Nadjib Aitsaadi

Cluster-Based Resource Management in OFDMA Femtocell Networks With QoS Guarantees

Recently, operators have resorted to femtocell networks 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. Although, some customers may want to pay more in exchange for a better quality of service (QoS), some others need fewer resources and can be charged accordingly. Hence, we consider an orthogonal frequency-division multiple-access (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 multiobjective optimization problem is NP-hard. For this aim, we propose in this paper a new resource-allocation and admission control algorithm, which is called QoS-based femtocell resource allocation (Q-FCRA), based on clustering and taking into account QoS requirements. We show through extensive network simulations that our proposal outperforms two state-of-the-art schemes [centralized-dynamic frequency planning (C-DFP) and distributed random access (DRA)] and our previous proposal, i.e., femtocell resource allocation (FCRA), in both low- and high-density networks. The results concern the throughput satisfaction rate, spectrum spatial reuse, the rate of rejected users, fairness, and computation and convergence time.

A Tabu Search WSN Deployment Method for Monitoring Geographically Irregular Distributed Events

In this paper, we address the Wireless Sensor Network (WSN) deployment issue. We assume that the observed area is characterized by the geographical irregularity of the sensed events. Formally, we consider that each point in the deployment area is associated a differentiated detection probability threshold, which must be satisfied by our deployment method. Our resulting WSN deployment problem is formulated as a Multi-Objectives Optimization problem, which seeks to reduce the gap between the generated events detection probabilities and the required thresholds while minimizing the number of deployed sensors. To overcome the computational complexity of an exact resolution, we propose an original pseudo-random approach based on the Tabu Search heuristic. Simulations show that our proposal achieves better performances than several other approaches proposed in the literature. In the last part of this paper, we generalize the deployment problem by including the wireless communication network connectivity constraint. Thus, we extend our proposal to ensure that the resulting WSN topology is connected even if a sensor communication range takes small values.

Differentiated Underwater Sensor Network Deployment

Underwater sensors technology has experienced a tremendous development over the last decade. They have generated considerable community enthusiasm, which applied them to a wide range of cases that could enhance human and animal habitat conditions e.g. climate changes prediction or pollution surveillance. In this paper, we address the issue of deploying a UWSN in an area characterized by the geographical irregularity of the sensed event. Our specific application context regards the monitoring of the water quality in a small closed area (e.g. a lake). For this purpose, we propose a differentiated deployment algorithm (DDA) based on a mesh representation method inspired from the image processing and 3D modeling field.

Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints

In this paper, we address a wireless sensor network deployment problem. It is considered when the deployment field is characterized by a geographical irregularity of the monitored event. Each point in the deployment area requires a specific minimum guarantee of event detection probability. Our objective is to generate the best network topology while minimizing cost of deployment, ensuring quality of monitoring and network connectivity, and optimizing network lifetime. The problem is formulated as combinatorial optimization problem, which is NP-complete. Unfortunately, due to the large solution state space and the exponential computational complexity, the exact methods can be applied only in the case of small-scale problem. To overcome the complexity of an optimal resolution, we propose new scalable deployment heuristics based on artificial potential field and Tabu search metaheuristic, namely potential field deployment algorithm (PFDA) and multi-objective deployment algorithm (MODA). We compare our proposal to the related deployment strategies, the obtained results show that PFDA and MODA obtain the best performances.

Multi-Objective WSN Deployment: Quality of Monitoring, Connectivity and Lifetime

In this paper, we will address a WSN deployment problem. The main objectives are i) reduce the cost of deployment, ii) ensure the requested event detection probabilities, iii) guarantee the network connectivity, and iv) maximize the lifetime of the network. We will formalize the problem as multi-objective combinatorial optimization problem. To resolve the problem, we will propose a new deployment algorithm named \texttt{MODA}. It will be based on evolutionary and neighborhood search algorithms. The obtained results are better than the deployment strategies found in the literature.

QoS-based power control and resource allocation in OFDMA femtocell networks

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.

Q-FCRA: QoS-based OFDMA femtocell resource allocation algorithm

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.

Potential Field Approach to Ensure Connectivity and Differentiated Detection in WSN Deployment

This paper addresses the issue of Wireless Sensor Network (WSN) deployment. We investigate this problem in the case where the monitored area is characterized by a geographical irregularity of the sensed events. Precisely, we consider that each point of the deployment area requires a minimum threshold guarantee on the event detection probability. Our proposed scalable deployment method, named Potential Field-based Deployment Algorithm (PFDA), is based on the Potential Field and the Virtual Force approaches. Our proposal is able to (1) satisfy the required event detection probability threshold for each point, in a large-scale area, while minimizing the number of deployed sensors and (2) to ensure the network connectivity. The results and evaluation analysis show that PFDA outperforms the other strategies proposed in literature.

Cloud networking: An overview of virtual network embedding strategies

This paper is an overview of virtual network embedding strategies within cloud infrastructure backbone networks. We will first of all summarise and define the main principles of cloud computing and its different supplied services. Then, we will describe the cloud infrastructure architecture, named the Totally Virtualized Cloud Infrastructure (TVCI), which makes use of virtualization in all the clouds equipment (i.e., backbone and data centers). Afterwards, we will describe, analyse, and compare the main virtual network mapping algorithms for cloud infrastructure networks found in existing literature. We will evaluate the above strategies in terms of: i) virtual network request reject rate, ii) embedding cost of virtual network request, iii) embedding revenue of virtual network request and iv) average usage rate of physical resources.

Dynamic resource allocation for Cloud-RAN in LTE with real-time BBU/RRH assignment

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.