Stochastic Analysis of Non-slotted Aloha in Wireless Ad-Hoc Networks

In this paper we propose two analytically tractable stochastic models of non-slotted Aloha for Mobile Ad-hoc NETworks (MANETs): one model assumes a static pattern of nodes while the other assumes that the pattern of nodes varies over time. Both models feature transmitters randomly located in the Euclidean plane, according to a Poisson point process with the receivers randomly located at a fixed distance from the emitters. We concentrate on the so-called outage scenario, where a successful transmission requires a Signal-to-Interference-and-Noise Ratio (SINR) larger than a given threshold. With Rayleigh fading and the SINR averaged over the duration of the packet transmission, both models lead to closed form expressions for the probability of successful transmission. We show an excellent matching of these results with simulations. Using our models we compare the performances of non-slotted Aloha to previously studied slotted Aloha. We observe that when the path loss is not very strong both models, when appropriately optimized, exhibit similar performance. For stronger path loss non-slotted Aloha performs worse than slotted Aloha, however when the path loss exponent is equal to 4 its density of successfully received packets is still 75% of that in the slotted scheme. This is still much more than the 50% predicted by the well-known analysis where simultaneous transmissions are never successful. Moreover, in any path loss scenario, both schemes exhibit the same energy efficiency.Comment: accepted for IEEE Infocom 201

A Markovian Analysis of IEEE 802.11 Broadcast Transmission Networks with Buffering

The purpose of this paper is to analyze the so-called back-off technique of the IEEE 802.11 protocol in broadcast mode with waiting queues. In contrast to existing models, packets arriving when a station (or node) is in back-off state are not discarded, but are stored in a buffer of infinite capacity. As in previous studies, the key point of our analysis hinges on the assumption that the time on the channel is viewed as a random succession of transmission slots (whose duration corresponds to the length of a packet) and mini-slots during which the back-o? of the station is decremented. These events occur independently, with given probabilities. The state of a node is represented by a two-dimensional Markov chain in discrete-time, formed by the back-off counter and the number of packets at the station. Two models are proposed both of which are shown to cope reasonably well with the physical principles of the protocol. The stabillity (ergodicity) conditions are obtained and interpreted in terms of maximum throughput. Several approximations related to these models are also discussed

NDR: Noise and Dimensionality Reduction of CSI for indoor positioning using deep learning

International audienceDue to the emerging demand for IoT applications, indoor positioning became an invaluable task. We propose a novel lightweight deep learning solution to the indoor positioning problem based on noise and dimensionality reduction of MIMO Channel State Information (CSI). Based on preliminary data analysis, the magnitude of the CSI is selected as the input feature for a Multilayer Perceptron (MLP) neural network. Polynomial regression is then applied to batches of data points to filter noise and reduce input dimensionality by a factor of 14. The MLP’s hyperparameters are empirically tuned to achieve the highest accuracy. The proposed solution is compared with a state-of-the-art method presented by the authors who designed the MIMO antenna that is used to generate the dataset. Our method yields a mean error which is 8 times less than that of its counterpart. We conclude that the arithmetic mean and standard deviation misrepresent the results since the errors follow a log- normal distribution. The mean of the log error distribution of our method translates to a mean error as low as 1.5 cm

A lightweight forwarding strategy for Named Data Networking in low-end IoT

International audienc

A down-to-earth integration of Named Data Networking in the real-world IoT

International audienceThe IEEE802.15.4 wireless technology is one of the enablers of the Internet of Things. It allows constrained devices to communicate with a satisfactory data rate, payload size and distance range, all with reduced energy consumption. To provide IoT devices with a global Internet identity, 6LoWPAN defines the IPv6 adaptation to communicate over IEEE802.15.4. However, this integration still needs additional protocols to support other IoT requirements, which makes the IP stack in IoT devices more complex and therefore shows the limitations of the IP model to support the needs of future Internet. Named Data Networking represents an alternative that can natively support IoT constraints including mobility, security and human readable data names. This paper is a synthesis of an ongoing work that investigates the integration of NDN with IEEE802.15.4 for constrained IoT devices. The proposed design has been implemented in a real-world smart agriculture scenario, and evaluated by simulation focusing on energy consumption and network overhead in comparison to IP-based protocols

Generalized scheduling on a single machine in a real-time systems based on time value functions

Projet REFLECSTime value functions is a new concept for the description of real-time system. In such systems time value function (TVF) is associated to each task. The value of this function taken at time t gives the award that the system receives if the corresponding task is achieved by this time. In this paper, we investigate the general scheduling problem which consists in maximizing the sum of the TVFs evaluated at the completion time of the corresponding tasks. As previous studies envision only regular processor (no idle between tasks) our general approach allows idle intervals between tasks. Except in special cases this new degree of freedom is likely to incrase the general criterion. For this NP-hard problem our aim.is to find efficient heuristics. First we describe an exact algorithm to solve the problem and we analyze its complexity. Then we define the optimal decomposition : the set of the tasks to be scheduled is divided into a ranked collection of subsets. To reach the optimum criterion, the tasks of a lower rank subset are to be scheduled prior to those of a higher rank. We also introduce polynomial scheduling algorithms which provide sequences respecting this optimal decomposition. On a practical point of view, simulation results have shown that these algorithms yield sequences which provide general criterions close to the optimum

Interference in spatial non-slotted Aloha networks

In this paper we propose two analytically tractable stochastic-geometric models of interference in ad-hoc networks using pure (non-slotted) Aloha as the medium access. In contrast to the slotted model, the interference in pure Aloha may vary during the transmission of a tagged packet. We develop closed form expressions for the Laplace transform of the empirical average of the interference experienced during the transmission of a typical packet. Both models assume a power-law path-loss function with arbitrarily distributed fading and feature configurations of transmitters randomly located in the Euclidean plane according to a Poisson point process. Depending on the model, these configurations vary over time or are static. We apply our analysis of the interference to study the Signal-to-Interference-and-Noise Ratio (SINR) outage probability for a typical transmission in pure Aloha. The results are used to compare the performance of non-slotted Aloha to slotted Aloha, which has almost exclusively been previously studied in the same context of mobile ad-hoc networks

Models for communications in the hypercube

Projet ICSL