Miklós Molnár

Bio: Miklós Molnár is an academic researcher from University of Montpellier. The author has contributed to research in topics: Multicast & Xcast. The author has an hindex of 16, co-authored 88 publications receiving 743 citations. Previous affiliations of Miklós Molnár include Institut de Recherche en Informatique et Systèmes Aléatoires & University of Rennes.

Leveraging Light Forest With Rateless Network Coding to Design Efficient All-Optical Multicast Schemes for Elastic Optical Networks

Abstract: In this paper, we study the multicast-capable routing, modulation, and spectrum assignment (MC-RMSA) schemes that consider the physical impairments from both the transmission and light splitting in elastic optical networks (EONs). Specifically, we propose to provision each multicast request with a light forest, which consists of one or more light trees to avoid the dilemma that because of the accumulated physical impairments, a relatively large light tree may have to use the lowest modulation level, and, hence, consume too many frequency slots (FS'). In order to further improve the spectral efficiency and compensate for the differential delays among the light trees, we incorporate the rateless network coding (R-NC) in the multicast system. We first formulate an integer linear programming (ILP) model to solve the problem for static network planning. Then, we propose three time-efficient heuristics that leverage the set-cover problem and utilize layered auxiliary graphs. The simulation results indicate that in both the ILP and heuristics, the MC-RMSA with R-NC can achieve better performance on the maximum index of used FS' than that without. After that we evaluate the heuristics in a dynamic network provisioning. The results show that the MC-RMSA with R-NC can effectively improve the performance of all-optical multicast in EONs to reduce the blocking probability.

Multiple constrained QoS routing with RPL

Abstract: In recent years, there have been significant efforts to standardize a routing protocol for Low-power and Lossy Networks (LLNs). This effort has culminated in standard IPv6 routing protocol for LLNs (RPL). The main interest of RPL is to improve routing in an LLN minimizing the usage of network resources. For this, RPL builds acyclic graphs and applies an Objective Function (OF) which is responsible of choosing the preferred parent and the best links during the construction of the Destination Oriented Directed Acyclic Graph (DODAG). This paper introduces a new OF, based on a Non-Linear Length (NL-OF) which takes into account any number of metrics and constraints for QoS routing. NL-OF ensures that each path in the DODAG respects the input constraints. The NL-OF can be used to meet the requirements of sensible applications, such as real-time applications. A significant part of this work aims at studying the theoretical aspect of the NL-OF. Finally, using Cooja simulator, we evaluate the performance of NL-OF. We show that our new Objective Function gives a good result and outperforms the three existing OFs when considering three QoS parameters which are end-to-end Delay, Packet Loss and Jitter.

Multi-constrained QOS multicast routing optimization

Abstract: In the future Internet, multimedia applications will be strongly present. When a group of users is concerned by the same traffic flow, the multicast communication can decrease considerably the network bandwidth utilization. The major part of this kind of multicast communication needs quality of service (QoS) specification. Often, the QoS is given as a set of QoS criteria and the computation of feasible or optimal routes corresponds to a multi-constrained optimization. Finding the multicast graph respecting the defined QoS requirements and minimizing network resources is a NP-complete optimization task. Exhaustive search algorithms are not supported in real networks. Greedy algorithms was proposed to find good multicast sub-graphs. The local decisions of greedy algorithms can lead to solutions which can be ameliorated. To improve greedy algorithm solution, we propose, first, ICRA algorithm which is an enhanced version of the well known Mamcra algorithm but is also limited. As Meta-heuristics are good candidates to find better solutions using a controlled execution time, we propose, secondly, Taboo-QMR algorithm which is a Taboo Search based algorithm to reduce the multicast sub-graph computed by the first step of the algorithm Mamcra. Simulations of all approaches are run based on random graphs and show that the application of Taboo-QMR algorithm presents a tangible enhancement in almost 32 per cent of the cases.

