Section: New Results
IoT and Wireless Sensor Networks
More than 50 billions of devices will be connected in 2020. This huge infrastructure of devices, which is managed by highly developed technologies, is called Internet of Things (IoT). The latter provides advanced services, and brings economical and societal benefits. This is the reason why engineers and researchers of both industry and scientific communities are interested in this area. The Internet of Things enables the interconnection of smart physical and virtual objects, managed by highly developed technologies. WSN (Wireless Sensor Network), is an essential part of this paradigm. The WSN uses smart, autonomous and usually limited capacity devices in order to sense and monitor their environment.
Distributed Scheduling for IEEE 802.15.4e TSCH networks
Participants : Yasuyuki Tanaka, Pascale Minet, Thomas Watteyne.
Since the scheduling algorithm is not standardized for IEEE 802.15.4e TSCH networks, many scheduling algorithms have been proposed. Most of them are centralized, few are distributed. Among the distributed scheduling algorithms, many rely on assumptions that may be violated by real deployments. This violation usually leads to conflicting transmissions of application data, decreasing the reliability and increasing the latency of data delivery. Others require a processing complexity that cannot be provided by sensor nodes of limited capabilities. Still others are unable to adapt quickly to traffic or topology changes, or are valid only for small traffic loads.
In the study funded by the Inria ADT DASMU (Action de Developement Technologique Distributed Adaptive Scheduling for MUltichannel wireless sensor networks), we focus on a distributed scheduling algorithm that relies on realistic assumptions, does not require complex computation, is valid for any traffic load, is adaptive and compliant with the standardized protocols used in the 6TiSCH working group at IETF.
First results have been obtained and an intensive simulation campaign made with the 6TiSCH simulator has provided comparative performance results. Our proposal outperforms MSF, the 6TiSCH Minimal Scheduling Function, in terms of end-to-end latency and end-to-end packet delivery ratio. More evaluations are needed to improve the proposal (e.g. less packet drops during transient situations, less overhead) in terms of scheduled cells).
IoT and IEEE 802.15.4e TSCH networks
Participants : Pascale Minet, Ines Khoufi, Zied Soua.
In 2018, we focus on how an IEEE 802.15.4e is autonomously built and how nodes join the network.
To join the TSCH network, a device randomly selects a physical channel used by this network and listens to a beacon advertising this network. Since the physical channel on which the beacon is broadcast changes at each beacon slot due to channel hopping, the joining device will eventually hear a beacon sent by one of its neighbors. Upon receipt of a valid beacon, this device gets synchronized with the TSCH network.
In this study, we focus on the time needed by a node to detect a beacon sent by a TSCH network, as well as on the time needed to build a TSCH network. These times are important for industrial applications where new nodes are inserted progressively, or when failed nodes are replaced. Both times highly depend on the beacon advertisement policy, policy that is not specified in the standard and is under the responsibility of a layer upper than the MAC one. Since beacons are broadcast, they are lost in case of collisions: the vital information they carry is lost. The main problem is how to avoid collisions between two devices that are not neighbors.
That is why we propose the Enhanced Deterministic Beacon Advertising algorithm, called EDBA, that ensures a collision-free advertising of beacons. Since the beacon cells are fairly distributed in the slotframe, the average joining time is minimized. The behavior of a joining node has been modeled by a Markov chain from which the average joining time is computed, taking into account the reliability of wireless links. An intensive performance evaluation based on NS3 simulations allows us to validate this model and conclude on the very good performance of EDBA, even when compared with MBS, considered as the best advertising algorithm in the literature. These results have been published in the Annals of Telecommunications, .
UAV-based Data Gathering
Participants : Nadjib Achir ( Paris 13), Tounsia Djamah, Paul Muhlethaler, Celia Tazibt ( Paris 13).
The recent advances in wireless sensors and Unmanned Aerial Vehicles have created new opportunities for environmental control and low cost aerial data gathering. We propose to use an Unmanned Aerial Vehicle (UAV) for data gathering . Basically, we have proposed a method for UAV path planning based on virtual forces and potential fields. In addition, and more importantly, we present a new approach to compute the attractive forces of the potential field.
We use as our starting point the idea used by Pereira of using a potential field approach. However, we extend this work by considering that each cell in the area apply an attractive force on the drone, not only the deployed sensors. We compared our results with those obtained with Pereira's method and we obtained better performance in terms of data collection time. In other words, for the same period of time our method collect more data. The second advantage of our approach is that it leads to a significant reduction in the distance that the drone must travel.
Towards evaluating Named Data Networking for the IoT: A framework for OMNeT++
Participants : Amar Abane, Samia Bouzefrane ( Cnam), Paul Muhlethaler.
Named Data Networking is a promising architecture for emerging Internet applications such as the Internet of Things (IoT). Many studies have already investigated how NDN can be an alternative for IP in future IoT deployments. However, NDN-IoT propositions need accurate evaluation at network level and system level as well. We introduce an NDN framework for OMNeT++ . Designed for low-end devices and gateways of the IoT, the framework is capable of simulating NDN scenarios at the boundary of the network and the system. The framework implementation is presented and used to study a typical aspect of NDN integration in IoT devices.
Evaluation of LORA with stochastic geometry
Participants : Bartek Blaszczyszyn ( Dyogen), Paul Muhlethaler.
We present a simple, stochastic-geometric model of a wireless access network exploiting the LoRA (Long Range) protocol, which is a non-expensive technology allowing for long-range, single-hop connectivity for the Internet of Things. We assume a space-time Poisson model of packets transmitted by LoRA nodes to a fixed base station. Following previous studies of the impact of interference, we assume that a given packet is successfully received when no interfering packet arrives with similar power before the given packet payload phase, see . This is as a consequence of LoRa using different transmission rates for different link budgets (transmissions with smaller received powers use larger spreading factors) and LoRa intra-technology interference treatment. Using our model, we study the scaling of the packet reception probabilities per link budget as a function of the spatial density of nodes and their rate of transmissions. We consider both the parameter values recommended by the LoRa provider, as well as proposing LoRa tuning to improve the equality of performance for all link budgets. We also consider spatially non-homogeneous distributions of LoRa nodes. We show also how a fair comparison to non-slotted Aloha can be made within the same framework.
Position Certainty Propagation: A location service for MANETs
Participants : Abdallah Sobehy, Paul Muhlethaler, Eric Renault ( Telecom Sud-Paris).
Localization in Mobile Ad-hoc Networks (MANETs) and Wireless Sensor Networks (WSNs) is an issue of great interest, especially in applications such as the IoT and VANETs. We propose a solution that overcomes two limiting characteristics of these types of networks. The first is the high cost of nodes with a location sensor (such as GPS) which we will refer to as anchor nodes. The second is the low computational capability of nodes in the network. The proposed algorithm  addresses two issues; self-localization where each non-anchor node should discover its own position, and global localization where a node establishes knowledge of the position of all the nodes in the network. We address the problem as a graph where vertices are nodes in the network and edges indicate connectivity between nodes. The weights of edges represent the Euclidean distance between the nodes. Given a graph with at least three anchor nodes and knowing the maximum communication range for each node, we are able to localize nodes using fairly simple computations in a moderately dense graph.