The integration of machine learning (ML) approaches in sensor-based applications in the field of pervasive computing is becoming increasingly prominent due to the increasing number of sensor-based applications in general and the continuous adaption of ML approaches to new domains. Several ML models are used within a processing pipeline that operates on the same sensor data. Still, the cloud computing approach is a straightforward solution where all sensor data is sent to the cloud before processing, which is inefficient according to resource utilization. Appropriate management of the different processing tasks for ML models enhances resource utilization.This paper proposes an architecture for resource-aware classification empowered by an ML model management (MLMM) framework and a distributed data stream management system (DDSMS). First, the classification pipeline is decomposed and implemented as data stream operators. Second, ML models are retrieved from an MLMM framework considering preprocessing, segmentation, and feature alignment to enable an effective redundancy elimination. Finally, the classification pipeline is deployed using resource-aware operator placement optimization. The evaluation results on a real-world scenario of a sensor-based activity classification pipeline for dairy cows show that our approach can reduce network utilization up to 98.9%.

The rapid development in IoT infrastructure for collecting Spatio-temporal data calls for various machine learning model evolution. Several models can be used on data streams to extract knowledge about different aspects of mobility. To monitor mobility in a dynamic environment like a city or museum, it is necessary to track all ML models deployed in different locations and times. The goal of this research work is to develop a context-aware framework for monitoring mobility in dynamic environments. To develop such a framework, it is essential to define the challenges of managing different ML models on Spatio-temporal data streams.

The increasing availability of indoor and outdoor location sensors raises the interest for understanding the mobility in different places, e.g., in museums, train stations, or cities. One of the highly used approaches for understanding the mobility of individuals is to detect staypoints from trajectories. Although offline detection of staypoints fits best for long-term planning, some applications require online staypoint detection from trajectories, like online recommendation systems. Latency plays an important role here. In this paper, we propose a stream-based approach for staypoint detection, which can be realized by applying a Data Stream Management System. We claim that the proposed approach can detect staypoints with low latency from the high-frequency location update streams. To evaluate our approach, we compare it with a batch-based approach on real data from an indoor tracking system and Geolife dataset. The results demonstrate that the online approach detects staypoints with much lower latency compared to traditional approaches like offline and batch processing. Moreover, we prove that the accuracy of the stream-based approach is similar to the batch-based approach.

Abstract The recent evolution of the Internet of Things into a cyber-physical reality has spawned various challenges from a data management perspective. In addition, IoT platform designers are faced with another set of questions. How can platforms be extended to smoothly integrate new data management functionalities? Currently, data processing related tasks are typically realized by manually developed code and functions which creates difficulties in maintenance and growth. Hence we need to explore other approaches to integration for IoT platforms. In this paper we cover both these aspects: (1) we explore several emerging data management challenges, and (2) we propose an IoT platform integration model that can combine disparate functionalities under one roof. For the first, we focus on the following challenges: sensor data quality, privacy in data streams, machine learning model management, and resource-aware data management. For the second, we propose an information-integration model for IoT platforms. The model revolves around the concept of a Data-Sharing Market where data management functionalities can share and exchange information about their data with other functionalities. In addition, data-sharing markets themselves can be combined into networks of markets where information flows from one market to another, which creates a web of information exchange about data resources. To motivate this work we present a use-case application in smart cities.