scholarly journals LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5044
Author(s):  
Mehran Behjati ◽  
Aishah Binti Mohd Noh ◽  
Haider A. H. Alobaidy ◽  
Muhammad Aidiel Zulkifley ◽  
Rosdiadee Nordin ◽  
...  
Keyword(s):  
Data Collection ◽  
Monitoring System ◽  
Large Scale ◽  
Service Providers ◽  
Wide Area ◽  
Management Approach ◽  
Communication Link ◽  
Reliable Communication ◽  
Wide Range ◽  
Smart Farming

Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

GOVERNMENT REGULATIONS OF THE USING CLOUD SERVICES

2022 ◽  
Author(s):  
Olexander Melnikov ◽  
◽  
Konstantin Petrov ◽  
Igor Kobzev ◽  
Viktor Kosenko ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Government Agencies ◽  
Remote Work ◽  
Cloud Infrastructure ◽  
Wide Range ◽  
Technological Base

The article considers the development and implementation of cloud services in the work of government agencies. The classification of the choice of cloud service providers is offered, which can serve as a basis for decision making. The basics of cloud computing technology are analyzed. The COVID-19 pandemic has identified the benefits of cloud services in remote work Government agencies at all levels need to move to cloud infrastructure. Analyze the prospects of cloud computing in Ukraine as the basis of e-governance in development. This is necessary for the rapid provision of quality services, flexible, large-scale and economical technological base. The transfer of electronic information interaction in the cloud makes it possible to attract a wide range of users with relatively low material costs. Automation of processes and their transfer to the cloud environment make it possible to speed up the process of providing services, as well as provide citizens with minimal time to obtain certain information. The article also lists the risks that exist in the transition to cloud services and the shortcomings that may arise in the process of using them.

scholarly journals RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

10.2196/11734 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e11734 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Pauline Conde ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Mobile Health ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
The European Union ◽  
Wide Range ◽  
Remote Assessment

Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Deworm the World

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
School Children ◽  
Large Scale ◽  
Activity Tracking ◽  
Large Scale System ◽  
Wide Range ◽  
The World ◽  
The Right

Deworm the World serves millions of school children every year. Monitoring on such a large scale can amplify the difficulty of developing a right-fit system: How can an organization ensure credible data collection across a wide range of sites and prioritize actionable information that informs implementation? How can such a large-scale system rapidly respond to issues once identified? This case illustrates the challenge of finding credible and actionable activity tracking measures. How does Deworm the World apply the credible, actionable, and responsible principles to determine the right amount of data to collect and the right time and place at which to collect it?

Development of Executive Function in Middle Childhood: a Large-Scale, In-School, Longitudinal Investigation

2021 ◽  
Author(s):  
Jessica Younger ◽  
Kristine D. O’Laughlin ◽  
Joaquin Anguera ◽  
Silvia A. Bunge ◽  
Emilio Ferrer ◽  
...  
Keyword(s):  
Academic Achievement ◽  
Executive Function ◽  
Data Collection ◽  
Middle Childhood ◽  
Large Scale ◽  
Assessment Tool ◽  
Group Settings ◽  
Wide Range ◽  
Eighth Grade Students ◽  
Control Procedures

This manuscript describes data collection, cleaning, and quality control procedures used in a large-scale, longitudinal, in-school study of executive function skills (EFs) and academic achievement in middle childhood, Project iLEAD (in-school Longitudinal Executive Function and Academic Achievement Database). Assessments were administered in real-world educational settings in a large sample of third through eighth grade students (ages 7 to 14; N = 1,280) over two years, with eight data collection timepoints in group settings. We assessed students with a novel, mobile EF assessment tool Adaptive Cognitive Evaluation (ACE). This battery included 11 tasks that were each designed to adapt to user performance in a trial-wise manner, allowing the same battery of tasks to be used multiple times within the same individual, and across a wide range of ages. Data quality analyses revealed that the adaptive algorithms were successful in equating challenge levels across ages 7 through 14 for 10 of 11 tasks. Data for each task were found to be approximately normally distributed and split-half reliability was acceptable across both accuracy and reaction time. ACE thus provides a reliable way to assess EFs in middle childhood using the same tasks while maintaining appropriate challenge level without facing ceiling or floor effects.

scholarly journals UAV-Assisted Wide Area Multi-Camera Space Alignment Based on Spatiotemporal Feature Map

