Section: New Results
Cellular network solutions
Participants: Marco Fiore, Anis Ouni, Hervé Rivano, Razvan Stanica, Fabrice Valois
Optimizing capacity and energy consumption in wireless mesh networks.
Wireless mesh networks (WMN) are a promising solution to support high data rate and increase the capacity provided to users, e.g. for meeting the requirements of mobile multimedia applications. However, the rapid growth of traffic load generated by the terminals is accompanied by an unsustainable increase of energy consumption, which becomes a hot societal and economical challenges. This thesis relates to the problem of the optimization of network capacity and energy consumption of wireless mesh networks. The network capacity is defined as the maximum achievable total traffic in the network per unit time.
The thesis of Anis Ouni  addresses this issue and is divided into four main parts. First, we address the problem of improvement of the capacity of 802.11 wireless mesh networks. We highlight some insensible properties and deterministic factors of the capacity, while it is directly related to a bottleneck problem. Then, we propose a joint TDMA/CSMA scheduling strategy for solving the bottleneck issue in the network.
Second, we focus on broadband wireless mesh networks based on time-frequency resource management. In order to get theoretical bounds on the network performances, we formulate optimization models based on linear programming and column generation algorithm. These models lead to compute an optimal offline configuration which maximizes the network capacity with low energy consumption. A realistic SINR model of the physical layer allows the nodes to perform continuous power control and use a discrete set of data rates.
Third, we use the optimization models to provide practical engineering insights on WMN. We briefly study the tradeoff between network capacity and energy consumption using a realistic physical layer and SINR interference model  . In particular, we show that power control and multi-rate functionalities allow to reach optimal throughput with lower energy consumption using a mix of single hop and multi- hop routes.
Finally, we focus on capacity and energy optimization for heterogeneous cellular networks. We develop optimization tools to calculate an optimal configuration of the network that maximizes the network capacity with low energy consumption. We then propose a heuristic algorithm that calculates a scheduling and partial sleeping of base stations in two different strategies, called LAFS and MAFS.
Sleep protocols for heterogeneous LTE networks.
The tremendous increase of the traffic demand in cellular networks imposes a massive densification of the traditional cellular infrastructure. The network architecture becomes heterogeneous, in particular 4G networks where LTE micro-eNodeBs are deployed to strengthen the coverage of macro-eNodeBs. This densification yields major issues related to the energy consumption of the infrastructure. Indeed, there is fixed and significant amount of energy required to run each additional node, whatever the traffic load of the network. Mitigating this fixed energy consumption is therefore a major challenge from a societal and economical viewpoint. Extensive researches about energy-saving highlight that to save energy the better strategy is to switch off the radio part of nodes. This is the heart of wireless sensor networks energy-saving strategies, even though the objective for WSN is to maximize the battery life of each individual nodes. In  , we develop a parallel between the principles of WSN protocols and the requirements of cellular infrastructures. We then propose a distributed and localized algorithm to dynamically switch off and on the micro-eNodeBs of an LTE heterogeneous network following the traffic demand evolution in time and analyze it in terms of energy savings. We show that one can expect energy savings of approximately 12% when implementing sleep modes whereas the energy cost for sending the traffic decreases by 24%.
Content downloading through a vehicular network.
The focus of the work we present in  is twofold: information dissemination from infrastructure nodes deployed along the roads, the so-called Road-Side Units (RSUs), to passing-by vehicles, and content downloading by vehicular users through nearby RSUs. In particular, in order to ensure good performance for both content dissemination and downloading, the presented study addresses the problem of RSU deployment and reviews previous work that has dealt with such an issue. The RSU deployment problem is then formulated as an optimization problem, where the number of vehicles that come in contact with any RSU is maximized, possibly considering a minimum contact time to be guaranteed. Since such optimization problems turn out to be NP-hard, heuristics are proposed to efficiently approximate the optimal solution. The RSU deployment obtained through such heuristics is then used to investigate the performance of content dissemination and downloading through ns2 simulations. Simulation tests are carried out under various real-world vehicular environments, including a realistic mobility model, and considering that the IEEE 802.11p standard is used at the physical and medium access control layers. The performance obtained in realistic conditions is discussed with respect to the results obtained under the same RSU deployment, but in ideal conditions and protocol message exchange. Based on the obtained results, some useful hints on the network system design are provided.
Offloading Floating Car Data.
Floating Car Data (FCD) is currently collected by moving vehicles and uploaded to Internet-based processing centers through the cellular access infrastructure. As FCD is foreseen to rapidly become a pervasive technology, the present network paradigm risks not to scale well in the future, when a vast majority of automobiles will be constantly sensing their operation as well as the external environment and transmitting such information towards the Internet. In order to relieve the cellular network from the additional load that widespread FCD can induce, we study  a local gathering and fusion paradigm, based on vehicle-to-vehicle (V2V) communication. We show how this approach can lead to significant gain, especially when and where the cellular network is stressed the most. Moreover, we propose several distributed schemes to FCD offloading based on the principle above that, despite their simplicity, are extremely efficient and can reduce the FCD capacity demand at the access network by up to 95%.
Mobile malware propagation in vehicular networks.
The large-scale adoption of vehicle-to-vehicle (V2V) communication technologies risks to significantly widen the attack surface available to mobile malware targeting critical automobile operations. Given that outbreaks of vehicular computer worms self-propagating through V2V links could pose a significant threat to road traffic safety, it is important to understand the dynamics of such epidemics and to prepare adequate countermeasures. In  , we perform a comprehensive characterization of the infection process of variously behaving vehicular worms on a road traffic scenario of unprecedented scale and heterogeneity. We then propose a simple yet effective data-driven model of the worm epidemics, and we show how it can be leveraged for smart patching infected vehicles through the cellular network in presence of a vehicular worm outbreak.