Goal-Driven Context-Aware Data Filtering in IoT-Based Systems

Abstract

One of the crucial research issues in an IoT-based system is how to manage the huge amount of data transmitted by the potentially large number of sensors that form the system. Prior research has focused on centralized cloud-based 'Big Data' architectures for collecting, collating and analyzing the data. However, most of these scenarios accumulate thousands of petabytes in a short period of time, increasing the demand for more storage, and also slowing down speed of data analysis. Hence for real-time scenarios, e.g., agricultural crop tracking, traffic management, etc., such an approach would be impractical. Moreover, depending on the context in which the data is generated and is to be used, only a fraction of the data would be needed for analysis. Therefore, the challenges are to determine which data to keep and which to discard for both short term and long term usage, and define the contextual parameters along which this filtering is to be done. Hence one key problem addressed in this paper is how to define what data the user needs so that filtering algorithms can be defined to extract the data needed. To that end, in this paper, we present a goal driven, context-aware data filtering, transforming and integration approach for IoT-based systems. We propose a data warehousebased data model for specifying the data needed at particular levels of granularity and frequency, that drive data storage and representation (aligned with the Semantic Sensor Network ontology). Throughout our paper, we illustrate our ideas via a realistic running example in the smart city domain, with emphasis on traffic management, and also present a proof of concept prototype.

Thiết kế và thực hiện cổng chuyển đổi LoRa - GSM giám sát các nút dựa trên công nghệ LoRa

“…® [6]. Áp dụng cách thức sàng lọc dữ liệu trên hệ thống IoT theo ngữ cảnh, thời gian, thời tiết [7]. GSM khắc phục được hạn chế ở những vùng thiếu kết nối mạng wifi và enthernet [8].…”

A Survey on Trustworthiness for the Internet of Things

“…Data reduction (filtering) mechanisms optimize the data flow between mist, fog, and cloud [22], [144][145][146][147][148]. However, data filtering must deal with mist and fog resource constraints regarding the processing and storage capacity for a smart service (like smart farming) or with different smart applications of this service (like smart irrigation, smart water management, or smart crop growth monitoring).…”

“…Fog data reduction (filtering) aims to reduce the amount of data stored and transmitted by the mist and fog stages. These techniques can discard redundant data or data with no relevant variations [48], [147]. However, the fog needs to consider the analytic data model and the smart application for data filtering because the filtered data will represent the original data and generate trusted decisions [147].…”

An Architectural Model for Fog Computing

“…The use and presentation of data should adapt to the context of the demand [27]. For such issues, a goal-driven and context-aware data filtering approach have been the focus of the system in [28]. Though, in case of an aggregated or time-dependent sensing activity, issue like mobility support may arise.…”