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.
Highlights
Over the last years, the Internet of Things has evolved from a high-level vision of always-connected devices to a real cyber-physical system class that appears in many application domains, from healthcare Pike et al (2019) over smart cities Zanella et al (2014) to smart farming and precision agriculture Kamilaris et al (2016)
This leads to the situation where people are not aware of the information they share with others, which paves the way for the challenge of privacy preserving data stream processing, which we refer to as the CPrivacy challenge
This challenge focuses on the following research question: How can a global query be distributed in a geographically-distributed data stream management system considering the limitations of the Internet of Things (IoT) ecosystem?
Summary
The Internet of Things has evolved from a high-level vision of always-connected devices to a real cyber-physical system class that appears in many application domains, from healthcare Pike et al (2019) over smart cities Zanella et al (2014) to smart farming and precision agriculture Kamilaris et al (2016). 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. IoT devices are ubiquitous, sensors are present in more devices that produce continuous streams of data about their environment This leads to the situation where people are not aware of the information they share with others, which paves the way for the challenge of privacy preserving data stream processing, which we refer to as the CPrivacy challenge. Data management is distributed geographically between the sensor/actuators, gateways up to the cloud This distribution leads to a high heterogeneity between the processing nodes in terms of computing resources, system security and connectivity; this creates the opportunity for building solutions that tackle the management problem from a resource-aware approach, which we call the CResource challenge.
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