scholarly journals Data communication for drone-enabled internet of things

2021 ◽  
Vol 22 (2) ◽  
pp. 1216
Author(s):  
Yousra Abdul Alsahib S. Aldeen ◽  
Haider Mohammed Abdulhadi
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Data Communication ◽  
Cost Effective ◽  
Process Data ◽  
Smart Services ◽  
Data Throughput ◽  
Recent Advancement ◽  
New Challenges ◽  
Time Critical

<p><span>Internet of things (IoT) is one of the prominent emerged technology of interconnected devices for people convenient and smart services. Recent advancement in this area caused various new challenges especially deployment of infrastructure. In order to fulfill the network requirements, the dynamic and dedicated drone networks have designed as a cost effective and flexible solution. The technologies of IoT and drone are emerged to collect, forward the data for further process. Data communication among drones and IoT infrastructure is new area of research where various different existing protocol are used. However, still this area need attention due to mobility of drones, obstacles and interferences in these networks. This paper proposes a Drone enabled Data Communication for Internet of Things (DDC-IoT) as a data communication solution for IoT networks, data collection centers and drones. The proposed data commination solution is tested in simulation to analyze its performance especially for real time critical applications in terms of data throughput and data delay.</span></p>

scholarly journals Machine Learning Applications: The Past and Current Research Trend in Diverse Industries

Inventions ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 8 ◽  
Author(s):  
Omid Ameri Sianaki ◽  
Ashkan Yousefi ◽  
Azadeh Tabesh ◽  
Mehregan Mahdavi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Smart Cities ◽  
Research Trend ◽  
The Internet ◽  
Process Data ◽  
Learning Techniques ◽  
Machine Learning Applications

Dramatic changes in the way we collect and process data has facilitated the emergence of a new era by providing customised services and products precisely based on the needs of clients according to processed big data. It is estimated that the number of connected devices to the internet will pass 35 billion by 2020. Further, there has also been a massive escalation in the amount of data collection tools as Internet of Things devices generate data which has big data characteristics known as five V (volume, velocity, variety, variability and value). This article reviews challenges, opportunities and research trends to address the issues related to the data era in three industries including smart cities, healthcare and transportation. All three of these industries could greatly benefit from machine learning and deep learning techniques on big data collected by the Internet of Things, which is named as the internet of everything to emphasise the role of connected devices for data collection. In the smart grid portion of this paper, the recently developed deep reinforcement learning techniques and their applications in Smart Cities are also presented and reviewed.

scholarly journals Lightweight Coordination Patterns for Applications of the Internet of Things

2020 ◽  
Vol 25 (2) ◽  
pp. 117-123
Author(s):  
Waseem Akhtar Mufti
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Computer Network ◽  
Programming Model ◽  
Data Communication ◽  
The Internet ◽  
Automatic Data ◽  
Distributed Cluster ◽  
The Internet Of Things

AbstractApplications of the Internet of Things (IoT) are famously known for connecting devices via the internet. The main purpose of IoT systems (wireless or wired) is to connect devices together for data collection, buffering and data gateway. The collected large size of data is often captured from remote sources for automatic data analytics or for direct decision making by its users. This paper applies the programming pattern for Big Data in IoT systems that makes use of lightweight Java methods, introduced in the recently published work on ClientNet Distributed Cluster. Considering Big Data in IoT systems means the sensing of data from different resources, the network of IoT devices collaborating in data collection and processing; and the gateways servers where the resulting big data is supposed to be directed or further processed. This mainly involves resolving the issues of Big Data, i.e., the size and the network transfer speed along with many other issues of coordination and concurrency. The computer network that connects IoT may further include techniques such as Fog and Edge computing that resolve much of the network issues. This paper provides solutions to these problems that occur in wireless and wired systems. The talk is about the ClientNet programming model and its application in IoT systems for orchestration, such as coordination, data communication, device identification and synchronization between the gateway servers and devices. These devices include sensors attached with appliances (e.g., home automations, supply chain systems, light and heavy machines, vehicles, power grids etc.) or buildings, bridges and computers running data processing applications. As described in earlier papers, the introduced ClientNet techniques prevent from big data transfers and streaming that occupy more resources (hardware and bandwidth) and time. The idea is motivated by Big Data problems that make it difficult to collect it from different resources through small devices and then redirecting it. The proposed programming model of ClientNet Distributed Cluster stores Big Data on the nearest server coordinated by the nearest coordinator. The gateways and the systems that run analytics programs communicate by running programs from other computers when it is essentially required. This makes it possible to let Big Data rarely move across a communication network and allow only the source code to move around the network. The given programming model greatly simplifies data communication overheads, communication patterns among devices, networks and servers.

A Numerically Coded File of Operative Procedures Derived from a Free Text Data Collection System : A Measure of the Accuracy

1976 ◽  
Vol 15 (01) ◽  
pp. 21-28 ◽  
Author(s):  
Carmen A. Scudiero ◽  
Ruth L. Wong
Keyword(s):  
Data Collection ◽  
Pap Smear ◽  
Operative Procedures ◽  
Free Text ◽  
Collection System ◽  
Process Data ◽  
Text Data ◽  
Data Collection System ◽  
History Of ◽  
Correlation System

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.

Design, Development and Practical Realization of a VLC Supportive Indoor Lighting System

2020 ◽  
pp. 87-97
Author(s):  
Sourish Chatterjee ◽  
Biswanath Roy
Keyword(s):  
Data Communication ◽  
Cost Effective ◽  
Ambient Light ◽  
Lighting System ◽  
Design Development ◽  
Led Driver ◽  
Communication Performance ◽  
Good Communication ◽  
Simulink Model ◽  
Light Interference

In an office space, an LED-based lighting system allows you to perform the function of a data transmitter. This article discusses the cost-effective design and development of a data-enabled LED driver that can transmit data along with its receiving part. In addition, this paper clearly outlines the application of the proposed VLC system in an office environment where ambient light interference is a severe issue of concern. The result shows satisfactory lighting characteristics in general for this area in terms of average horizontal illuminance and illuminance uniformity. At the same time, to evaluate real-time and static communication performance, Arduino interfaced MATLAB Simulink model is developed, which shows good communication performance in terms of BER (10–7) even in presence of ambient light noise with 6 dB signal to interference plus noise ratio. Our designed system is also flexible to work as a standalone lighting system, whenever data communication is not required.

scholarly journals Practical Cross-Layer Radio Frequency-Based Authentication Scheme for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4034
Author(s):  
Arie Haenel ◽  
Yoram Haddad ◽  
Maryline Laurent ◽  
Zonghua Zhang
Keyword(s):  
Internet Of Things ◽  
Radio Frequency ◽  
Real Life ◽  
Computation Time ◽  
Cost Effective ◽  
Battery Life ◽  
Cross Layer ◽  
Security Level ◽  
Cost Of Energy ◽  
Iot Devices

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.

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