the given river regardless of the position of its source. A geographical field installation is formed by a set of barrier coverage underwater sensors which detect the pollutant if its molarity in the water is greater than a predefined threshold. Indeed, the substance molarity is inversely proportional to the moving distance. To generate the best topology, we propose a sub-optimal novel geographic Installation Field Deployment Algorithm based on the Backtracking heuristic named BT-FIDA. Since the river has a several forks, in order to reduce the number of installation fields, BT-FIDA minimises the rate of at least 2-covered river segments. The simulation results obtained show that our proposal minimises the number of field installations (i.e., deployment cost) while minimising the rate of areas which are miss-covered and over-covered.

Femtocell provisioning is emerging as a key technology to improve coverage and network capacity in indoor environments. When femtocells use different frequency bands than macrocells (i.e., split-spectrum approach), femto-to-femto interference remains the major issue. In particular, congestion cases in which femtocell demands exceed the available resources pose an important challenge. In this paper, we propose a joint resource and power allocation strategy for the management of interference in cooperative femtocell networks. We model the resource and power allocation problem as an operations research game, where imputations are deduced from cooperative game theory, namely the Shapley value and the Nucleolus, using utility components results of partial optimizations. The performance of the developed solutions is analyzed and extensive simulation results are presented to illustrate their potential advantages. In particular, we show that the Shapley value solution with power control offers the overall best performance in terms of throughput, fairness, and transmit power, compared to alternative solutions.

In the last few years, femtocells have gained a great deal of interest as an emerging wireless and mobile access technology to improve indoor coverage and network capacity. In such an environment, mobility management is one of the major concerns that may limit the wide deployment and adoption of such networks. In this paper, we investigate the handover procedure for the two-tier macro/femto LTE networks. An optimized handover algorithm with an efficient call admission control has been proposed and described. Our proposed scheme is mainly designed to reduce the number of unnecessary handovers and to maintain the communication quality during the handover. The choice of the femtocell target takes into account the direction of the mobile user, its velocity and the quality of the signal. Performance evaluation results show that our algorithm minimizes both the number of hand-in and the handover drop rate. Besides, the signal quality in terms of SINR after the hand-in is maintained higher than a fixed threshold, which maximizes the sojourn time of the mobile user within the selected femtocell.

Cloud computing promises to provide on-demand computing, storage, and networking resources. However, most cloud providers simply offer virtual machines (VMs) without bandwidth and delay guarantees, which may hurt the performance of the deployed services. Recently, some proposals suggested remediating such limitation by offering virtual data centers (VDCs) instead of VMs only. However, they have only considered the case where VDCs are embedded within a single data center. In practice, infrastructure providers should have the ability to provision requested VDCs across their distributed infrastructure to achieve multiple goals including revenue maximization, operational costs reduction, energy efficiency, and green IT, or to simply satisfy geographic location constraints of the VDCs. In this paper, we propose Greenhead, a holistic resource management framework for embedding VDCs across geographically distributed data centers connected through a backbone network. The goal of Greenhead is to maximize the cloud provider's revenue while ensuring that the infrastructure is as environment-friendly as possible. To evaluate the effectiveness of our proposal, we conducted extensive simulations of four data centers connected through the NSFNet topology. Results show that Greenhead improves requests' acceptance ratio and revenue by up to 40 percent while ensuring high usage of renewable energy and minimal carbon footprint.

Wireless mesh networks (WMNs) are emerging as a key solution to provide broadband and mobile wireless connectivity in a flexible and cost-effective way. In suburban areas, a common deployment model relies on orthogonal frequency division multiple access (OFDMA) communications between mesh routers (MRs), with one MR installed at each user premises. In this paper, we investigate a possible user cooperation path to implement strategic resource allocation in OFDMA WMNs, under the assumption that users want to control their interconnections. In this case, a novel strategic situation appears: How much an MR can demand, how much it can obtain, and how this shall depend on the interference with its neighbors. Strategic interference management and resource allocation mechanisms are needed to avoid performance degradation during congestion cases between MRs. In this paper, we model the problem as a bankruptcy game taking into account the interference between MRs. We identify possible solutions from cooperative game theory, namely the Shapley value and the nucleolus, and show through extensive simulations of realistic scenarios that they outperform two state-of-the-art OFDMA allocation schemes, namely, centralized-dynamic frequency planning, and frequency-ALOHA. In particular, the nucleolus solution offers best performance overall in terms of throughput and fairness, at a lower time complexity.