2021 ◽  
Vol 13 (6) ◽  
pp. 1117
Author(s):  
Jing Li ◽  
Yuguang Xie ◽  
Congcong Li ◽  
Yanran Dai ◽  
Jiaxin Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Superior Performance ◽  
Wide Area ◽  
Feature Map ◽  
Map Construction ◽  
Wide Range ◽  
Spatiotemporal Feature ◽  
Ground Point ◽  
Matching Strategy ◽  
Ground Monitoring

In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to represent the input aerial and ground monitoring data. In this way, the motion consistency across view is well mined to overcome the great perspective gap between the UAV and ground cameras. To obtain the corresponding relationship between their pixels, we propose a cross-view spatiotemporal matching strategy. Through solving relative relationship with the above air-to-ground point correspondences, all ground cameras can be aligned into one surveillance space. The proposed approach was evaluated in both simulation and real environments qualitatively and quantitatively. Extensive experimental results demonstrate that our system can successfully align all ground cameras with very small pixel error. Additionally, the comparisons with other works on different test situations also verify its superior performance.

scholarly journals RADAR-base: An Open Source mHealth Platform for Collecting, Monitoring and Analyzing Data Using Sensors, Wearables, and Mobile Devices (Preprint)

2018 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Maximilian Kerz ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
User Interfaces ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
Multiple Sources ◽  
High Resolution Data ◽  
Wide Range

BACKGROUND With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable and extensible platform is of high interest to the open source mHealth community. The EU IMI RADAR-CNS program is an exemplar project with the requirements to support collection of high resolution data at scale; as such, the RADAR-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. OBJECTIVE Wide-bandwidth networks, smartphone penetrance and wearable sensors offer new possibilities for collecting (near) real-time high resolution datasets from large numbers of participants. We aimed to build a platform that would cater for large scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security and privacy. METHODS RADAR-base is developed as a modular application, the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides two main mobile apps for data collection, a Passive App and an Active App. Other 3rd Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. RESULTS General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy and Depression cohorts. CONCLUSIONS RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

scholarly journals A Proposed Agent System for Network Monitoring

2014 ◽  
Vol 11 (2) ◽  
pp. 284-291
Author(s):  
Baghdad Science Journal
Keyword(s):  
Data Collection ◽  
Network Management ◽  
Large Scale ◽  
Agent Technology ◽  
Management Approach ◽  
Successful Management ◽  
Data Collection And Analysis ◽  
Working Together ◽  
Large Scale Networks ◽  
The Cost

The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presented in real time which is minimizing the cost that represents the important factor to successful management of networks that was achieved using agents.

Optimal placement of wide area monitoring system components in active distribution networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Surinder Chauhan ◽  
Ratna Dahiya
Keyword(s):  
Monitoring System ◽  
Distribution System ◽  
Distribution Networks ◽  
Wide Area ◽  
Optimal Placement ◽  
Communication Link ◽  
Placement Problem ◽  
Wide Area Monitoring ◽  
Area Monitoring ◽  
Active Distribution Networks

Abstract The optimal placement of micro-Phasor Measurement Units (µPMUs) reduce the cost of wide area monitoring system (WAMS) in active distribution networks (ADNs); therefore, it is becoming a popular research topic. However, µPMUs alone cannot minimize the WAMS cost. An appropriate location of Phasor Data Concentrator (PDC) and a fiber optic communication link (CL) that transfers data from µPMUs to PDCs also need to be optimized. Hence this paper proposes a hybrid algorithm that determines the optimal cost-effective solution of the placement problem of µPMUs, PDC, and CL. The proposed algorithm uses the graph theory and binary integer linear programming (BILP) with the constraints of distribution generation (DG) presence, regular network reconfiguration, and maximizing system redundancy. The proposed algorithm is tested on IEEE 69 bus, IEEE 123 bus, and 345 bus active distribution system. The results obtained show the reduction in cost mainly through the CL and optimal placement of µPMUs in decentralized WAMS.

scholarly journals Network Latency in Systems Event Monitoring for Multiple Locations

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Lukasz Kufel
Keyword(s):  
Data Collection ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Wide Area ◽  
Wide Area Networks ◽  
Event Monitoring ◽  
Geographic Locations ◽  
Impact Monitoring ◽  
Network Latency ◽  
Events Data

Network latency is one of the key parameters to consider when designing and implementing remote monitoring for security and system events. This paper describes how network latency may impact monitoring over wide area networks, especially when the monitoring system is hundreds or thousands of miles away from the monitored servers. Furthermore, an idea of local distributor is proposed to reduce the time of events data collection from multiple geographic locations.

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