International audienceThe Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context, sensors are surrounding our environment (e.g., cars, buildings, smartphones) and continuously collect data about our living environment. Thus, the IoT is a prototypical example of Big Data. The contribution of this paper is to define a software architecture supporting the collection of sensor-based data in the context of the IoT. The architecture goes from the physical dimension of sensors to the storage of data in a cloud-based system. It supports Big Data research effort as its instantiation supports a user while collecting data from the IoT for experimental or production purposes. The results are instantiated and validated on a project named SMARTCAMPUS, which aims to equip the SophiaTech campus with sensors to build innovative applications that supports end-users

Sensor networks empower Internet of Things (IoT) applications by connecting them to physical world measurements. However, the necessary use of limited bandwidth networks and battery-powered devices makes their optimal configuration challenging. An over-usage of periodic sensors (i.e. too frequent measurements) may easily lead to network congestion or battery drain effects, and conversely, a lower usage is likely to cause poor measurement quality. In this paper we propose a middleware that continuously generates and exposes to the sensor network an energy-efficient sensors configuration based on data live observations. Using a live learning process, our contributions dynamically act on two configuration points: (i) sensors sampling frequency, which is optimized based on machine-learning predictability from previous measurements, (ii) network usage optimization according to the frequency of requests from deployed software applications. As a major outcome, we obtain a self-adaptive platform with an extended sensors battery life while ensuring a proper level of data quality and freshness. Through theoretical and experimental assessments, we demonstrate the capacity of our approach to constantly find a near-optimal tradeoff between sensors and network usage, and measurement quality. In our experimental validation, we have successfully scaled up the battery lifetime of a temperature sensor from a monthly to a yearly basis.

Abstract. Sensors networks are the backbone of large sensing infrastructures such as Smart Cities or Smart Buildings. Classical approaches suffer from several limitations hampering developers' work (e.g., lack of sensor sharing, lack of dynamicity in data collection policies, need to dig inside big data sets, absence of reuse between implementation platforms). This paper presents a tooled approach that tackles these issues. It couples (i) an abstract model of developers' requirements in a given infrastructure to (ii) timed automata and code generation techniques, to support the efficient deployment of reusable data collection policies on different infrastructures. The approach has been validated on several real-world scenarios and is currently experimented on an academic campus.