Mika Saari

Data collector service - practical approach with embedded linux

Nowadays embedded systems are one of the most important application areas in information technology. Embedded systems are often used in life critical situations, where reliability and safety are more important criteria than performance. This paper presents a data collector service that has been developed based on embedded Linux, which operates as a key element in a larger intelligent alarm system. The target of this study was to test out how well a cost-efficient single-board computer could be used to gather sensory data, and how this data can be provided for the client over the public Internet. The paper describes the data collector service currently in use and its functionality and also gives a concrete example of how to utilize a microcontroller with an embedded Linux distribution. The paper presents one solution on how to utilize embedded systems for managing and controlling conditions in buildings and also environmental conditions in a smart and cost-effective way.

Embedded Linux controlled sensor network

This study utilizes a simple model for constructing sensor nodes - master controller combinations in the Internet of Things. The model combines hardware and software for embedded systems which measure a predefined set of parameters. The master controller manages several sensor nodes, collects data from them and provides data for clients. The paper introduces a proof-of-concept implementation based on the model. The implementation uses an embedded Linux based small computer and microcontroller based sensor nodes in the context of condition measurement, and represents a way to use wireless data transfer between controller and nodes. The target of this study was to test the model, to determine how well a cost-efficient single-board computer could be used to gather sensory data from several sensor nodes, and how this data can be provided for clients over the public Internet.

Survey of prototyping solutions utilizing Raspberry Pi

Sensor networks are a highly researched application area in the field of Internet of Things (IoT). A key cost and resource question in the development of IoT network sensor solutions is prototype implementation. In this study, the Raspberry Pi—a widely used single board computer—is investigated as it is one of the most commonly used prototyping devices available and is also widely used in scientific research. In this paper, we address which technologies, the usefulness and what kinds of issues arise when the prototyping of a sensor network solution is done with Raspberry Pi. The extant literature is studied by selecting papers with the systematic literature review method. Based on an extensive survey of the selected studies, we found several sensor-based implementations where Raspberry Pi has been used. In addition, this survey revealed subjects, such as e-health and education, which expanded the research topic in new ways. Further research opportunities have been identified in specifying the usefulness of various technologies with single board computers.

Low-energy algorithm for self-controlled Wireless Sensor Nodes

In Internet of Things (IoT), the lifespan of Wireless Sensor Networks (WSN) has often become an issue. Sensor nodes are typically battery powered. However, high energy consumption by Radio Frequency (RF) module limits the lifespan of sensor nodes. In conventional WSN, the frequency of data transmission is normally fixed or adjusted according to requests from the gateway. In this paper, we present a WSN system for intelligent sensing. We propose a low-energy algorithm for sensor data transmission from sensor nodes for such system. In this algorithm, the sensor nodes are able to self-control their data transmission according to the trends of data. We adopt Adaptive Duty Cycle for adjustment of data transmission frequency and Compressive Sensing (CS) for sensor data compression. The simulation results show that Collective Transmission with CS-based data compression achieves 83.34% of RF energy reduction for the best-case transmission and 83.31% of RF energy reduction in the worst-case transmission, compared to the Continuous Transmission.