This paper proposes a new joint power control and resource allocation algorithm in OFDMA femtocell networks. We consider both QoS constrained high-priority (HP) and best-effort (BE) users having different types of application and bandwidth requirements. Our objective is to minimize the transmit power of each femtocell, while satisfying a maximum number of HP users and serving BE users as well as possible. This optimization problem is multi-objective NP-hard. Hence, we propose a new scheme based on clustering and taking into account QoS requirements of users. We show by extensive network simulation results that our proposal outperforms three state of the art schemes (Centralized-Dynamic Frequency Planning, C-DFP, Distributed Random Access, DRA and Distributed Resource Allocation with Power Minimization, DRAPM as well as our previous proposal, FCRA, in both low and high density networks. The results concern the rate of rejected users, the throughput satisfaction rate, the spectrum spatial reuse, fairness, as well as computation time.

Femtocells have recently appeared as a viable solution to enable broadband connectivity in mobile cellular networks. Instead of redimensioning macrocells at the base station level, the modular installation of short-range access points can grant multiple benefits, provided that interference is efficiently managed. In the case where femtocells use different frequency bands than macrocells (i.e., split-spectrum approach), interference between femtocells is the major issue. In particular, congestion cases in which femtocell demands exceed the available bandwidth pose an important challenge. If, as expected, the femtocell service is going to be separately billed by legacy wire-line Internet Service Providers, strategic interference management and resource allocation mechanisms are needed to avoid performance degradation during congestion cases. In this paper, we model the resource allocation in cooperative femtocell networks as a bankruptcy game. We identify possible solutions from cooperative game theory, namely the Shapley value and the Nucleolus, and show through extensive simulations of realistic scenarios that they outperform two state-of-the-art schemes, namely Centralized-Dynamic Frequency Planning, C-DFP, and Frequency-ALOHA, F-ALOHA. In particular, the Nucleolus solution offers best performance overall in terms of throughput and fairness, at a lower time complexity.

Recently, operators have resorted to femtocell networks in order to enhance indoor coverage and increase system capacity. Nevertheless, to successfully deploy such solution, efficient resource allocation algorithms and interference mitigation techniques should be deployed. The new applications delivered by operators require large amounts of network bandwidth. Whereas, some customers may want to pay more in exchange for a better quality of service (QoS), some others need less resources and can be charged accordingly. Hence, we consider an OFDMA femtocell network serving both QoS constrained high-priority (HP) and best-effort (BE) users. Our objective is to satisfy a maximum number of HP users while serving BE users as well as possible. This optimization problem is multi-objective NP-hard. For this aim, we propose in this paper a new resource allocation and admission control algorithm, called Q-FCRA, based on clustering and taking into account QoS requirements. We show by extensive network simulation results that our proposal outperforms two state of the art schemes (Centralized-Dynamic Frequency Planning, C-DFP, and Distributed Random Access, DRA) as well as our previous proposal, FCRA, in both low and high density networks. The results concern the number of accepted users, the fairness, the throughput satisfaction rate and the spectrum spatial reuse.

Wireless Mesh Networks (WMNs) are emerging as a key solution to provide broadband and mobile wireless connectivity in a flexible and cost effective way. In suburban areas, a common deployment model relies on OFDMA communications between mesh routers (MRs), with one MR installed at each user premises. In this paper, we investigate a possible user cooperation path to implement strategic resource allocation in OFDMA WMNs, under the assumption that users want to control their interconnection. In this case, a novel strategic situation appears: how much a MR can demand, how much it can obtain and how this shall depend on the interference with its neighbors. Strategic interference management and resource allocation mechanisms are needed to avoid performance degradation during congestion cases between MRs. In this paper, we model the problem as a bankruptcy game taking into account the interference between MRs. We identify possible solutions from cooperative game theory, namely the Shapley value and the Nucleolus, and show that they outperform two state-of-the-art schemes, namely Centralized-Dynamic Frequency Planning, C-DFP, and Frequency-ALOHA, F-ALOHA. In particular, the Nucleolus solution offers best performance overall in terms of throughput and fairness.

