# Adaptive Segmentation of Streaming Sensor Data on Edge Devices
## 2 Related Work
### Possible Application Areas  
The application areas, for which there is a need for on-the-fly algorithms allowing for data stream segmentation, smoothing, and compression include, but are not limited to, IoT devices, sensor networks, edge computing, and autonomous vehicles (cars, robots, drones). The need for real-time pre-processing of big data streams coming from multiple sensors results from data noise, bandwidth limitations and energy efficiency requirements. Below, the selected research in three areas (sensor networks, Unmanned Aerial Vehicles teams and robot teams), in which the proposed algorithm could be applied, is presented. Weather prediction generally requires expensive weather stations and supercomputers for computations. An alternative can be the approach using Distributed Sensor Network for collecting data and performing weather prediction computations [56]. Such an approach requires real-time pre-processing, segmentation, smoothing, and compression of data streams because the used weather stations continuously communicate with each other and exchange large amounts of data coming from sensors to compute the predictions. The approach proposed in this paper meets all the requirements to be used in this area of applications--it allows for online \(C^{2}\)-continuous cubic splinelet-based adaptive segmentation, compression, and smoothing of noisy data streams. Using multiple Unmanned Aerial Vehicles (UAVs) for surveillance, environmental monitoring, and rescue operations has become an increasingly popular research topic in the recent years [57; 58]. Tracking single or multiple moving ground targets requires continuously updated and accurate data about their position. The accuracy of data coming from UAV's sensors is crucial for that task. However, data coming from sensors such as GPS, radar, LiDAR, gyroscopes, and accelerometers can be noisy and inaccurate due to many technical reasons. Using multiple coordinated UAVs allows for combining data coming from their sensors and thus using more accurate information about the current target(s) position [57]. Furthermore, the navigation and coordination of the group of UAVs will require real-time continuous exchange of large amounts of data coming from each unit sensors [59]. Using a real-time algorithm that can segment, smooth and compress data streams coming from each UAV's sensors will be crucial for successfully navigating and coordinating a whole team. The online algorithm proposed in this paper not only computes \(C^{2}\)-segmentations, but also smooths and compress data in real time, which makes it fully applicable in such a domain as UAVs' sensors data analytics. The online \(C^{2}\)-continuous segmentation is crucial for UAVs because, for example, the approximated trajectory of a vehicle must be twice differentiable to give a continuous velocity and acceleration. The research on multi-robot systems (MRS) gained importance and developed significantly in the recent years. Some of the most important research problems in MRS domain include communication mechanisms, planning and coordination strategies, and decision-making algorithms [60]. Research issues related to team coordination [61], sharing data, intelligence, and resources between many robots [62] are of great importance. The effective communication between many robots in the case of limited bandwidth resulting from environmental conditions (for example underwater environment), in which teams of robots are operating, is also the subject of intensive research [63]). As in the case of drone teams, also in the case of robot teams coordinating their actions and sharing data, the essential issue is to deal in real time with data streams, which additionally can be noisy and incomplete. In such a case, using a method of segmenting, smoothing and compressing data in real time is crucial. The proposed approach not only does this, but also provides \(C^{2}\)-segmentations, which is of crucial importance when we use the data to plan the trajectories for robots.