An Energy-Efficient Multiobjective Scheduling Model for Monitoring in Internet of Things

Abstract: To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via lossy links. This paper addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal vertex cover (VC) problem with constraint generation. Assuming periodical functioning, VCs are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of VCs to periods is modeled as a multiobjective generalized assignment problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a traveling salesman path problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review

Abstract: Energy-efficiency in target tracking applications has been extensively studied in the literature of Wireless Sensor Networks (WSN). However, there is little work which has been done to survey and summarize this effort. In this paper, we address the lack of these studies by giving an up-to-date State-of-the-Art of the most important energy-efficient target tracking schemes. We propose a novel classification of schemes that are based on the interaction between the communication subsystem and the sensing subsystem on a single sensor node. We are interested in collaborative target tracking instead of single-node tracking. In fact, WSNs are often of a dense nature, and redundant data that can be received from multiple sensors help at improving tracking accuracy and reducing energy consumption by using limited sensing and communication ranges. We show that energy-efficiency in a collaborative WSN-based target tracking scheme can be achieved via two classes of methods: sensing-related methods and communication-related methods. We illustrate both of them with several examples. We show also that these two classes can be related to each other via a prediction algorithm to optimize communication and sensing operations. By self-organizing the WSN in trees and/or clusters, and selecting for activation the most appropriate nodes that handle the tracking task, the tracking algorithm can reduce the energy consumption at the communication and the sensing layers. Thereby, network parameters (sampling rate, wakeup period, cluster size, tree depth, etc.) are adapted to the dynamic of the target (position, velocity, direction, etc.). In addition to this general classification, we discuss also a special classification of some protocols that put specific assumptions on the target nature and/or use a "non-standard" hardware to do sensing. At the end, we conduct a theoretic comparison between all these schemes in terms of objectives and mechanisms. Finally, we give some recommendations that help at designing a WSN-based energy efficient target tracking scheme.

Dynamic Multilevel Priority Packet Scheduling Scheme for Wireless Sensor Network

Abstract: Scheduling different types of packets, such as real-time and non-real-time data packets, at sensor nodes with resource constraints in Wireless Sensor Networks (WSN) is of vital importance to reduce sensors' energy consumptions and end-to-end data transmission delays. Most of the existing packet-scheduling mechanisms of WSN use First Come First Served (FCFS), non-preemptive priority and preemptive priority scheduling algorithms. These algorithms incur a high processing overhead and long end-to-end data transmission delay due to the FCFS concept, starvation of high priority real-time data packets due to the transmission of a large data packet in non-preemptive priority scheduling, starvation of non-real-time data packets due to the probable continuous arrival of real-time data in preemptive priority scheduling, and improper allocation of data packets to queues in multilevel queue scheduling algorithms. Moreover, these algorithms are not dynamic to the changing requirements of WSN applications since their scheduling policies are predetermined. In this paper, we propose a Dynamic Multilevel Priority (DMP) packet scheduling scheme. In the proposed scheme, each node, except those at the last level of the virtual hierarchy in the zone-based topology of WSN, has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. We evaluate the performance of the proposed DMP packet scheduling scheme through simulations for real-time and non-real-time data. Simulation results illustrate that the DMP packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay.

Genetic Algorithm for Energy-Efficient QoS Multicast Routing

Abstract: The consideration of energy consumption in wireless ad hoc networks prevents the problem of the network exhausting batteries, thus partitioning the entire network. Power-aware multicasting is proposed to reduce the power consumption. This letter presents an energy-efficient genetic algorithm mechanism to resolve quality of service (QoS) multicast routing problem, which is NP-complete. The proposed genetic algorithm depends on bounded end-to-end delay and minimum energy cost of the multicast tree. Simulation results show that the proposed algorithm is effective and efficient.

Energy-Efficient Future High-Definition TV

Abstract: The rapidly growing IPTV market has resulted in increased traffic volumes raising concerns over Internet energy consumption. In this paper, we explore the dynamics of TV viewing behavior and program popularity in order to devise a strategy to minimize energy usage. We evaluate the impact of our strategy by calculating the power consumption of IPTV delivered over an IP-over-WDM network, considering both standard definition and high definition TV. Caches are used to reduce energy consumption by storing the most popular programs at nodes closer to the end user. We then use our knowledge of viewing behaviors to generate a time-driven content replacement strategy to maximize cache hit ratios and minimize energy use. We develop a mixed integer linear programming (MILP) model to evaluate the power consumption of the network while performing time-driven content replacements on caches and validate the results by simulation. Finally, we extend our model to perform content replacements on caches with sleep-mode capabilities which can save power by reducing their size. Our results show that time-based content replacements with such variable caches increase cache hit ratios and so reduces the overall power consumption by up to 86% compared to no caching. Our findings also show that more power savings are achieved for high definition TV compared to standard definition TV, so this strategy will be beneficial in the long term.