Abdellatif Kobbane

Lightweight Mobile Core Networks for Machine Type Communications

Machine type communications (MTCs) enable the communications of machines (devices) to machines over mobile networks. Besides simplifying our daily lives, the MTC business represents a promising market for mobile operators to increase their revenues. However, before a complete deployment of MTC over mobile networks, there is need to update the specifications of mobile networks in order to cope with the expected high number (massive deployment) of MTC devices. Indeed, large scale deployment of MTC devices represents an important challenge as a high number of MTC devices, simultaneously connecting to the mobile network, may cause congestion and system overload, which can degrade the network performance and even result in network node failures. Several activities have been led by 3GPP to alleviate system overload introduced by MTC. Most of the devised approaches represent only incremental solutions. Unlike these solutions, we devise a complete new architectural vision to support MTC in mobile networks. This vision relies on the marriage of mobile networks and cloud computing, specifically exploiting recent advances in network function virtualization (NFV). The aim of the proposed vision, namely LightEPC, is: 1) to orchestrate the on-demand creation of cloud-based lightweight mobile core networks dedicated for MTC and 2) to simplify the network attach procedure for MTC devices by creating only one NFV MTC function that groups all the usual procedures. By doing so, LightEPC is able to create and scale instances of NFV MTC functions on demand and in an elastic manner to cope with any sudden increase in traffic generated by MTC devices. To evaluate LightEPC, some preliminary analysis were conducted and the obtained analytical results indicate the ability of LightEPC in alleviating congestion and scaling up fast with massive numbers of MTC devices in mobile networks. Finally, a real-life implementation of LightEPC on top of cloud platform is discussed.

Data traffic-based analysis of delay and energy consumption in cognitive radio networks with and without resource reservation

A new opportunistic cross-layer MAC protocol involving channel allocation and packet scheduling for cognitive radio networks is proposed. Cognitive radio allows secondary users SUs to exploit the available portions of the licensed spectrum bands without interfering with primary users. In particular, we consider a cognitive radio system, where SUs are equipped with two transceivers: a control transceiver and a software-defined radio transceiver. Data traffic characteristics of SUs are considered to ameliorate system performance. So, we propose a mechanism of resource reservation to improve QoS requirements that favors successful SUs to transmit data during x time slots without interfering with primary users. The key novelty of this paper is giving priority for SUs with important data traffic and which frequently solicits data channels to transmit for the remaining time of the ongoing time slot and for the next time slots directly after checking the channel availability. We develop a new analytical model to evaluate delay parameter for two scenarios with and without resource reservation and we then investigate the impact of those scenarios on the energy consumption. We show through simulations that cognitive radio performances increase noticeably with the proposed scheme. Copyright

A decentralized network selection algorithm for group vertical handover in heterogeneous networks

The traditional vertical handover schemes postulate that vertical handover of each user comes on an individual basis. This enables the users to know previously the decision already made by other users, and then the choice will be made accordingly. However, in the case of a group vertical handover, almost all the VHO decisions - which will certainly choose the best network, will be made at the same time which will lead to system performance degradation or network congestion. In this paper, we propose a totally decentralized algorithm for network selection which based on the Congestion Game to resolve the problem of network congestion in GVHO. Therefore, the proposed algorithm named Fully Decentralized Nash Learning Algorithm with incomplete information is a prediction done by each mobile in the group that helps them to reach the Nash equilibrium. Simulation results validate the algorithm and show its robustness under two scenarios. In the first one, we examine the algorithm with a fixed number of mobiles in group to evaluate the mixed strategy and the average perceived throughput of mobiles in WIMAX and HSDPA on the basis of iteration. In the second one, we examine the algorithm with different number of mobiles in group for testing the average number of iterations needed to reach the Nash equilibrium. We also compare it with the traditional vertical handover algorithm.

Green opportunistic access for cognitive radio networks: A minority game approach

We investigate energy conservation and system performance of decentralized resource allocation scheme in cognitive radio networks thoroughly based on secondary users competitive behavior. Indeed, the contention on data channel unoccupied by licensed user leads to a single winner, but also involves a loss of energy of all nodes. In this paper, we apply minority game (MG) to the most important phase from the opportunistic spectrum access (OSA) process: the sensing phase. We attempt to carry out a cooperation in a non-cooperative environment with no information exchange. We study the Nash equilibrium solution for pure and fully mixed strategies, and we use distributed learning algorithms enabling cognitive users to learn the Nash equilibrium. Finally, we provide numerical results to validate the proposed approach. The resource allocation based on minority game approach improves secondary users battery life and the performance of the network.