Achieving efficient QoS routing is one of the major concerns in multi-radio multi-channel wireless mesh networks (WMNs). Due to interference among links, bandwidth in these networks is neither a concave nor an additive metric. Thus, choosing the highest-throughput path among all feasible ones is a very challenging task. We propose in this paper a new routing metric which strikes a balance between the quality of wireless links and the resulting inter-flow and intra-flow interference introduced using such links on specific channels. Our metric does not necessarily choose the best path in terms of throughput. It selects a high-throughput path with low inter-flow and intra-flow interference by making a judicious use of different channels. Simulation results show that our routing metric significantly outperforms previously proposed metrics especially in large-scale WMNs. An improvement up to 20% of the total network throughput is particularly observed when multiple concurrent flows are considered in the network.

In this paper, we propose a new routing protocol for home networks, called dependable routing protocol (DRP) that adapts to the changes in local topology within home networks environments. DRP is based on an effective selection of paths through which a packet must pass to reach the home unit. The selection, in such dynamic home networks, is made using dependable routing, i.e, in a way that maximizes the routes quality between the network nodes and the home unit while minimizing the Failure of Service (FoS). To minimize FoS, DRP maintains requirements on both the tolerable end-to-end delay (for time-sensitive routing) and the bit error rate (for reliable routing) within the network. To achieve this, we formulate the routing dependability problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the route quality as well as the delay and bit error rate of a route in a home network scenario are derived. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the home network performance when compared to one prominent routing protocol: the Minimum Total Transmission Power Routing scheme, MTPR.

Recently, operators have resorted to femtocell networks in order to enhance indoor coverage and quality of service since macro-antennas fail to reach these objectives. Nevertheless, they are confronted to many challenges to make a success of femtocells deployment. In this paper, we address the issue of resources allocation in femtocell networks using OFDMA technology (e.g., WiMAX, LTE). Specifically, we propose a hybrid centralized/distributed resource allocation strategy namely Femtocell Cluster-based Resource Allocation (FCRA). Firstly, FCRA builds disjoint femtocell clusters. Then, within a cluster the optimal resource allocation for each femtocell is performed by its cluster-head. Finally, the contingent collisions among different clusters are fixed. To achieve this, we formulate the problem mathematically as Min-Max optimization problem. Performance analysis shows that FCRA converges to the optimal solution in small-sized networks and outperforms two prominent related schemes (C-DFP and DRA) in large-sized ones. The results concern the throughput satisfaction rate, the spectrum spatial reuse, and the convergence time metrics.

In this paper, we propose a new Compression Cluster-based scheme in a Spatial Correlated Region (CC_SCR) for event-driven applications in wireless sensor networks. The main idea behind our proposal is to exploit the spatial correlation of such networks in order to reduce the size of the data packets that will be sent by means of data compression. The proposed clustering scheme is based on selecting a data value as reference while the rest of the active nodes transmit only the difference between their sensed value and this reference value. Hence, one major issue in the proposed mechanism is to appropriate select the reference node that achieves the highest reduction of the packet size among all active nodes. The CC_SCR protocol is evaluated by simulations. The results show that CC_SCR may reduce as much as 11 times the energy consumption compared to a classical clustering scheme.

In this paper, we address the radio resource utilization efficiency in single channel wireless mesh networks (WMNs) while considering the mobility of users and when multiple simultaneous connections on the same channel exist in the network. To achieve this, we use clustering. We first identify through analytical models and simulations the cases where clustering is helpful. Building on these results, we propose two clustering schemes that take into consideration the mobility properties of users in order to improve the WMN performance. We prove that both schemes can achieve significant gains in terms of radio resource utilization, especially when the number of simultaneous connections in the network increases. Specifically, we show that the first scheme fits better low-connected wireless mesh networks, whereas the second scheme is more suitable for highly-connected networks.

