scholarly journals Approach in the development of lightweight microservice architecture for small data center monitoring system

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Milan Malić ◽  
Dalibor Dobrilović ◽  
Dušan Malić ◽  
Željko Stojanov
Keyword(s):  
Software Architecture ◽  
Real Time ◽  
Data Center ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Data ◽  
Test Results ◽  
Iot Devices ◽  
Sqlite Database ◽  
Microservice Architecture

In the past decade there is a significant trend of implementing IoT technologies and standards in different industries. This trend brings cost reductions to the companies and other benefits as well. One of the main benefits is real-time and uniform data collection. The data are transferred using diverse communication protocols, from the sensor nodes to the centralized application. So far, current approaches in developing applications are not proved itself to be efficient enough in scenarios when a significant amount of data needs to be stored and analyzed. The focus of this paper is on development of software architecture suitable for usage in Internet of Things (IoT) systems where the larger amount of data can be processed in real-time. The software architecture is developed in order to support the sensor network for monitoring the small data center and it is based on microservices. Besides the system and its architecture, this paper presents the method of analysis of system performances in real-time environment. The proposal for lightweight microservice architecture, presented in this paper, is developed with .NET Core and RabbitMQ, with the utilization of MongoDB and SQLite databases systems for storing data collected with IoT devices. In this paper, the system evaluation and research results in different stress scenarios are also presented. Because of its complexity, only the most significant segments of architecture will be presented in this paper. The proposed solution showed that proposed lightweight architecture based on microservices could deal with the larger amount of sensor data in the case of using MongoDB. On the other hand, the usage of SQLite database is not recommended due to the lower performances and test results.

scholarly journals IoT Notary : Attestable Sensor Data Capture in IoT Environments

2022 ◽  
Vol 3 (1) ◽  
pp. 1-30
Author(s):  
Nisha Panwar ◽  
Shantanu Sharma ◽  
Guoxi Wang ◽  
Sharad Mehrotra ◽  
Nalini Venkatasubramanian ◽  
...  
Keyword(s):  
Real Time ◽  
Location Based Services ◽  
Third Party ◽  
Sensor Data ◽  
Published Data ◽  
Resource Limited ◽  
User Data ◽  
Data Capturing ◽  
Iot Devices ◽  
The University

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced—IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals’ privacy or service integrity. To address such concerns, we propose IoT Notary , a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable “proof-of-integrity,” based on which a verifier can attest that captured sensor data adhere to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California, Irvine to provide various real-time location-based services on the campus. We present extensive experiments over real-time WiFi connectivity data to evaluate IoT Notary , and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day’s data in less than 2 s even using a resource-limited device.

scholarly journals Implementation of Oxygen Saturation Sensor Data Acquisition Based on Bluetooth Low Energy Protocol

2021 ◽  
Author(s):  
Muhammad Fahmi Ali Fikri ◽  
Dany Primanita Kartikasari ◽  
Adhitya Bhawiyuga
Keyword(s):  
Data Acquisition ◽  
Data Center ◽  
Low Energy ◽  
Sensor Data ◽  
Data Delivery ◽  
Bluetooth Low Energy ◽  
Data Redundancy ◽  
Iot Devices ◽  
Store And Forward ◽  
Sensor Data Acquisition

Sensor data acquisition is used to obtain sensor data from IoT devices that already provide the required sensor data. To acquire sensor data, we can use Bluetooth Low Energy (BLE) protocol. This data acquisition aims to process further data which will later be sent to the server. Bluetooth Low Energy (BLE) has an architecture consisting of sensors, gateways, and data centers, but with this architecture, there are several weaknesses, namely the failure when sending data to the data center due to not being connected to internet network and data redundancy at the time of data delivery is done. The proposed solution to solve this problem is to create a system that can acquire sensor data using the Bluetooth Low Energy (BLE) protocol with use a store and forward mechanism and checking data redundancy. The proposed system will be implemented using sensors from IoT devices, the gateway used is Android devices, and using the Bluetooth Low Energy protocol to acquire data from sensors. Then the data will be sent to the cloud or server. The results of the test give the results of the system being successfully implemented and IoT devices can be connected to the gateway with a maximum distance of 10 meters. Then when the system stores, for every minute there is an increase in data of 4 kb. Then there is no data redundancy in the system.

