IoTAbench

Abstract

The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This "Internetof-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer "Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform "apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.

PVS-GEN: Systematic Approach for Universal Synthetic Data Generation Involving Parameterization, Verification, and Segmentation

“…Several studies frequently compare the descriptive statistics of synthetic data and visualize the generated data to evaluate their synthetic data-generation techniques. Comparing the actual data-generation models enables the assessment of how well the generation techniques reflect the empirical data and evaluation the validity of using synthetic data across various datasets [ 30 , 31 , 39 , 59 , 60 , 61 ].…”

Survey of Time Series Data Generation in IoT

“…IoTAbench [ 29 ] is a benchmark toolkit designed for IoT big data scenarios. It contains a Markov chain-based synthetic data generator for smart meter data.…”

“…Researchers in many fields have proposed a variety of time series data generation methods in their respective fields such as biology [ 8 ], database benchmark [ 29 , 30 ], electricity [ 31 ], energy [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], environment [ 40 , 41 , 42 ], finance [ 5 , 43 ], medicine [ 7 ], music [ 44 ], networks [ 45 ], remote sensing [ 46 , 47 , 48 , 49 ] and sensors [ 50 ]. Despite the abundance of research on time series generation, a comprehensive survey that systematically classifies and evaluates the previous work is lacking.…”

“…Based on the underlying algorithms and models used by the existing time series data generation methods, this paper divides these methods into four categories: rule-based methods [ 51 , 52 , 53 ], simulation-model-based methods [ 37 , 45 ], traditional machine-learning-based methods [ 29 , 32 , 39 ] and deep-learning-based methods [ 7 , 30 , 44 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. Rule-based methods use a set of rules or constraints to specify the properties and structure of the data to be generated.…”

Extended Reality for Smart Built Environments Design: Smart Lighting Design Testbed

“…Similarly, [14] provides a simple platform based on open-source code, in which the authors present a solution for remote monitoring in a smart home by using ESP8266 micro-controller and MQTT protocol [3]. This minimizes the IoT system's security risk while also increasing its cost.…”