Section: New Results

IoT and Low-Power Wireless Meshed Networks

More than 50 billion devices will be connected in 2020. This huge infrastructure of devices, which is managed by highly developed technologies, is called the Internet of Things (IoT). The IoT provides advanced services, and brings economic and societal benefits. This is the reason why engineers and researchers in 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. Low-Power Wireless Meshed Network is an essential part of this paradigm. It uses smart, autonomous and usually limited capacity devices in order to sense and monitor their environment.

Centralized or Distributed Scheduling for IEEE 802.15.4e TSCH networks

Participants : Yasuyuki Tanaka, Pascale Minet, Thomas Watteyne, Malisa Vucinic, Tengfei Chang, Keoma Brun-Laguna.

The wireless TSCH (Time Slotted Channel Hopping) network specified in the e amendment of the IEEE 802.15.4 standard has many appealing properties. Its schedule of multichannel slotted data transmissions ensures the absence of collisions. Because there is no retransmission due to collisions, communication is faster. Since the devices save energy each time they do not take part in a transmission, the power autonomy of nodes is prolonged. Furthermore, channel hopping mitigates multipath fading and interferences.

All communication in a TSCH network is orchestrated by the communication schedule it is using. The scheduling algorithm used hence drives the latency and capacity of the network, and the power consumption of the nodes. To increase the flexibility and the self-organizing capacities required by IoT, the networks have to be able to adapt to changes. These changes may concern the application itself, the network topology by adding or removing devices, the traffic generated by increasing or decreasing the device sampling frequency, for instance. That is why flexibility of the schedule ruling all network communications is needed. We have designed a number of scheduling algorithms for TSCH networks, answering different needs. For instance, the centralized Load-based scheduler that assigns cells per flow, starting with the flow originating from the most loaded node has proved optimal for many configurations. Simulations with the 6TiSCH simulator showed that it gets latencies close to the optimal. They also highlighted that end-to-end latencies are positively impacted by message prioritization (i.e. each node transmits the oldest message first) at high loads, and negatively impacted by unreliable links, as presented at GlobeCom 2019 [30].

Among the distributed scheduling algorithms proposed in the literature, 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. We have designed MSF and YSF, two distributed scheduling algorithms that are adaptive and compliant with the standardized protocols used in the 6TiSCH working group at IETF. The Minimal Scheduling Function (MSF) is a distributed scheduling algorithm in which neighbor nodes locally negotiate adding and removing cells. MSF was evaluated by simulation and experimentation, before becoming the default scheduling algorithm of the IETF 6TiSCH working group, and now an official standard. We also designed LLSF, a scheduling algorithm focused on low latency communication. We proposed a full-featured 6TiSCH scheduling function called YSF, that autonomously takes into account all the aspects of network dynamics, including the network formation phase and parent switches. YSF aims at minimizing latency and maximizing reliability for data gathering applications. Simulation results obtained with the 6TiSCH simulator show that YSF yields lower end-to-end latency and higher end-to-end reliability than MSF, regardless of the network topology. Unlike other top-down scheduling functions, YSF does not rely on any assumption regarding network topology or traffic load, and is therefore more robust in real network deployments. 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.

Furthermore we published additional research on computing the upper bounds on the end-to-end latency, finding the best trade-off between latency and network lifetime.

Modeling and Improving Named Data Networking over IEEE 802.15.4

Participants : Amar Abane, Samia Bouzefrane ( Cnam ), Paul Muhlethaler.

Enabling Named Data Networking (NDN) in real world Internet of Things (IoT) deployments becomes essential to benefit from Information Centric Networking (ICN) features in current IoT systems. One objective of the model is to show that caching can attenuate the number of transmissions generated by broadcast to achieve a reasonable overhead while keeping the data dissemination power of NDN. To design realistic NDN-based communication solutions for IoT, revisiting mainstream technologies such as low-power wireless standards may be the key. We explore the NDN forwarding over IEEE 802.15.4 by modeling a broadcast-based forwarding [27]. Based on the observations, we adapt the Carrier-Sense Multiple Access (CSMA) algorithm of 802.15.4 to improve NDN wireless forwarding while reducing broadcast effects in terms of packet redundancy, round-trip time and energy consumption. As future work, we aim to explore more complex CSMA adaptations for lightweight forwarding to make the most of NDN and design a general-purpose Named-Data CSMA.

Evaluation of LORA with stochastic geometry

Participants : Bartek Blaszczyszyn ( Dyogene ), 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 [16]. 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 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 ).

A location method based on triangulation (via Channel State Information (CSI) based localization method is proposed [6]. A known method of triangulation is adopted to deduce the location of a node from 3 reference nodes (anchor nodes). We propose an optimized energy-aware and low computational solution, requiring 3-GPS equipped nodes (anchor nodes) in the network. Moreover, the computations are lightweight and can be implemented distributively among nodes. Knowing the maximum range of communication for all nodes and distances between 1-hop neighbors, each node localizes itself and shares its location with the network in an efficient manner. We simulate our proposed algorithm on a NS-3 simulator, and compare our solution with state-of-the-art methods. Our method is capable of localizing more nodes i.e. $\simeq 90\%$ of nodes in a network with an average degree $\simeq 10$.