In this paper, we propose a new intersection-based geographical routing protocol, called delay tolerant routing protocol (DTRP) that adapts to the changes in the local topology within city environments. DTRP is based on an effective selection of road intersections through which a packet must pass to reach the gateway to the Internet. The selection, in such delay tolerant VANETs, is made in a way that maximizes the connectivity probability of the route between mobile nodes and the gateway while maintaining a threshold for the end-to-end delay and the hop count within the network. To achieve this, we formulate the QoS routing problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the connectivity probability as well as the delay and hop count of a route in a two-way road scenario are derived. Then, we propose a genetic algorithm to solve the optimization problem. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the VANETs performance when compared with several prominent routing protocols, such as GPSR, GPCR and OLSR.

This paper presents a new metric for routing in wireless mesh networks (WMNs). The proposed metric does not only consider the quality of wireless links to choose a high throughput path between a pair of nodes, but it also includes the resulting interference introduced by using such links. The philosophy behind this metric is to choose a good path for an arriving connection, not necessarily the best in terms of throughput, but that alleviates the resulting interference in order to preserve good paths for the subsequent arriving connections. In doing so, we find that our metric significantly outperforms previously proposed routing metrics when multiple concurrent flows are considered in the network. The total network throughput is indeed increased.

One of the major concerns in wireless mesh networks (WMNs) is the radio resource utilization efficiency, which can be enhanced by efficiently managing the mobility of users. To achieve this, we propose in this paper the use of mobility-aware clustering. The main idea behind WMN clustering is to restrict a major part of the exchanged signaling messages due to the mobility of users to a local area (i.e., inside a cluster). As such, less wireless links are used by the signaling messages, which reduces the resources occupied by a mobile user during its service and thus improves the total network capacity. Through analytical models and simulations, this work first identifies the cases where clustering is helpful. Building on these results, we propose two clustering schemes that take into consideration the mobility properties of the users in order to improve the WMN performance. We prove that both schemes can achieve significant gains in terms of radio resource utilization. Specifically, we show that the first scheme fits better both large and low-connected wireless mesh networks, whereas the second scheme is more suitable for both small to moderate and highly connected networks.

As progress in VANETs research continues, there is a persuasive need to support Quality of Service (QoS) routing in such networks. While greedy forwarding is used in many MANETs applications, it is found that it is not convenient for VANETs applications. In this paper, we investigate the important and difficult challenge of QoS routing in VANETs. First, we present an adaptive message routing protocol that uses up to date information about the local topology in order to find the route with minimum end-to-end delay while maintaining a threshold for the connectivity probability and hop count. Then, we propose a genetic algorithm to solve this. To do so, we formulate the QoS routing as a constrained optimization problem. We also derive analytical expressions for the delay as well as the connectivity probability of a route in a two-way street scenario. Numerical and simulation results show that our algorithm gives an optimal or near optimal solutions, which provides an interactive and effective design environment and enriches our protocol performance compared to GPCR.

Efficient location management is one of the major challenges in vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and the increase in their number, the location information updating and querying messages will consume the limited bandwidth of VANETs. This involves the development of a scalable and locality-aware location service management protocol. In this paper, we propose a promising solution called the modified region-based location service management protocol (MRLSMP), which utilizes the existing infrastructure on the road as a location management service entity. To evaluate the efficiency of our proposal, we compare our scheme with existing solutions using both analytical and simulation approaches. Specifically, we develop analytical models to evaluate the total control overhead. Numerical and simulation results show that our protocol scales better than existing schemes, when increasing the size of VANETs which enhances the feasibility of such large scale ad hoc networks.

