scholarly journals INTERNET OF THINGS BASED SMART AGRICULTURE SYSTEM USING PREDICTIVE ANALYTICS

2017 ◽  
Vol 10 (13) ◽  
pp. 148 ◽  
Author(s):  
Suhas M Patil ◽  
Sakkaravarthi R
Keyword(s):  
Internet Of Things ◽  
Data Transfer ◽  
Predictive Analytics ◽  
Weather Prediction ◽  
Soil Classification ◽  
Farming System ◽  
Quality Data ◽  
Growth Monitoring ◽  
Quality Of Data ◽  
Iot Devices

Due to the use of internet of things (IoT) devices, communication between different things is effective. The application of IoT in agriculture industryplays a key role to make functionalities easy. Using the concept of IoT and wireless sensor network (WSN), smart farming system has been developedin many areas of the world. Precision farming is one of the branches comes forward in this aspect. Many researchers have developed monitoring andautomation system for different functionalities of farming. Using WSN, data acquisition and transmission between IoT devices deployed in farms will be easy. In proposed technique, Kalman filter (KF) is used with prediction analysis to acquire quality data without any noise and to transmit this data for cluster-based WSNs. Due to the use of this approach, the quality of data used for analysis is improved as well as data transfer overhead is minimized in WSN application. Decision tree is used for decision making using prediction analytics for crop yield prediction, crop classification, soil classification, weather prediction, and crop disease prediction. IoT components, such as and cube (IOT Gateway) and Mobius (IOT Service platform), are integrated in proposed system to provide smart solution for crop growth monitoring to users. 

Big Data and the Internet of Things

2021 ◽  
pp. 1321-1342
Author(s):  
Zablon Pingo ◽  
Bhuva Narayan
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Predictive Analytics ◽  
Negative Impact ◽  
Consumer Products ◽  
Informational Privacy ◽  
Protection Mechanisms ◽  
Iot Devices ◽  
Aid Decision ◽  
Privacy Issues

The privacy construct is an important aspect of internet of things (IoT) technologies as it is projected that over 20 billion IoT devices will be in use by 2022. Among other things, IoT produces big data and many industries are leveraging this data for predictive analytics to aid decision making in health, education, business, and other areas. Despite benefits in some areas, privacy issues have persisted in relation to the use of the data produced by many consumer products. The practices surrounding IoT and Big Data by service providers and third parties are associated with a negative impact to individuals. To protect consumers' privacy, a wide range of approaches to informational privacy protections exist. However, individuals are increasingly required to actively respond to control and manage their informational privacy rather than rely on any protection mechanisms. This chapter highlights privacy issues across consumers' use of IoT and identifies existing responses to enhance privacy awareness as a way of enabling IoT users to protect their privacy.

Blockchain-Enabled Decentralized Reliable Smart Industrial Internet of Things (BCIIoT)

2021 ◽  
pp. 192-204
Author(s):  
Chandramohan Dhasarathan ◽  
Shanmugam M. ◽  
Shailesh Pancham Khapre ◽  
Alok Kumar Shukla ◽  
Achyut Shankar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Wireless Communication ◽  
Information Flow ◽  
Data Transfer ◽  
Base Station ◽  
Transmission Power ◽  
Wireless Devices ◽  
Industrial Internet ◽  
Iot Devices

The development of wireless communication in the information technological era, collecting data, and transfering it from unmanned systems or devices could be monitored by any application while it is online. Direct and aliveness of countless wireless devices in a cluster of the medium could legitimate unwanted users to interrupt easily in an information flow. It would lead to data loss and security breach. Many traditional algorithms are effectively contributed to the support of cryptography-based encryption to ensure the user's data security. IoT devices with limited transmission power constraints have to communicate with the base station, and the data collected from the zones would need optimal transmission power. There is a need for a machine learning-based algorithm or optimization algorithm to maximize data transfer in a secure and safe transmission.

