Efficient data collection with heterogeneous cooperating objects for environmental monitoring
Environmental Monitoring is the process of retrieving and analysing information about a specific target area, such as a city, a factory or a rural environment. Environmental Monitoring is an essential piece of the puzzle, to get an understanding about how different environmental attributes rely on each other or to understand how the area is influenced by external factors e.g. by human kind. After all, traditional approaches for environmental monitoring – such as wired systems of sensors – require an existing infrastructure, which makes them very costly and inflexible once they are installed. A solution is provided by networks of Cooperating Objects (COs), which are an ideal candidate for automated environmental monitoring. In such a network, each device brings its own resources regarding energy supply, as well as computation and communication capabilities which makes them a scalable solution that can flexibly be adapted for the operator’s needs. Here, a set of static sensors provide reliable monitoring data, which is complemented by on demand readings from autonomous robots, such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). However, keeping in mind the environmental and system dynamics, the deployment and data collection procedures for these systems must also be carefully planned. Furthermore, the operator needs a simple and intuitive interface for the deployment, maintenance and data analysis. This thesis focuses on how environmental characteristics can be made available to the user, by using a network of COs. Specifically, this thesis makes the following contributions: 1. It enables faithful simulations for environmental-aware deployment planning, 2. it considers the system complexity by enabling the simulation of networks of heterogeneous COs, such as Wireless Sensor Networks (WSNs), UAVs and UGVs, 3. it allows maximisation of the throughput to a mobile sink by providing an adaptive communication protocol and 4. it assists non-technical experts in using heterogeneous networks of COs by providing tools for efficient visualisation, data analysis and deployment management. Each of these contributions is one unique and coherent step towards enabling efficient data collection with heterogeneous COs for environmental monitoring. The evaluation was performed in different experimental setups in testbed scenarios, as well as real-world experiments. It shows that the combination of all four contributions allows users to efficiently plan a deployment of networked COs, to collect sensor data with mobile sinks and to analyse the collected data. Based on this work, the outlook shows that it becomes also possible to include collected data into a publicly available knowledge graph – known as the semantic web – that enables users around the world to integrate monitored data into their own applications.
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