scholarly journals A Cloud Based Disaster Management System

2020 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Omar Cheikhrouhou ◽  
Anis Koubaa ◽  
Anis Zarrad
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Game Engine ◽  
New Paradigm ◽  
Lossy Networks ◽  
Training Environment ◽  
3D Environment

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.

scholarly journals Distributed Fusion of Sensor Data in a Constrained Wireless Network

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1006 ◽  
Author(s):  
Charikleia Papatsimpa ◽  
Jean-Paul Linnartz
Keyword(s):  
Large Scale ◽  
Sensor Nodes ◽  
Sensor Data ◽  
A Posteriori ◽  
Likelihood Ratios ◽  
Process Data ◽  
Local Data ◽  
Fusion Algorithm ◽  
Distributed Information ◽  
Iot Devices

Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be enormous but highly redundant. Devices will be required to pre-process data locally or at least in their vicinity. Thus, local data fusion, subject to constraint communications will become necessary. In that sense, distributed architectures will become increasingly unavoidable. Anticipating this trend, this paper addresses the problem of presence detection in a building as a distributed sensing of a hidden Markov model (DS-HMM) with limitations on the communication. The key idea in our work is the use of a posteriori probabilities or likelihood ratios (LR) as an appropriate “interface” between heterogeneous sensors with different error profiles. We propose an efficient transmission policy, jointly with a fusion algorithm, to merge data from various HMMs running separately on all sensor nodes but with all the models observing the same Markovian process. To test the feasibility of our DS-HMM concept, a simple proof-of-concept prototype was used in a typical office environment. The experimental results show full functionality and validate the benefits. Our proposed scheme achieved high accuracy while reducing the communication requirements. The concept of DS-HMM and a posteriori probabilities as an interface is suitable for many other applications for distributed information fusion in wireless sensor networks.

scholarly journals Architecture and Architectural Patterns for Mobile Augmented Reality

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Donald Munro ◽  
Andre Calitz ◽  
Dieter Vogts
Keyword(s):  
Augmented Reality ◽  
Software Architecture ◽  
Real Time ◽  
Sensor Data ◽  
Mobile Augmented Reality ◽  
Software Developers ◽  
Real Time Processing ◽  
Main Requirement ◽  
Architectural Patterns ◽  
Architecture And Design

A software architecture codifies the design choices of software developers, which defines a modularorganizational spine for the design of a software artefact. Different architectures may bespecified for different types of artefacts, a real-time interactive artefact, for example, wouldhave markedly different requirements to those of a batch based transactional system. The use ofsoftware architecture becomes increasingly important as the complexity ofartefacts increases. Augmented Reality blends the real world observed through a computerinterface, with a computer generated virtual world. With the advent ofpowerful mobile devices, Mobile Augmented Reality (MAR)applications have become increasingly feasible, however the increased power hasled to increased complexity. Most MAR research has been directed towardstechnologies and not design resulting in a dearth of architecture and design literature for MAR. This research is targeted at addressing this void. The main requirement that a MAR architecture must meet isidentified as being the efficient real-time processing of data streams such asvideo frames and sensor data. A set of highly parallelised architecturalpatterns are documented within the context of MAR that meet thisrequirement. The contribution of this research is a software architecture, codifiedas architectural patterns, for MAR.

Web-Based Data Inquiry and Real-Time Control of Structural Health Monitoring Sensor Networks

2009 ◽  
Author(s):  
Bo Chen ◽  
Wenjia Liu ◽  
Jinjiang Wang ◽  
Justin Slepak
Keyword(s):  
Real Time ◽  
Operating Mode ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Data Repository ◽  
Real Time Control ◽  
Web Based ◽  
Structural Health ◽  
Time Control ◽  
Data Inquiry

This paper presents a Web-based data inquiry and real-time control of sensor’s operating mode for structural health monitoring sensor networks. The main objective of the presented system is to provide a Web interface for real-time sensor data visualization, sensor-level damage diagnosis, and control of sensor’s operating mode. Web services are available both on distributed sensor nodes and a data repository machine. Users can request Web pages hosted on the sensor nodes or the data repository machine by specifying corresponding sensor IDs. The ability of directly accessing data on sensor nodes via internet allows users to monitor a structure’s performance in a timely manner. The damage diagnosis algorithms implemented on the sensor nodes help users to assess the structural health conditions without the need of transmitting sensor data to a central data station. The presented system also provides the capability of dynamically changing sensor’s operating mode through the Web interface. This feature greatly enhances the flexibility of the system to accommodate different sensing needs and achieve a long lifespan. The system has been tested in the Laboratory to validate its capabilities.