Efficient mobility management is one of the major challenges for next-generation mobile systems. Indeed, a mobile node (MN) within an access network may cause excessive signaling traffic and service disruption due to frequent handoffs. The two latter effects need to be minimized to support quality of service (QoS) requirements of emerging multimedia applications. In this paper, we propose a new adaptive micromobility management scheme designed to track efficiently the mobility of nodes so as to minimize both handoff latency and total signaling cost while ensuring the MN's QoS requirements. We introduce the concept of residing area. Accordingly, the micromobility domain is divided into virtual residing areas where the MN limits its signaling exchanges within this local region instead of communicating with the relatively far away root of the domain at each handoff occurrence. A key distinguishing feature of our solution is its adaptive nature since the virtual residing areas are constructed according to the current network state and the QoS constraints. To evaluate the efficiency of our proposal, we compare our scheme with existing solutions using both analytical and simulation approaches for the 2-D random walk model as well as real mobility patterns. Numerical and simulation results show that our proposed scheme can significantly reduce registration updates and link usage costs and provide low handoff latency and packet loss rate under various scenarios.

One of the major challenges for vehicular ad hoc networks (VANETs) is related to efficient location management issue. In this paper, we propose a new region-based location service management protocol (RLSMP) that uses mobility patterns as means to synthesize vehicle movement and thus can be used in VANETs applications. One of the key distinguishing features of our solution from existing literature is its scalability since it uses message aggregation in both updating and querying, and promises locality awareness as well as minimum signaling overhead. To evaluate the efficiency of our proposal, we compare our scheme with existing solutions using both analytical and simulation approaches. To achieve this, we develop analytical models to evaluate the location updates cost. Numerical and simulation results show that our protocol scales better than existing schemes, when increasing the size of VANET which enhances the feasibility of such large scale ad hoc networks.

In this paper, we propose a new adaptive MPLS-enabled micro-mobility management scheme designed to track efficiently the mobility of nodes so as to minimize both handoff latency and total signaling cost while ensuring the mobile node's QoS requirements. To achieve this, we introduce a new concept called residing area. Accordingly, the micro-mobility domain is divided into virtual residing areas where the MN limits its signaling exchanges within this local region instead of communicating with the relatively far away root of the domain at each handoff occurrence. One of the key distinguishing features of our solution from existing literature is its adaptive nature since the virtual residing areas are constructed according to the current network state and the QoS constraints. To evaluate the efficiency of our proposal, we compare our scheme with existing solutions using both analytical and simulation approaches. Numerical and simulation results show that our proposed scheme can significantly reduce registration updates and link usage costs and provide low handoff latency under various scenarios.

International audience

One of the major challenges for the wireless networks is related to efficient mobility management issue. In this paper, we propose a new micro-mobility management scheme, called Micro Mobile MPLS, that supports both mobility and quality-of-service (QoS) management in cellular networks. Our proposal includes two protocol variants. In the first variant, called FC-Micro Mobile MPLS, the forwarding chain (FC) concept is provided to track efficiently the host mobility within a domain. This concept fits mobile nodes (MNs) with high mobility rate. The second protocol variant, called Master Forwarding Chain (MFC)-Micro Mobile MPLS, aims to reduce the total signaling cost by controlling the number of registration updates with the root of the domain. In order to assess the efficiency of our proposals, the aforesaid protocols are compared with respect to the existing solutions. To achieve this, we develop analytical models to evaluate both registration updates and link usage costs. Numerical and simulation results show that the proposed mechanisms can significantly reduce the registration updates cost and provide low handoff latency and packet loss rate under various scenarios.

In this paper, we propose an analytical approach to study the handoff performance of a wireless system with a new micro-mobility support called Micro Mobile MPLS. Our scheme includes two protocol variants. First, the fast handoff process, which anticipates the LSP procedure setup with an adjacent neighbor subnet that an mobile node (MN) is likely to visit, is provided to reduce service disruption. Second, a new mechanism based on the forwarding chain concept is proposed to track efficiently the host mobility whithin a domain. This concept can significantly reduce the registration updates cost and provide low handoff latency and packet loss rate. Analytical models, which capture the mobility behavior of an MN, are developed for cost analysis. Numerical and simulation results are given to justify the benefits of our proposed mechanisms.

