scholarly journals Big NB-IoT Data: Enhancing Portability of Handheld Narrow-Band Internet of Things Performance on Big Data Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Jie Xu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Network Optimization ◽  
Resource Sharing ◽  
Narrow Band ◽  
Research Work ◽  
Sensor Data ◽  
Battery Life ◽  
High Coverage ◽  
Iot Devices

The Narrow Band-Internet of Things (NB-IoT) is a wideband radio technology developed for the Internet of Things that enables smoother- and farther-reaching connectivity between IoT devices. In addition to traditional network optimization devices, Bluetooth and Wi-Fi, its virtue is low cost, and it consumes less energy and has high coverage and extended battery life. In order to secure the balance of task execution latency across NB-IoT devices, in this research work, we design a handheld NB-IoT wireless communication device. Furthermore, we provide realistic resource-sharing methods between multimedia and sensor data in NB-IoT wireless deployment by our accurate analytical methodology. In addition, we have considerably enhanced technology for gathering Big Data from several scattered sources, in combination with advancements in big data processing methodologies. The proposed handheld terminal has a wide variety of commercial applications in intelligent manufacturing and smart parking. Simulation outcomes illustrate the benefits of our handheld terminal, which provides practical solutions for network optimization, improving market share and penetration rate.

scholarly journals Big Data Integration with IoT to Achieve the Challenges

2019 ◽  
Vol 8 (S3) ◽  
pp. 45-49
Author(s):  
V. Bhagyasree ◽  
K. Rohitha ◽  
K. Kusuma ◽  
S. Kokila
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real World ◽  
Physical World ◽  
Sensor Data ◽  
The Internet ◽  
The Future ◽  
Iot Devices ◽  
The Right ◽  
The Internet Of Things

The Internet of Things anticipates the combination of physical gadgets to the Internet and their access to wireless sensor data which makes it useful to restrain the physical world. Big Data convergence has many aspects and new opportunities ahead of business ventures to get into a new market or enhance their operations in the current market. The existing techniques and technologies is probably safe to say that the best solution is to use big data tools to provide an analytical solution to the Internet of Things. Based on the current technology deployment and adoption trends, it is visioned that the Internet of Things is the technology of the future; while to-day’s real-world devices can provide best and valuable analytics, and people in the real world use many IOT devices. In spite of all the advertisements that companies offer in connection with the Internet of Things, you as a liable consumer, have the right to be suspicious about IoT advertisements. This paper focuses on the Internet of things concerning reality and what are the prospects for the future.

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.

scholarly journals Developing an On-Demand Cloud-Based Sensing-as-a-Service System for Internet of Things

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Mihui Kim ◽  
Mihir Asthana ◽  
Siddhartha Bhargava ◽  
Kartik Krishnan Iyyer ◽  
Rohan Tangadpalliwar ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Service System ◽  
Value Added ◽  
Computing System ◽  
Sensor Data ◽  
Dynamic Provisioning ◽  
On Demand ◽  
Cloud Computing System ◽  
Iot Devices

The increasing number of Internet of Things (IoT) devices with various sensors has resulted in a focus on Cloud-based sensing-as-a-service (CSaaS) as a new value-added service, for example, providing temperature-sensing data via a cloud computing system. However, the industry encounters various challenges in the dynamic provisioning of on-demand CSaaS on diverse sensor networks. We require a system that will provide users with standardized access to various sensor networks and a level of abstraction that hides the underlying complexity. In this study, we aim to develop a cloud-based solution to address the challenges mentioned earlier. Our solution, SenseCloud, includes asensor virtualizationmechanism that interfaces with diverse sensor networks, amultitenancymechanism that grants multiple users access to virtualized sensor networks while sharing the same underlying infrastructure, and adynamic provisioningmechanism to allow the users to leverage the vast pool of resources on demand and on a pay-per-use basis. We implement a prototype of SenseCloud by using real sensors and verify the feasibility of our system and its performance. SenseCloud bridges the gap between sensor providers and sensor data consumers who wish to utilize sensor data.

Communication Trends in Internet of Things

2018 ◽  
pp. 284-305 ◽  
Author(s):  
Bharathi N. Gopalsamy
Keyword(s):  
Internet Of Things ◽  
Short Range ◽  
Near Field ◽  
Communication Technologies ◽  
Good Choice ◽  
Battery Life ◽  
Power Requirement ◽  
Storage Security ◽  
Central Hypothesis ◽  
Iot Devices

The central hypothesis of Internet of Things is the term “connectivity”. The IoT devices are connected to the Internet through a wide variety of communication technologies. This chapter explains the various technologies involved in IoT connectivity. The diversity in communication raises the query of which one to choose for the proposed application. The key objective of the application needs to be defined very clearly. The application features such as the power requirement, data size, storage, security and battery life highly influence the decision of selecting one or more communication technology. Near Field Communication is a good choice for short-range communication, whereas Wi-Fi can be opted for a larger range of coverage. Though Bluetooth is required for higher data rate, it is power hungry, but ZigBee is suitable for low power devices. There involves always the tradeoff between the technologies and the requirements. This chapter emphasizes that the goal of the application required to be more precise to decide the winner of the IoT connectivity technology that suits for it.

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.

scholarly journals Recent Advancements in Intrusion Detection Systems for the Internet of Things

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zeeshan Ali Khan ◽  
Peter Herrmann
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Research Work ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Systems ◽  
Iot Applications ◽  
Iot Devices

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

How the Internet of Things can help knowledge management: a case study from the automotive domain

2017 ◽  
Vol 21 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lorna Uden ◽  
Wu He
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Internet Of Things ◽  
Current Knowledge ◽  
Time Data ◽  
Content Type ◽  
Useful Knowledge ◽  
Iot Devices ◽  
The Internet Of Things

Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 495-500
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Private Information ◽  
Large Scale ◽  
Layer System ◽  
Collection Data ◽  
Efficient Data ◽  
Multiple Environments ◽  
Iot Devices

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

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