scholarly journals Scheduling in Sensor Grid Middleware for Telemedicine Using ABC Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
T. Vigneswari ◽  
M. A. Maluk Mohamed
Keyword(s):  
Real Time ◽  
Microelectromechanical Systems ◽  
Scheduling Algorithm ◽  
Vital Signs ◽  
Base Station ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Time Data ◽  
Grid Middleware ◽  
Real Time Data

Advances in microelectromechanical systems (MEMS) and nanotechnology have enabled design of low power wireless sensor nodes capable of sensing different vital signs in our body. These nodes can communicate with each other to aggregate data and transmit vital parameters to a base station (BS). The data collected in the base station can be used to monitor health in real time. The patient wearing sensors may be mobile leading to aggregation of data from different BS for processing. Processing real time data is compute-intensive and telemedicine facilities may not have appropriate hardware to process the real time data effectively. To overcome this, sensor grid has been proposed in literature wherein sensor data is integrated to the grid for processing. This work proposes a scheduling algorithm to efficiently process telemedicine data in the grid. The proposed algorithm uses the popular swarm intelligence algorithm for scheduling to overcome the NP complete problem of grid scheduling. Results compared with other heuristic scheduling algorithms show the effectiveness of the proposed algorithm.

scholarly journals Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

2019 ◽  
Vol 8 (12) ◽  
pp. 5483-5489
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Outlier Detection ◽  
Window Size ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Training Dataset ◽  
Time Motion

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.

scholarly journals Sensor Virtualization Module: Virtualizing IoT Devices on Mobile Smartphones for Effective Sensor Data Management

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
JeongGil Ko ◽  
Byung-Bog Lee ◽  
Kyesun Lee ◽  
Sang Gi Hong ◽  
Naesoo Kim ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Management ◽  
Software Architecture ◽  
Sensor Data ◽  
Application Development ◽  
Mobile Platforms ◽  
Embedded Devices ◽  
End To End ◽  
Iot Devices ◽  
Sensor Data Management

The vision of theInternet of Things (IoT)is coming closer to reality as a large number of embedded devices are introduced to our everyday environments. For many commercial IoT devices, ubiquitously connected mobile platforms can provide global connectivity and enable various applications. Nevertheless, the types of IoT resource-utilizing applications are still limited due to the traditional stovepipe software architecture, where the vendors provide supporting software on an end-to-end basis. This paper tries to address this issue by introducing theSensor Virtualization Module (SVM), which provides a software abstraction for external IoT objects and allows applications to easily utilize various IoT resources through open APIs. We implement the SVM on both Android and iOS and show that the SVM architecture can lead to easy development of applications. We envision that this simplification in application development will catalyze the development of various IoT services.

scholarly journals Classification of cow’s behaviors based on 3-DoF accelerations from cow’s movements

2019 ◽  
Vol 9 (3) ◽  
pp. 1656
Author(s):  
Phung Cong Phi Khanh ◽  
Kieu Thi Nguyen ◽  
Nguyen Dinh-Chinh ◽  
Tran Duc-Nghia ◽  
Hoang Quang Trung ◽  
...  
Keyword(s):  
Decision Tree ◽  
Data Acquisition ◽  
Real Time ◽  
Wireless Transmission ◽  
Sensor Data ◽  
Test Results ◽  
Acceleration Sensor

Cow’s behavior classification helps people to monitor cow activities, thus the health and physiological periods of cows can be well tracked. To classify the behavior of cows, the data from the 3-axis acceleration sensor mounted on their neck is often used. Data acquisition and preprocessing of sensor data is required in this device. We acquire data from the 3-axis acceleration sensor mounted on the cows’neck and send to the microcontrollter. At the microcontroller, a proposed decision tree is applied in real-time manner to classify four important activities of the cows (standing, lying, feeding, and walking). Finally, the results can be sent to the server through the wireless transmission module. The test results confirm the reliability of the proposed device.

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