Joint Technology and Route Selection in Multi-RAT Wireless Sensor Networks with RODENT

International audienceWireless Sensor Networks (WSN) are limited by the characteristics of the Radio Access Technologies (RAT) their are based on. We call a wireless multi-hop network composed of nodes able to use several RAT a Multiple Technologies Network (MTN). Nodes must manage the RAT and route selection, in a local and distributed way, with an suitable communication protocol stack. Nodes may share multiple common RAT with multiple neighbors. Thus the devices' heterogeneity of technologies has to be taken into account by each of the stack's layer. In this article, we introduce our custom Routing Over Different Existing Network Technologies protocol (RODENT), designed for MTN. It is capable of dynamically (re)selecting the best RAT and route based on data requirements evolving over time. RODENT is based on a multi-criteria route selection via a custom lightweight TOPSIS method from our previous work [1]. For an evaluation of performance, we implemented a functional prototype of RODENT on Pycom FiPy devices. Results show that RODENT enables multiple data requirements support and energy savings, while increasing effective coverage

Sélection d’interface de communication dans les réseaux de capteurs multi-technologies

National audienceLes réseaux de capteurs sont composés de systèmes généralement contraints en énergie et communiquant via des liaisons sans fil.Cependant, le déploiement d'un tel réseau est limité par la portée radio et le débit de la technologie utilisée.Pouvoir choisir la technologie la plus adaptée au scénario permettrait de dépasser cette limite et de réduire la consommation énergétique tout en permettant la différenciation des flux de données."Technique for Order of Preference by Similarity to Ideal Solution" (TOPSIS) est une méthode permettant de comparer finement des technologies basées sur des attributs contradictoires.Mais elle est limitée par un phénomène d'anomalie de classement pouvant altérer la qualité de la sélection.De plus, TOPSIS nécessite des calculs complexes, augmentant la consommation d'énergie sur du matériel contraint.Dans cet article, nous proposons une méthode TOPSIS adaptée pour la sélection d'interface de communication sur du matériel contraint.L'évaluation de notre solution avec des modules FiPy de Pycom montre une amélioration du temps de calcul de 40% tout en assurant une similarité de classement avec TOPSIS de 80%

RODENT: a flexible TOPSIS based routing protocol for multi-technology devices in wireless sensor networks

International audienceWireless Sensor Networks (WSN) are efficient tools for many use cases, such as environmental monitoring. However WSN deployment is sometimes limited by the characteristics of the Radio Access Technologies (RATs) they use. To overcome some of these limitations, we propose to leverage the use of a Multiple Technologies Network (MTN). What we refer to as MTN is a network composed of nodes which are able to use several RAT and communicating wirelessly through multi-hop paths. The management of the RAT and routes must be handled by the nodes themselves, in a local and distributed way, with a suitable communication protocol stack. Nodes may reach multiple neighbors over multiple RAT. Therefore, each stack's layer has to take the technologies' heterogeneity of the devices into account. In this article, we introduce our custom Routing Over Different Existing Network Technologies protocol (RODENT), designed for MTN. It enables dynamic (re)selection of the best route and RAT based on the data type and requirements that may evolve over time, potentially mixing each technology over a single path. RODENT relies on a multi-criteria route selection performed with a custom lightweight TOPSIS method. To assess RODENT's performances, we implemented a functional prototype on real WSN hardware, Pycom FiPy devices. Unlike related prototypes, ours has the advantage not to rely on specific infrastructure on the operator's side. Results show that RODENT enables energy savings, an increased coverage as well as multiple data requirements support

Lightweight network interface selection for reliable communications in multi-technologies wireless sensor networks

International audienceWireless sensor networks (WSN) are composed of hardware constrained and battery-powered devices that communicate wirelessly. WSN find more and more applications, but their deployment is limited among others by the range and the throughput of the communication technology used. Several technologies are available nowadays, with various performances, cost and coverage. One solution to overcome the deployment limitations and in some cases extend the coverage would be to dynamically select the technology based on the data requirements, environment, geographic location, etc. Thus we need multitechnologies WSN devices and efficient algorithms to select the best available technology in an autonomous and local way. This issue is known as Network Interface Selection (NIS). Multi-Attribute Decision Making (MADM) methods are an efficient tool to tackle NIS. Among MADM methods is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). However, TOPSIS suffers from a rank reversal issue, which may alter the ranking quality. Furthermore, TOPSIS method is computationally heavy, which might increase the energy consumption of the constrained devices and the latency of the network. In this paper, we introduce a lightweight TOPSIS-based method tailored for NIS in WSN, allowing more reliable communications. Experimental results obtained on real hardware, i.e., Pycom FiPy modules, show an improvement in computation time of 38% while maintaining a selection similar to TOPSIS in 82% of runs

Autonomous Collaborative Wireless Weather Stations: A Helping Hand for Farmers

International audienceIn the context of smart farming, communications still pose a key challenge. Ubiquitous access to the internet is not available worldwide, and battery capacity is still a limitation. Inria and the Sencrop company are collaborating to develop an innovative solution for wireless weather stations, based on multi-technology communications, to enable smart weather stations deployment everywhere around the globe

