Lyes Khoukhi

Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks

In this study, we aim to cover a sensing area by deploying a minimum number of wireless sensors while maintaining the connectivity between the deployed sensors. The problem may be reduced to a two-dimensional critical coverage problem which is an NP-Complete problem. We develop an integer linear programming model to solve the problem optimally. We also propose a local search (LS) algorithm and a genetic algorithm (GA) as approximate methods. We verify by computational experiments that the integer linear model, using Cplex, is able to provide an optimal solution of all our small and medium size problems. We also show that the proposed methods outperform some regular sensor deployment patterns.

SCRP: Stable CDS-Based Routing Protocol for Urban Vehicular Ad Hoc Networks

This paper addresses the issue of selecting routing paths with minimum end-to-end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks (VANETs). Most existing schemes aim at reducing E2ED via greedy-based techniques (i.e., shortest path, connectivity, or number of hops), which make them prone to the local maximum problem and to data congestion, leading to higher E2ED. As a solution, we propose SCRP, which is a distributed routing protocol that computes E2ED for the entire routing path before sending data messages. To do so, SCRP builds stable backbones on road segments and connects them at intersections via bridge nodes. These nodes assign weights to road segments based on the collected information of delay and connectivity. Routes with the lowest aggregated weights are selected to forward data packets. Simulation results show that SCRP outperforms some of the well-known protocols in literature.

Intelligent QoS management for multimedia services support in wireless mobile ad hoc networks

In this paper, we propose a new intelligent cross-layer QoS support for wireless mobile ad hoc networks. The solution, named FuzzyQoS, exploits fuzzy logic for improving traffic regulation and the control of congestion to support both real-time multimedia (audio/video) services and non-real-time traffic services. FuzzyQoS includes three contributions: (1) a fuzzy logic approach for best-effort traffic regulation (FuzzyQoS-1), (2) a new fuzzy Petri nets technique (FuzzyQoS-2) for modeling and analyzing the QoS decision making for traffic regulation control, and (3) a fuzzy logic approach for threshold buffer management (FuzzyQoS-3). In FuzzyQoS-1, the feedback delay information received from the network is used to perform a fuzzy regulation for best-effort traffic. Using fuzzy logic, FuzzyQoS-3 uses fuzzy thresholds to adapt to the dynamic conditions. The evaluation of FuzzyQoS performances was studied under different mobility, channel, and traffic conditions. The results of simulations confirm that a cross layer design using fuzzy logic at different levels can achieve low and stable end-to-end delay, and high throughput under different network conditions. These results will benefit delay- and jitter-sensitive real-time services.

Smart Grid Solution for Charging and Discharging Services Based on Cloud Computing Scheduling

Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of electric vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for SG and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for SG based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging–discharging loads at peak-hours period.

Queuing model for EVs charging at public supply stations

As electric vehicles become more popular, public charging stations for such vehicles will become common. Since the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the waiting time for users to have their vehicles charged. In this paper, we present an approach for balancing the load among charging stations in an area while minimizing the charging time of electric vehicles. We propose a model where vehicles communicate beforehand with the grid to convey information about their charging status, and develop a mathematical model of handling requests for charging vehicles at public charging station based on queuing theory. Finally, we propose an algorithm for directing vehicles to charging stations in a way to minimize their waiting time to charge completion. The simulation results show the effectiveness of the proposed approach when considering both real electric vehicle and charging station characteristics and constraints.

Towards secure route discovery protocol in MANET

Wireless Mobile ad hoc network (MANET) has become an exciting and important technology in recent years because of a rapid proliferation of wireless devices. MANET is a self-organizing network of wireless links connecting mobile nodes. MANETs technology offers both new challenges and opportunities for many applications. One of the major challenges for ad hoc technology is routing security, due essentially to MANET features (e.g., open medium, lack of centralized management, nodes mobility). In this paper, we propose ASRP, an efficient secure routing protocol to ensure the routing security in ad hoc networks. ASRP provides powerful security extensions to the reactive AODV protocol, based on modified secure remote password protocol and Diffie-Hellman (DH) algorithms. The simulation results show the efficiency of the proposed ASRP protocol, and its cost towards both the users and the network.

Admission control in wireless ad hoc networks: a survey

Wireless mobile ad hoc networks (MANETs) have emerged as a key technology for next-generation wireless networking. Because of their advantages over other wireless networks, MANETs are undergoing rapid progress and inspiring numerous applications. However, many technical issues are still facing the deployment of this technology, and one of the most challenging aspects is the quality of service (QoS) provisioning for multimedia real-time applications. MANETs are expected to offer a diverse range of services to support real-time traffic and conventional data in an integrated fashion. Because of the diversified QoS requirements of these services, QoS models are needed for an efficient usage of network resources. One of the most crucial mechanisms for providing QoS support is admission control (AC). AC has the task of estimating the state of networks resources and thereby to decide which application data flows can be admitted without promising more resources than are available and thus violating previously made guarantees. In order to provide a better understanding of the AC research challenges in MANETs, this paper presents a detailed investigation of current state-of-the-art AC models in ad hoc networks. A brief outline of the admission function, feedback to route failures, as well as the advantages and drawbacks of each discussed model are given.

Traffic adaptation in wireless mesh networks: Fuzzy-based model

The emergence of real-time applications and their widespread usage in communication have generated the need to provide quality-of-Service (QoS) support in wireless networks environments. One of the most crucial mechanisms of a model for providing QoS support is the traffic regulation. In the aim of better representing and analyzing the decision making policy of the traffic adaptation process in wireless mesh networks (WMN), we propose a novel model named FuzzyWMN. The proposed model combines the essential notions of both fuzzy logic theory and Petri nets; this enables FuzzyWMN to achieve the traffic adaptation process in the context of dynamic network events characterized by the uncertainty and imprecision information, due to the dynamic traffic behavior, channels interference, etc. The evaluation of FuzzyWMN performances, compared to AIMD-SWAN and IEEE 802.11, was studied under different network and traffic conditions. The promising results obtained from extensive simulations confirm that the traffic adaptation based on the fuzzy design can achieve stable end-to-end delay, and good throughput under different network conditions.

A quality of service approach based on neural networks for mobile ad hoc networks

In this paper, we propose an intelligent quality of service (QoS) model named GQOS, with service differentiation based on neural networks in mobile ad hoc networks. The model is composed of two plans: the GQOS kernel plan and the intelligent learning plan. New mechanisms have been developed and integrated in the kernel plan in order to ensure the detection and recovery of QoS violations. The intelligent learning plan performs the training of GQOS kernel operations by using a multilayered feedforward neural network. Simulation results show that our model outperforms the SWAN model by about 10% in terms of average delay and throughput at lower and medium mobility.

Decentralized Cloud-SDN Architecture in Smart Grid: A Dynamic Pricing Model

Smart grids (SG) energy management system and electric vehicle (EV) have gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real-time price and demand–supply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on software define networking (SDN) technology and network function virtualization (NFV). We aim to schedule users requests in a real-time way and to supervise communications between microgrids controllers, SG and user entities (i.e., EVs, electric vehicles public supply stations, advance metering infrastructure, smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all microgrids. We solve the problems by using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized Cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximizes EVs utility, and maintains the microgrid stability. The simulation is based on real electric load of the city of Toronto.