Big Data and the Internet of Things

2019 ◽  
pp. 55-76
Author(s):  
Zablon Pingo ◽  
Bhuva Narayan
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Predictive Analytics ◽  
Negative Impact ◽  
Consumer Products ◽  
Informational Privacy ◽  
Protection Mechanisms ◽  
Iot Devices ◽  
Aid Decision ◽  
Privacy Issues

The privacy construct is an important aspect of internet of things (IoT) technologies as it is projected that over 20 billion IoT devices will be in use by 2022. Among other things, IoT produces big data and many industries are leveraging this data for predictive analytics to aid decision making in health, education, business, and other areas. Despite benefits in some areas, privacy issues have persisted in relation to the use of the data produced by many consumer products. The practices surrounding IoT and Big Data by service providers and third parties are associated with a negative impact to individuals. To protect consumers' privacy, a wide range of approaches to informational privacy protections exist. However, individuals are increasingly required to actively respond to control and manage their informational privacy rather than rely on any protection mechanisms. This chapter highlights privacy issues across consumers' use of IoT and identifies existing responses to enhance privacy awareness as a way of enabling IoT users to protect their privacy.

scholarly journals E-HIP: An Energy-Efficient OpenHIP-Based Security in Internet of Things Networks

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4921 ◽  
Author(s):  
Peter Kaňuch ◽  
Dominik Macko
Keyword(s):  
Internet Of Things ◽  
Data Transfer ◽  
Security Protocols ◽  
Security Threats ◽  
Optimized Protocol ◽  
Host Identity ◽  
Iot Devices ◽  
High Level ◽  
Application Specific ◽  
The Internet Of Things

The rapidly growing segment of the Internet of Things (IoT) makes the security threats more prominent than ever. The research around communication security and cybersecurity in such networks is still a challenge, mainly due to the typically limited energy and computation resources of IoT devices. The strong security mechanisms require significant power and thus the energy wastage must be minimized. Optimized application-specific security protocols are commonly used to make the data transfer more efficient, while still offering a high level of security. The supported security features, such as confidentiality, integrity or authenticity, should not be affected by the optimization. Our work is focused on optimizing one of the existing security protocols for the use in the IoT area, namely the Host Identity Protocol (HIP). Based on the analysis of related works, we have identified multiple possibilities for optimization and combined some of them into the proposed E-HIP optimized protocol. For verification purpose, it has been implemented as a modification of the open-source OpenHIP library and applied on a communication between real hardware devices. The secured communication worked correctly. The resulting effect of the proposed optimization has been evaluated experimentally and it represents an increase in energy efficiency by about 20%. Compared to other HIP optimizations, the achieved results are similar; however, the proposed optimizations are unique and can be further combined with some of the existing ones to achieve even higher efficiency.

scholarly journals Efficient distributed network covert channels for Internet of things environments†

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Krzysztof Cabaj ◽  
Piotr Żórawski ◽  
Piotr Nowakowski ◽  
Maciej Purski ◽  
Wojciech Mazurczyk
Keyword(s):  
Internet Of Things ◽  
Communication Networks ◽  
Data Hiding ◽  
Network Traffic ◽  
Data Transfer ◽  
Research Area ◽  
Distributed Network ◽  
Covert Channels ◽  
Secret Communication ◽  
Iot Devices

Abstract Each day more and more Internet of Things (IoT) devices are being connected to the Internet. In general, their applications are diverse but from the security perspective, it is evident that they are increasingly targeted by cybercriminals and used for nefarious purposes. Network covert channels form a subgroup of the information-hiding research area where secrets are sent over communication networks embedded within the network traffic. Such techniques can be used, among others, by malware developers to enable confidential data exfiltration or stealth communications. Recently, distributed network covert channels have raised the attention of security professionals as they allow the cloaking of secret transmission by spreading the covert bits among many different types of data-hiding techniques. However, although there are many works dealing with IoT security, little effort so far has been devoted in determining how effective the covert channels threat can be in the IoT henvironments. That is why, in this article, we present an extensive analysis on how distributed network covert channels that utilize network traffic from IoT devices can be used to perform efficient secret communication. More importantly, we do not focus on developing novel data-hiding techniques but, instead, considering the nature of IoT traffic, we investigate how to combine existing covert channels so the resulting data transfer is less visible. Moreover, as another contribution of our work, we prepare and share with the community the network traffic dataset that can be used to develop effective countermeasures against such threats.

scholarly journals Efficient and Secure Data Transfer in IoT

2019 ◽  
Vol 8 (4) ◽  
pp. 3712-3715
Keyword(s):  
Health Care ◽  
Internet Of Things ◽  
Smart City ◽  
Data Transfer ◽  
Limited Resources ◽  
The Internet ◽  
Safety Aspects ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Security Of Iot

