The development of vehicular technologies and infrastructures leads to development in mobility handling for wireless communications. Improving connectivity establishment and reliability became an issue, especially for vehicles that may move out of antenna coverage during connection establishment. This paper focuses on improving LoRaWAN connectivity for roaming devices by combining a machine learning predictor and DNS prefetching to gather information necessary for connection establishment before the device comes under coverage, thus reducing the overall latency for connection establishment. The paper also relates to other issues by comparing the solution with other approaches and studying antenna occupation.

In a smart grid environment, we study the coalition formation of prosumers that aim at entering the energy market. It is paramount for the grid operation that the energy producers are able to sustain the grid demand in terms of stability and a minimum production requirement. We design an algorithm that seeks to form coalitions that will meet both of these requirements: a minimum energy level for the coalitions and a steady production level, which leads to finding uncorrelated sources of energy to form a coalition. We propose an algorithm that uses graph tools such as correlation graphs or clique percolation to form coalitions that meet such complex constraints. We validate the algorithm against a random procedure and show that, it not only performs better in terms of social welfare for the power grid, but also that it is more robust against unforeseen production variations due to changing weather conditions for instance.

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve wide network characteristics. Studies have shown that Small-World properties, primarily low average path length (APL) and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small-world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small-world properties show a degree of distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the APL and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using the Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on APL, clustering coefficient and connectivity.

In this paper, we propose an autonomous self-organization mechanism for the monitoring of the cold chain. All along the transportation through this logistic chain, the sensors are moved with the goods in very different networks. We argue that depending on the situation some protocols are more suited than others and it is necessary the sensors to adapt and to switch dynamically depending on where they are. The mechanism we designed allows a sensor to autonomously detect the context where it is and to choose the best protocols for this situation. It assures the adaptability to different contexts where a sensor may move (warehouses, trucks,...). It implements a hybrid synchronization mechanism to assure duty cycle synchronization between nodes with high power efficiency and context adaptation. This hybrid mechanism uses wake up signals to activate a sleeping sensor to assure fast synchronization and efficient power consumption optimization. This mechanism also changes dynamically the routing protocol to the different networks the sensors enter in (dense or sparse topologies, with or without total visibility among all nodes,...).

In WSN, it is not often desirable to use the GPS technology. Indeed, the use of GPS is expensive and may reduce the overall network performance. Moreover, indoor reception of the GPS signal is not possible. The Link Quality Indicator (LQI) is defined in the 802.15.4 standard, but its context of use is not specified in this standard. Some works on the LQI, few of which are field experiments, have shown that the LQI decreases as the distance increases. However, the challenge of clustering mechanisms is to form the smallest number of clusters by maximizing distances separating cluster heads to provide an efficient cover of the network and also minimizes the cluster overlaps. This reduces the amount of channel contention between clusters, and also improves the efficiency of algorithms that run at the level of the cluster heads. Therefore we propose an analytical model based on the use of the LQI in order to derive an optimally one-dominating set in which the smaller distance separating two cluster heads is improved.

Converging Web and Telco services is one of the most interesting and valuable challenge that both Web providers and Telco operators will be facing during the next few years. Indeed, recent evolutions like smartphones' enhanced web-browsing capabilities (e.g. IPhone) and the technical incursion of Web2.0 main actors in the Telco world (e.g. Android) points out that this convergence is necessary and unavoidable. As converging Web and Telco consists mainly on converging Web2.0 and IMS services, this paper will focus on this point. We will first provide an overview of the involved technologies and the convergence stakes. Thus we will present the different solutions that have been proposed so far and finally expose TEWCOP (stands for TElco and Web COnverging Protocol), our proposition, through a set of significant use cases.

Nowadays low cost UMTS and HSDPA mobile networks and broadcast systems such as DVB-T and DVB-H lead to an increase of multimedia broadcasted services. The new standard for broadcast over mobiles, DVB-H, needs service reliability. This issue is tricky, because in a broadcast transmission, the sender may not be able to receive all acknowledgements from all users in order to retransmit as in TCP. Moreover real time multimedia diffusion enforces constraints on this system, such as restricted transmission delay. In this paper, we evaluate the performance and benefits of using a separate return channel for quality of service of a broadcast diffusion. Peer-to-peer links between the receivers are used in order to allow local retransmissions and estimation of the loss rate with low risk of feedback implosion on the central server. Other techniques such as FEC should be combined to this system in order to reach "quasi error free transmission", as recommended by the DVB workgroup.

This paper generalizes the classical Jacobaeus (1950) method for evaluating the call blocking probabilities in multistage interconnection networks in the context of ATM type traffic. The aim of this generalization is twofold, first the results are extended to capture the heterogeneous, multirate and multiclass nature of the traffic; second it takes into account a path hunting algorithm inside the interconnection network. Sequential routing is chosen. The kernel of the method is computing occupancy probabilities of interstage links. An aggregation method is used for this computation. The accuracy of the method is shown by comparing with simulation. In addition to call blocking evaluation, the method may easily be extended to include other hunting algorithms.

An approach toward user behavior modeling that takes advantage of the properties of neural algorithms is described, and results obtained on preliminary testing of the approach are presented. The basis of the approach is the IDES (Intruder Detection Expert System) which has two components, an expert system looking for evidence of attacks on known vulnerabilities of the system and a statistical model of the behavior of a user on the computer system under surveillance. This model learns the habits a user has when he works with the computer, and raises warnings when the current behavior is not consistent with the previously learned patterns. The authors suggest the time series approach to add broader scope to the model. They therefore feel the need for alternative techniques and introduce the use of a neural network component for modeling user's behavior as a component for the intrusion detection system.>

Instead of using a very expensive and powerful central processing unit, the Hypercube F8 uses independent parallel microprocessors that are slow and inexpensive. This architecture is modeled in order to determine the proper number of microprocessors and to validate the system. The model is hierarchical. Different levels of the model are considered. Each level model corresponds to a subsystem of the Hypercube F8. Most of the common analytical methods are used and direct calculations are made. For each level several methods are compared and the approximations validated. The level 1 direct method is a general solution of polling models. The global model gives the throughput of the system the utilizations of the processors and the service times. We discuss the values of the parameters of the system.