Multi-protocol label switching (MPLS) is deployed in the Internet backbone to support service differentiation and traffic engineering. In recent years, there has been an interest to extend the MPLS capability to the wireless access networks for mobility management support. In this paper, we present an analytical approach to study the handoff performance of a wireless system with a new micro-mobility support called micro mobile MPLS. Our scheme includes two protocol variants. In the first variant called FH-micro mobile MPLS, we consider the fast handoff mechanism, which anticipates the LSP procedure setup with an adjacent neighbor subnet that an mobile node (MN) is likely to visit. This mechanism is proposed to reduce service disruption by using the link-layer (L2) functionalities. In the second variant called FC-micro mobile MPLS, the forwarding chain concept, which is a set of forwarding path, is provided to track efficiently the host mobility within a domain. This concept can significantly reduce the resource reservation cost, the registration updates cost and provide low handoff latency. Analytical models, which capture the mobility behavior of an MN, are developed for cost analysis. Numerical and simulation results are given to justify the benefits of our proposed mechanisms

One of the major challenges for the wireless networks is related to efficient mobility management issue. In this paper, we propose a new micro-mobility management scheme, called micro mobile MPLS, that supports both mobility and quality-of-service (QoS) management in cellular networks. Our proposal includes two protocol variants. In the first variant, called FC-micro mobile MPLS, the forwarding chain (FC) concept is provided to track efficiently the host mobility within a domain. This concept fits mobile nodes (MNs) with high mobility rate. The second protocol variant, called master forwarding chain (MFC)-micro mobile MPLS, aims to reduce the total signaling cost by controlling the number of registration updates with the root of the domain. In order to assess the efficiency of our proposals, all underlying protocols are compared through simulations using both two-dimensional (2-D) and one-dimensional (1-D) mobility models. Simulation results show that our proposed mechanisms can significantly reduce the registration updates cost and provide low handoff latency and packet loss rate under various scenarios

In this paper, we propose a practical approach to address how micro-mobility can be provided in an efficient way with continuous quality of service (QoS) support. Our proposal is based on multiprotocol label switching (MPLS) and mobile IP and relies on two-level hierarchy architecture. It supports two protocol variants. First, the fast handoff process, which anticipates the LSP procedure setup with an adjacent neighbor subnet that a mobile node (MN) is likely to visit, is provided to reduce service disruption. Second, a new mechanism based on a master forwarding chain concept is proposed to track efficiently the host mobility within a domain. Our concept can significantly reduce the total signaling cost and provide low handoff latency. This can be achieved by dynamically controlling the number of registration updates with the root of the domain according to a cost comparison. Analytical models are developed and simulations are conducted to justify the benefits of our proposed mechanisms

This article presents the micro mobile MPLS scheme, which is a new proposal for IP local mobility in wireless MPLS access networks. Our proposal is based on multiprotocol label switching (MPLS) and mobile IP and relies on two-level hierarchy architecture. With micro mobile MPLS, mobile nodes (MNs) do not require changes to their IP protocol stack, neither for micro-mobility nor macro-mobility. Nevertheless, it still uses Mobile IP for macro-mobility scenarios. The main features of our scheme are related on scalability, flexibility, fast handoff and the ability to provide quality of service (QoS) by using the underlying MPLS traffic engineered paths. Simulation results are given to justify the benefits of our proposed architecture.

In this paper, we propose a practical approach to address how micro-mobility can be provided in an efficient way with continuous quality of service (QoS) support. Our proposal is based on multiprotocol label switching (MPLS) and mobile IP and relies on two-level hierarchy architecture. It supports two protocol variants. First, the fast handoff process, which anticipates the LSPs procedure setup with the neighboring locations where a mobile node (MN) may move to, is provided to reduce the service disruption. Second, a new mechanism based on the forwarding chain concept is proposed to track efficiently the host mobility within a domain. This concept can significantly reduce the registration updates cost and provide low handoff latency. Analytical models are developed and simulations are conducted to justify the benefits of our proposed mechanisms