Nowadays, the Internet of Things (IoT) has been used widely in our daily day to day life, starting from health care devices, hospital management appliances to a smart city. Most of the IoT devices have limited resources and limited storing capability. All the sensed information must have to be transmitted and to store in the cloud. To make a decision and for making analysis all the data stored in the cloud has to be retrieved. Making certain the credibility and security of the sensed information are much necessary and very important for the use of IoT devices. We tend to examine the proposed technique to be much secure than the existing one. In IoT, if the security is not ensured, then it may result in a variety of unsought issues. This survey resembles the overall safety aspects of IoT and debates the overall issues in the security of IoT.

scholarly journals ‘Do you trust those data?’—a mixed-methods study assessing the quality of data reported by community health workers in Kenya and Malawi

2020 ◽  
Vol 35 (3) ◽  
pp. 334-345 ◽  
Author(s):  
Regeru Njoroge Regeru ◽  
Kingsley Chikaphupha ◽  
Meghan Bruce Kumar ◽  
Lilian Otiso ◽  
Miriam Taegtmeyer
Keyword(s):  
Mixed Methods ◽  
Data Quality ◽  
Community Health ◽  
Health Workers ◽  
Calculated Data ◽  
Mixed Methods Study ◽  
Quality Data ◽  
Growth Monitoring ◽  
Health Coverage ◽  
Quality Of Data

Abstract High-quality data are essential to monitor and evaluate community health worker (CHW) programmes in low- and middle-income countries striving towards universal health coverage. This mixed-methods study was conducted in two purposively selected districts in Kenya (where volunteers collect data) and two in Malawi (where health surveillance assistants are a paid cadre). We calculated data verification ratios to quantify reporting consistency for selected health indicators over 3 months across 339 registers and 72 summary reports. These indicators are related to antenatal care, skilled delivery, immunization, growth monitoring and nutrition in Kenya; new cases, danger signs, drug stock-outs and under-five mortality in Malawi. We used qualitative methods to explore perceptions of data quality with 52 CHWs in Kenya, 83 CHWs in Malawi and 36 key informants. We analysed these data using a framework approach assisted by NVivo11. We found that only 15% of data were reported consistently between CHWs and their supervisors in both contexts. We found remarkable similarities in our qualitative data in Kenya and Malawi. Barriers to data quality mirrored those previously reported elsewhere including unavailability of data collection and reporting tools; inadequate training and supervision; lack of quality control mechanisms; and inadequate register completion. In addition, we found that CHWs experienced tensions at the interface between the formal health system and the communities they served, mediated by the social and cultural expectations of their role. These issues affected data quality in both contexts with reports of difficulties in negotiating gender norms leading to skipping sensitive questions when completing registers; fabrication of data; lack of trust in the data; and limited use of data for decision-making. While routine systems need strengthening, these more nuanced issues also need addressing. This is backed up by our finding of the high value placed on supportive supervision as an enabler of data quality.

scholarly journals Method of developing machine learning architecture for IoT­devices based on serverless architecture

Connectivity ◽  
2020 ◽  
Vol 146 (4) ◽  
Author(s):  
G. O. Grynkevych ◽  
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Data Transfer ◽  
Predictive Analytics ◽  
Distributed Applications ◽  
Learning Models ◽  
Data Source ◽  
Data Ingestion ◽  
Iot Devices ◽  
Hardware Platforms

With the advent of IoT and microservice architectures, a multitude of intelligent distributed applications have emerged in which IoT devices collect, transform, and analyze data in large volumes and at high speed. A large number of these programs require robust, predictive analytics in real time, which require threading workflows closer to the data source, as well as dynamic resource management decisions. Moreover, predictive analytics requires the developer to create robust deep learning models, which, in turn, requires them to develop functions, find and configure parameters, and select machine learning models, which takes not only time, but also requires high experience level. The proliferation of machine learning libraries and frameworks, data ingestion tools, streaming and batch processing engines, rendering techniques, and the myriad of available hardware platforms further exacerbate these challenges. To overcome these complex challenges faced by developers of intelligent IoT applications, this article proposes a method for deploying machine learning architecture for IoT devices based on a serverless architecture called MLAbosa. The MLAbosa method can deploy, plan and dynamically manage data transfer tools, streaming software, batch analytics tools, and visualization tools across the cloud spectrum. This article describes the architecture of the MLAbosa method, highlighting the problems it solves.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

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