Routing Over Multiple Technologies with RODENT

International audienceWireless Sensor Networks (WSN) are limited by the characteristics of the Radio Access Technologies (RAT) they are based on. What we refer to as Multiple Technologies Network (MTN) is a network composed of nodes able to use several RAT. The management of the RAT and routes must be handled by the nodes themselves, in a local way, with a suited communication protocols stack. Each stack's layer has to take the technologies' heterogeneity of the devices into account. In this demonstration paper, we show the practical implementation of our custom routing protocol Routing Over Different Existing Network Technologies (RODENT), designed for MTN. It enables dynamic (re)selection of the best route and RAT based on the data type and requirements that may evolve over time. To assess its performance, we have implemented a functional prototype on real WSN hardware, Pycom FiPy devices

Des capteurs multitechnologies pour surveiller les champs : les épouvantails 2.0

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Comment générer des traces applicatives avec FIT IoT-LAB pour la science ouverte

International audienceL'essor récent de l'apprentissage automatique et l'intérêt grandissant porté à l'intelligence artificielle a généré une demande de données de plus en plus importante. Le mouvement de la science ouverte, et plus particulièrement les données ouvertes, apportent une réponse à cette demande en offrant à tous un accès et un usage libre à des jeux de données. Dans cet article, nous proposons une méthodologie pour générer des traces correspondant à différentes applications IoT. Pour cela, nous caractérisons les différents types de trafic, comme leur fréquence de communication et la taille des paquets échangés. Puis, nous simulons ces applications sur la plateforme FIT IoT-LAB pour générer les traces. Les paramètres des simulations, tels que le nombre de noeuds employés, sont choisis selon les caractéristiques de l'application simulée. Les traces ainsi générées sont enrichies de plusieurs données qui permettent de déduire des métriques utiles, telles que le taux de livraison des paquets et le délai de bout en bout. Nous partageons en accès ouvert les traces obtenues ainsi qu'une base de code pour générer, manipuler et analyser les données obtenues de FIT IoT-LAB

A study of the LoRa signal propagation in forest, urban, and suburban environments

International audienceSensing is an activity of paramount importance for smart cities. The coverage of large areas based on reduced infrastructure and low energy consumption is desirable. In this context, Low Power Wide Area Network (LPWAN) plays an important role. In this paper, we investigate LoRa, a low-power technology offering large coverage, but low transmission rates. Radio range and data rate are tunable by using different spreading factors and coding rates, which are configuration parameters of the LoRa physical layer. LoRa can cover large areas but variations in the environment affect link quality. This work studies the propagation of LoRa signals in forest, urban, and suburban vehicular environments. Besides being environments with variable propagation conditions, we evaluate scenarios with node mobility. To characterize the communication link, we mainly use the Received Signal Strength Indicator (RSSI), Signal to Noise Ratio (SNR), and Packet Delivery Ratio (PDR). As for node mobility, speeds are chosen according to prospective applications. Our results show that the link reaches up to 250 m in the forest scenario, while in the vehicular scenario it reaches up to 2 km. In contrast, in scenarios with high-density buildings and human activity, the maximum range of the link reaches up to 200 m in the urban scenario

Perspectives of healthcare providers, service users, and family members about mental illness stigma in primary care settings: A multi-site qualitative study of seven countries in Africa, Asia, and Europe

Background: Stigma among healthcare providers is a barrier to the effective delivery of mental health services in primary care. Few studies have been conducted in primary care settings comparing the attitudes of healthcare providers and experiences of people with mental illness who are service users in those facilities. Such research is necessary across diverse global settings to characterize stigma and inform effective stigma reduction. Methods: Qualitative research was conducted on mental illness stigma in primary care settings in one low-income country (Nepal), two lower-middle income countries (India, Tunisia), one upper-middle-income country (Lebanon), and three high-income countries (Czech Republic, Hungary, Italy). Qualitative interviews were conducted with 248 participants: 64 primary care providers, 11 primary care facility managers, 111 people with mental illness, and 60 family members of people with mental illness. Data were analyzed using framework analysis. Results: Primary care providers endorsed some willingness to help persons with mental illness but reported not having appropriate training and supervision to deliver mental healthcare. They expressed that people with mental illness are aggressive and unpredictable. Some reported that mental illness is incurable, and mental healthcare is burdensome and leads to burnout. They preferred mental healthcare to be delivered by specialists. Service users did not report high levels of discrimination from primary care providers; however, they had limited expectations of support from primary care providers. Service users reported internalized stigma and discrimination from family and community members. Providers and service users reported unreliable psychiatric medication supply and lack of facilities for confidential consultations. Limitations of the study include conducting qualitative interviews in clinical settings and reliance on clinician-researchers in some sites to conduct interviews, which potentially biases respondents to present attitudes and experiences about primary care services in a positive manner. Conclusions: Primary care providers' willingness to interact with people with mental illness and receive more training presents an opportunity to address stigmatizing beliefs and stereotypes. This study also raises important methodological questions about the most appropriate strategies to accurately understand attitudes and experiences of people with mental illness. Recommendations are provided for future qualitative research about stigma, such as qualitative interviewing by non-clinical personnel, involving non-clinical staff for recruitment of participants, conducting interviews in non-clinical settings, and partnering with people with mental illness to facilitate qualitative data collection and analysis