Mitigating Radio Interference in Large IoT Networks through Dynamic CCA Adjustment

The performance of low-power wireless sensor networks used to build Internet of Things applications often suffers from radio interference generated by co-located wireless devices or from jammers maliciously placed in their proximity. As IoT devices typically operate in unsupervised large-scale installations, and as radio interference is typically localized and hence affects only a portion of the nodes in the network, it is important to give low-power wireless sensors and actuators the ability to autonomously mitigate the impact of surrounding interference. In this paper we present our approach DynCCA, which dynamically adapts the clear channel assessment threshold of IoT devices to minimize the impact of malicious or unintentional interference on both network reliability and energy efficiency. First, we describe how varying the clear channel assessment threshold at run-time using only information computed locally can help to minimize the impact of unintentional interference from surrounding devices and to escape jamming attacks. We then present the design and implementation of DynCCA on top of ContikiMAC and evaluate its performance on wireless sensor nodes equipped with IEEE 802.15.4 radios. Our experimental investigation shows that the use of DynCCA in dense IoT networks can increase the packet reception rate by up to 50% and reduce the energy consumption by a factor of 4

Constructive Interference in 802.15.4: A Tutorial

International audienceConstructive Interference (CI) can happen when multiple wireless devices send the same frame at the same time. If the time offset between the transmissions is less than 500 ns, a receiver will successfully decode the frame with high probability. CI can be useful for achieving low-latency communication or low-overhead flooding in a multi-hop low-power wireless network. The contribution of this article is threefold. First, we present the current state-of-the-art CI-based protocols. Second, we provide a detailed hands-on tutorial on how to implement CI-based protocols on TelosB motes, with well documented open-source code. Third, we discuss the issues and challenges of CI-based protocols, and list open issues and research directions. This article is targeted at the level of practicing engineers and advanced researchers and can serve both as a primer on CI technology and a reference to its implementation

WEAVE : routage géographique efficace dans les réseaux à grande échelle

International audienceNous proposons WEAVE, un protocole de routage géographique 2D/3D reposant sur l'utilisation de points d'étapes, waypoints, et de points de passage, checkpoints, pour acheminer le trafic à destination. Les noeuds obtiennent leur information de routage à partir de traces partielles collectées dans les paquets relayés et utilisent un système de points intermédiaires associés à des portions de chemin pour tisser (to weave) des chemins de bout en bout proches des plus courts chemins. WEAVE ne génère aucun paquet de contrôle, permet le routage en 2D comme en 3D et ne fait aucune hypothèse forte sur le graphe sous-jacent, telle que disque unitaire ou graphe planaire. WEAVE présente d'excellentes performances comparé aux protocoles existants, à la fois en simulation et lors d'expérimentations réelles

Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions

In this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access to the network and their autonomy, while taking into account the impact of encountered radio collisions. For that end, several heuristics employing Upper-Confident Bound (UCB) algorithms are examined, to explore the contextual information provided by the number of retransmissions. Our results show that approaches based on UCB obtain a significant improvement in terms of successful transmission probabilities. Furthermore, it also reveals that a pure UCB channel access is as efficient as more sophisticated learning strategies.Comment: The source code (MATLAB or Octave) used for the simula-tions and the figures is open-sourced under the MIT License, atBitbucket.org/scee\_ietr/ucb\_smart\_retran

SoEasy: A Software Framework for Easy Hardware Control Programming for Diverse IoT Platforms

Many Internet of Things (IoT) applications are emerging and evolving rapidly thanks to widespread open-source hardware platforms. Most of the high-end open-source IoT platforms include built-in peripherals, such as the universal asynchronous receiver and transmitter (UART), pulse width modulation (PWM), general purpose input output (GPIO) ports and timers, and have enough computation power to run embedded operating systems such as Linux. However, each IoT platform has its own way of configuring peripherals, and it is difficult for programmers or users to configure the same peripheral on a different platform. Although diverse open-source IoT platforms are widespread, the difficulty in programming those platforms hinders the growth of IoT applications. Therefore, we propose an easy and convenient way to program and configure the operation of each peripheral using a user-friendly Web-based software framework. Through the implementation of the software framework and the real mobile robot application development along with it, we show the feasibility of the proposed software framework, named SoEasy

The "Smart Ring" Experience in l'Aquila (Italy): Integrating Smart Mobility Public Services with Air Quality Indexes

This work presents the "City Dynamics and Smart Environment" activities of the Smart Ring project, a model for the smart city, based on the integration of sustainable urban transport services and environmental monitoring over a 4–5-km circular path, the "Smart Ring", around the historical center of l'Aquila (Italy). We describe our pilot experience performed during an experimental on-demand public service electric bus, "SmartBus", which was equipped with a multi-parametric air quality low-cost gas electrochemical sensor platform, "NASUS IV". For five days (28–29 August 2014 and 1–3 September 2014), the sensor platform was installed inside the SmartBus and measured air quality gas compounds (nitrogen dioxide, carbon oxide, sulfur dioxide, hydrogen sulfide) during the service. Data were collected and analyzed on the bases of an air quality index, which provided qualitative insights on the air status potentially experienced by the users. The results obtained are in agreement with the synoptic meteorological conditions, the urban background air quality reference measurements and the potential traffic flow variations. Furthermore, they indicated that the air quality status was influenced by the gas component NO 2 , followed by H 2 S, SO 2 and CO. We discuss the features of our campaign, and we highlight the potential, limitations and key factors to consider for future project designs

Competition: Channel Exploration/Exploitation Based on a Thompson Sampling Approach in a Radio Cognitive Environment

International audienceMachine learning approaches have been extensively applied in interference mitigation and cognitive radio devices. In this work, we model the spectrum selection process as a multi-arm bandit problem and apply Thompson sampling, a fast and efficient algorithm, to find the best channel in the shortest time interval. The learning algorithm will work on top of a network layer to efficiently route the event information to the sink

A Thompson Sampling Approach to Channel Exploration-Exploitation Problem in Multihop Cognitive Radio Networks

International audienceCognitive radio technology is a promising solution to the exponential growth in bandwidth demand sustained by increasing number of ubiquitous connected devices. The allocated spectrum is opened to the secondary users conditioned on limited interference on the primary owner of the band. A major bottleneck in cognitive radio systems is to find the best available channel quickly from a large accessible set of channels. This work formulates the channel exploration-exploitation dilemma as a multi-arm bandit problem. Existing theoretical solutions to a multi-arm bandit are adapted for cognitive radio and evaluated in an experimental test-bed. It is shown that a Thompson sampling based algorithm efficiently converges to the best channel faster than the existing algorithms and achieves higher asymptotic average throughput. We then propose a multihop extension together with an experimental proof of concept