Remote Sensing-based Channel Modeling and Deployment Planning for Low-power Wireless Networks

The deployment of low power wireless networks is notoriously effort-demanding, as costly in-field campaigns are required to assess the connectivity properties of the target location and understand where to place the wireless nodes. The characteristics of the environment, both static (e.g., obstacles obstructing the link line of sight) and dynamic (e.g., changes in weather conditions) cause variability in the communication performance, thus affecting the network operation quality and reliability. This translates into difficulties in effectively deploy, plan and manage these networks in real-world scenarios, especially outdoor. Despite the large literature on node placement, existing approaches make over-simplifying assumptions neglecting the complexity of the radio environment. Airborne and satellite Remote Sensing (RS) systems acquire data and images over wide areas, thus enabling one to derive information about these areas at large scale. In this dissertation, we propose to leverage RS systems and related data processing techniques to i) automatically derive the static characteristics of the deployment environment that affect low power wireless communication; ii) model the relation between such characteristics and the communication quality; and iii) exploit this knowledge to support the deployment planning. We focus on two main scenarios: a) the deployment of Wireless Sensor Networks (WSNs) in forests; and b) the communication performance of Internet of Things (IoT) networks based on Long Range (LoRa) wireless technology in the presence of mixed environments. As a first major contribution, we propose a novel WSN node placement approach (LaPS) that integrates remote sensing data acquired by airborne Light Detection and Ranging (LiDAR) instruments, a specialized path loss model and evolutionary computation to identify (near-)optimal node position in forests, automatically and prior to the actual deployment. When low-power WSNs operating at 2.4 GHz are deployed in forests, the presence of trees greatly affects communication. We define a processing architecture that automatically derives local forest attributes (e.g., tree density) from LiDAR data acquired over the target forest. This information is incorporated into a specialized path loss model, which is validated in deployments in a real forest, enabling fine-grained, per-link estimates of the radio signal attenuation induced by trees. Combining the forest attributes derived from LiDAR data with the specialized path loss model and a genetic algorithm, LaPS provides node placement solutions with higher quality than approaches based on a regular placement or on a standard path loss model, while satisfying the spatial and network requirements provided by the user. In addition, LaPS enables the exploration of the impact of changes in the user requirements on the resulting topologies in advance, thus reducing the in-field deployment effort. Moreover, to explore a different low-power wireless technology with starkly different trade-offs, we consider a LoRa-based IoT network operating in i) a free space like communication environment, i.e., the LoRa signal is transmitted from an high altitude weather balloon, traverses a free-of-obstacles space and is received by gateways on the ground; and ii) a mixed environment that contains built-up areas, farming fields and groups of trees, with both LoRa transmitters and receiving gateways close to the ground. These scenarios show a huge gap in terms of communication range, thus revealing to which extent the presence of objects affects the coverage that LoRa gateways can provide. To characterize the mixed environment we exploit detailed land cover maps (i.e., with spatial grain 10x10m2) derived by automatically classifying multispectral remote sensing satellite images. The land cover information is jointly analyzed with LoRa connectivity traces, enabling us to observe a correlation between the land cover types involved in LoRa links and the trend of the signal attenuation with the distance. This analysis opens interesting research venues aimed at defining LoRa connectivity models that quantitatively account for the type of environment involved in the communication by leveraging RS data.