## 1 Introduction  
Since the Internet of Things (IoT) paradigm was first coined more than a decade ago [1], wireless sensor networks (WSNs) were included among its enabling technologies as flexible, low-cost systems suitable to build dense and cost-effective monitoring infrastructures [2]. WSNs have relatively low material and deployment costs, since they do not need wiring, can self-configure and can operate for extended periods of time with very low, if any, scheduled maintenance. Sensor networks are also considered among the best techniques for environmental data collection for a variety of open-space applications, either close to human daily activities or remote, in the wild. WSNs are typically made of multiple nodes, which are small and portable embedded computing systems interfaced with specialized transducers and radios for short-range communication. They are capable of autonomously monitoring, processing and transmitting various parameters at diverse locations for extended periods of time, using very limited energy and often without any maintenance in their lifetime. More than a decade after the WSN vision, the application domain experts expect the technology to solve application demands inexpensively and with low effort. However, the multidisciplinary challenges raised by WSN optimization to multiple application needs have been constantly acknowledged over the past decade, especially for outdoor applications [3; 4; 5]. Most networks remain relatively small in size and/or are deployed for short periods of time, and node programming often requires experienced software engineers [6]. High cost and perceived low reliability are still important barriers [7], out of which maintenance cost may significantly increase in the case of remote deployments or in locations that are difficult to access. As for most maturing technologies, WSNs reached the stage where standards consolidate most existing achievements. IEEE 802.15.4 and 6 LoWPAN set the groundwork for applications with IPv6 support. Software-defined radio efforts build on these, aiming at more robust and versatile communication architectures through antenna-, frequency-, modulation- and data-rate diversity, on which services for diverse application spaces can be built. At the same time, environmental monitoring has constantly grown in importance. Routinely collected data about the state and the evolution of the environment can help to detect and alert of hazardous events and to assess and optimize the long-term sustainability of, e.g., population growth, urban sprawl, invasive species, climate change and habitat destruction. Nevertheless, the outdoor monitoring application domain is often challenging [8]. The quality of radio communication between nodes can be unpredictable, especially during atmospheric events (e.g., heavy rain, snow, frost, fog), due to plant and foliage growth or their movement due to wind, or due to the accumulation of dirt or insect nests on nodes. Repeated mechanical stress induced by large daily temperature variations may prematurely break the nodes, increasing the operating cost, since maintenance in open nature is usually difficult. Communication protocol scalability and the cost of nodes and field deployment may be stretched for applications that require thousands or tens of thousands of nodes for pervasive monitoring. WSN platforms typically rely on existing hardware and software components to support a broad range of applications, often using some cross-layer optimizations. However, applications with stringent requirements, such as low cost, a large number of nodes, long service time, fast field deployment and reduced maintenance, can motivate custom design and optimization of the entire WSN platform. In this paper, we focus on the needs of a demanding application, pervasive wildfire monitoring using _in situ_ distributed temperature sensors. Early detection of forest wildfires is very important for fire fighting success and to effectively limit damage and cost.