A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

Abstract

The term Internet of Things (IoT) refers to the interaction and communication between billions of devices that produce and exchange data related to real world objects (i.e. Things). Extracting higher-level information from the raw sensory data captured by the devices and representing this data as machine-interpretable or human-understandable information has several interesting applications. Deriving raw data into higher-level information representations demands mechanisms to find, extract and characterise meaningful abstractions from the raw data. This meaningful abstractions then have to be presented in a human and/or machineunderstandable representation. However, the heterogeneity of the data originated from different sensor devices and application scenarios such as e-health, environmental monitoring and smart home applications and the dynamic nature of sensor data make it difficult to apply only one particular information processing technique to the underlying data. A considerable amount of methods from machine-learning, the semantic web, as well as pattern and data mining have been used to abstract from sensor observations to information representations. This paper provides a survey of the requirements and solutions and describes challenges in the area of information abstraction and presents an efficient workflow to extract meaningful information from raw sensor data based on the current state-of-the-art in this area. The paper also identifies research directions at the edge of information abstraction for sensor data. To ease the understanding of the abstraction workflow process, we introduce a software toolkit that implements the introduced techniques and motivates to apply them on various data sets.

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

“…Interested readers can refer to the survey of [Figo et al 2010] for a detailed discussion about di erent pre-processing techniques. After pre-processing of the raw data, dimensionality reduction techniques can be applied to obtain low granularity data that retains the most relevant information of the original form [Ganz et al 2015].…”

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

“…In the following section, we introduce a general workflow [23] to extract meaningful information from raw sensor data that has been defined by examining several different approaches for information abstraction in the domain of sensor data [24]. The approaches that have been examined follow the workflow as shown in Figure 8.…”

“…iii) Data Classifications Phase After the raw data has passed to the dimensionality reduction and the features of the data produced by IoT devices have been extracted [23] to detect the outliers [29]. In analysis of the time-series the similarity can be computed by comparing the observed values and can be computed also using meta information such as time or type.…”

“…Future research will focus on extending the platform with new analytics techniques to work with high performance computing and Big Data analytics tools such as Hadoop [23]. In addition revamp the current system architecture to apply lambda architecture that consists of Batch, Serving and real time layer.…”

Energy efficient IoT virtualization framework with passive optical access networks

“…It is predicted that the number of IoT devices to reach 50 billion in the next few years [1]. Having such a vast number of interconnected devices paved the way for futuristic smart applications in manufacturing, smart transportation, agriculture …etc.…”