New insights from a delay analysis for cognitive radio networks with and without reservation

In wireless communication systems, the delay remains a crucial factor. In this work, we focus on the design of a new opportunistic cross-layer MAC protocol involving channels allocation and packet scheduling for cognitive networks in order to optimize system performance. Cognitive radio provides the opportunity for secondary users (unlicensed users) to use available portions of the licensed spectrum bands without interfering with primary users (licensed users). We consider that each secondary user is equipped with two transceivers. The role of the first transceiver is to obtain and exchange channels information on the control channel. The second transceiver is devoted to periodically detect and dynamically use the available data channels. The paper deals with channel allocation considering traffic characteristics of secondary users. So, we propose a mechanism of resource reservation to improve Quality of Service (QoS) requirements that favors successful secondary users to transmit data during x time slots without interfering with primary users. We develop a new analytical model, while taking into account the backoff mechanism. We analyze delay parameter for two scenarios with and without resource reservation. We show through simulations that our approach guarantees an optimal response delay.

Dynamic power control for energy harvesting wireless multimedia sensor networks

Optimization of energy usage in wireless sensor networks (WSN) has been an active research field for the last decades and various approaches have been explored. In fact, A well designed energy consumption model is the foundation for developing and evaluating a power management scheme in network of energy constrained devices such as: WSN. We are interested in developing optimal centralized power control policies for energy harvesting wireless multimedia sensor networks (WMSN) equipped with photovoltaic cells. We propose a new complete information Markov decision process model to characterize sensors battery discharge/recharge process and inspect the structural properties of optimal transmit policies.

A coalitional-game-based incentive mechanism for content caching in heterogeneous Delay Tolerant Networks

In recent years, Delay Tolerant Networks (DTNs) have successfully presented as a possible extension of the Internet architecture in order to provide communication support to existing networks. However, these networks have a major issue which is the coordination among relays. In this work, we study the cooperative transmission for DTNs using coalitional game theory. We design a new incentive mechanism for heterogeneous system to induce coordination among DTN relays. In particular, we focus on the source packet dissemination to a destination using tow-hop relaying scheme, considering networks resource constraints: the relay buffer, the packet life time, and the energy consumption according to the mobile technologies. Rational mobiles are organized into separate coalition structures to meet a trade off between the source reward and the energy conservation. We discus the Nash equilibria for our game and the stable strategy state in which no mobile can get a higher payoff through changing unilaterally its coalition. Then, we use the distributed imitative Boltzmann-Gibbs learning algorithm enabling relays to learn the Nash equilibrium strategy; grand coalition. The improvement of the global system performance is examined, and a comparison between different inter-node collaboration states is presented.

The greening of spectrum sensing: a minority game-based mechanism design

Cognitive radio technology allows the reuse of the underutilized frequency spectrum on an opportunistic and non-interfering basis by means of introducing, besides the legitimate primary users of the spectrum, a new kind of users called cognitive or secondary users. Thus, reliable spectrum sensing is critical to dynamically detect available licensed frequency bands and mitigate the primary signals, but it remains realistically difficult to carry out. In fact, although distributed collaborative sensing has turned out to be fruitful for the cognitive radio environment, its accuracy is often affected by the selfish and autonomous behavior of users. In this article, we model distributed spectrum sensing and channel allocation as a non-cooperative game, and apply the minority game to bring forth and study the cooperative behavior of users. The novelty brought by our study consists of alleviating the number of users contending for primary channels by giving them the opportunity to choose between the two, either sensing the channel or being inactive during the time slot. To address the trade-off faced by the SUs, we evaluate the performance of two secondary systems in a green communications context: energy consumption and transmission delay.

Optimal distributed relay selection for duty-cycling Wireless Sensor Networks

Recent advances in localization technologies and algorithms for Wireless Sensor Networks (WSN) motivate the exploitation of location information in routing protocols. In this paper we consider the geographic forwarding of sporadically generated alarm messages. Our objective is to optimize sensors energy consumption while respecting QoS constraints on transmission delay. For instance, we propose an optimal distributed relay selection policy for WSN with duty-cycling sensors based on a Markov Decision Process (MDP) with complete information. Also, we establish sufficient conditions for optimality of threshold policies. Then, end-to-end performances for a heuristic multi-hop relay selection strategy are established. Finally, we extend our model to account for queuing capabilities at sensor level.

Coalitional game-based behavior analysis for spectrum access in cognitive radios

The core of cognitive radio paradigm is to introduce cognitive devices able to opportunistically access the licensed radio bands. The coexistence of licensed and unlicensed users prescribes an effective spectrum hole-detection and a non-interfering sharing of those frequencies. Collaborative resource allocation and spectrum information exchange are required but often costly in terms of energy and delay. In this paper, each secondary user SU can achieve spectrum sensing and data transmission through a coalitional game-based mechanism. SUs are called upon to report their sensing results to the elected coalition head, which properly decides on the channel state and the transmitter in each time slot according to a proposed algorithm. The goal of this paper is to provide a more holistic view on the spectrum and enhance the cognitive system performance through SUs behavior analysis. We formulate the problem as a coalitional game in partition form with non-transferable utility, and we investigate on the impact of both coalition formation and the combining reports costs. We discuss the Nash Equilibrium solution for our coalitional game and propose a distributed strategic learning algorithm to illustrate a concrete case of coalition formation and the SUs competitive and cooperative behaviors inter-coalitions and intra-coalitions. We show through simulations that cognitive network performances, the energy consumption and transmission delay, improve evidently with the proposed scheme. Copyright