Mohammed Elkoutbi

An energy aware algorithm for OLSR clustering

One of the important aspects of a mobile ad hoc network (MANET) is the limitation of the amount of available energy and the network lifetime. The tremendous amount of using mobile nodes in wireless communication medium makes energy efficiency a fundamental requirement for MANETs. In this paper, we propose a novel energy aware clustering algorithm for the optimized link state routing (OLSR) routing protocol. This algorithm takes into account the node density and mobility and gives major improvements regarding the number of elected cluster heads. Our objective is to elect a reasonable number of cluster heads that will serve for hierarchical routing based on OLSR. The proposed algorithm aims to increase the network lifetime by considering the ad hoc residual energy while taking routing decisions. It also optimizes the delay of carried flows by adopting a selective forwarding approach based on a hierarchical routing model.

Modeling and evaluating a cloudlet-based architecture for Mobile Cloud Computing

With the rising popularity of Internet-enabled mobile devices, users are increasingly demanding better quality of service (QoS). However, the resources of these devices and their connectivity levels remain insufficient, even though they are improving, for offering acceptable levels of QoS to users. Cloud computing infrastructures offer large and scalable resources that allow shifting the physical location of computation and storage to the cloud. Nevertheless, the integration of mobile computing with cloud computing would not guarantee adequate levels of service for mobile users. It rather delivers scalability at the cost of higher delay and higher power consumption on the mobile device. Instead, using local resources based on users geographical locations has the potential to improve the performance and QoS for mobile users. In this paper, we present and study a centralized architecture that relies on the concept of local clouds, cloudlets, to leverage the geographical proximity of resources to mobile users and offer them a better user experience. We use a continuous time Markov-chain (CTMC) to model the different nodes of the architecture: user nodes, cloudlets, and the main cloud. We estimate the delay incurred in the proposed architecture by simulating search engine queries generated by mobile users using the CTMC state models. Initial simulation results show that the usage of a cloudlet-based architecture especially centralized architecture has an efficient gains in terms of latency delay and synchronisation mechanisms.

New Method: Mapping of 802.11e into MPLS Domains, Conception and Experimentation

Mapping between two heterogeneous networks, Multi Protocol Label Switching (MPLS) and IEEE 802.11e is choice of future technology as it has ability to perform traffic engineering and create the corresponds between LSP (Label Switch Path) and AC (Access Category). This paper discusses the mapping between IEEE 802.11 MPLS integration to achieve quality of service on MPLS networks. MPLS and 802.11e is very useful approach for today’s internet to ensure the Quality of service the end to end. It talks about different approaches to map 802.11e Access Categories (AC) to Label Switched Path (LSP) and their advantages. It then introduces the concept of encapsulated LSPs to achieve future QoS, which requires further study to examine its practicability.

A Bargaining Nash Game Based Adaptive Negotiation of Context Level Agreements for Pervasive Systems

With the growing popularity of Internet-enabled devices, the impressive progress in sensing technology, and the adoption of cloud computing for provisioning services, users increasingly demand services that can adapt to their recent context. In this paper, we propose a multi-attributes and adaptive approach for Context Level Agreements (CLAs) negotiation between a context provider and a context consumer using a context broker. The approach employs a Nash bargaining model and evaluates the global utility of each party as a linear function of normalized Quality of Context (QoC) attributes during the rounds of negotiation. The ultimate goal is to improve context-based adaptation of context-aware applications and services. One of the advantages of this approach is that it permits to resolve conflicts of interests between the context provider and the context consumer when the global utility of each party reaches a Pareto optimum.

A new trust model to secure routing protocols against DoS attacks in MANETs

In mobile Ad hoc networks, data is transmitted in airspace between mobile entities without any centralized control. This environment offers many and very important services for mobile users. However, the level of security in this kind of network has not yet reached a significant level of maturity against the ferocious attacks. In this work, we present a new model to trust routing protocols communications in MANETs against DoS attacks. We have used the box plot theory to define accurately the threshold to detect a DoS attack and to reduce the number of false positives.

A signaling game-based approach for Data-as-a-Service provisioning in IoT-Cloud

Abstract The impressive progress in sensing technology over the last few years has contributed to the proliferation and popularity of the Internet of Things (IoT) paradigm and to the adoption of Sensor Clouds for provisioning smart ubiquitous services. Also, the massive amount of data generated by sensors and smart objects led to a new kind of services known as Data-as-a-Service (DaaS). The quality of these services is highly dependent on the quality of sensed data (QoD), which is characterized by a number of quality attributes. DaaS provisioning is typically governed by a Service Level Agreement (SLA) between data consumers and DaaS providers. In this work, we propose a game-based approach for DaaS Provisioning, which relies on signaling based model for the negotiation of several QoD attributes between DaaS providers and data consumers. We consider that these entities are adaptive, rational, and able to negotiate the QoD offering even in the case of incomplete information about the other party. We use in the negotiation between the two parties a Q-learning algorithm for the signaling model and a Multi Attributes Decision Making (MADM) model to select the best signal. Moreover, we empirically validate the MADM model using Shannon’s entropy. The results obtained in the case of a multi-stages negotiation scenario show the convergence towards the pooling equilibrium.