scholarly journals Understanding of RF Cloud Interference Measurement and Modeling

2021 ◽  
Author(s):  
Kaveh Pahlavan
Keyword(s):  
Real Time ◽  
Spectrum Management ◽  
Wireless Devices ◽  
New Paradigm ◽  
Daily Lives ◽  
Wireless Technologies ◽  
Monitoring And Forecasting ◽  
Iot Devices ◽  
Rf Signal ◽  
The Universe

AbstractImportance of spectrum regulation and management was first revealed on May of 1985 after the release of unlicensed ISM bands resulting in emergence of Wi-Fi, Bluetooth and many other wireless technologies that has affected our daily lives by enabling the emergence of the smart world and IoT era. Today, the idea of a liberated spectrum is circulating around, which can potentially direct wireless networking industry into another revolution by enabling a new paradigm in intelligent spectrum regulation and management. The RF signal radiated from IoT devices as well as other wireless technologies create an RF cloud causing co- and cross-interference to each other. Lack of a science and technology for understanding, measurement, and modeling of the RF cloud interference in near real-time results in inefficient utilization of the precious spectrum, a unique natural resource shared among all wireless devices of the universe in frequency, time, and space. Near real time forecasting of the RF cloud interference is essential to pursue the path to the optimal utilization of spectrum and a liberated spectrum management. This paper presents a historical perspective on the evolution of spectrum regulation and management, explains the diversified meanings of interference for different sectors of the wireless industry, and presents a path for implementing a theoretical foundation for interference monitoring and forecasting to enable the emergence of a liberated spectrum industry and a new paradigm in spectrum management and regulations.

Game Theory for Cognitive Radio Networks

Game Theory ◽  
2017 ◽  
pp. 487-502
Author(s):  
Sungwook Kim
Keyword(s):  
Cognitive Radio ◽  
Cognitive Abilities ◽  
Cognitive Radios ◽  
Primary User ◽  
Spectrum Management ◽  
Cognitive Networks ◽  
Wireless Devices ◽  
New Paradigm ◽  
Secondary Users ◽  
Dynamic Spectrum Management

A cognitive radio is an intelligent radio that can be programmed and configured dynamically. Its transceiver is designed to use the best wireless channels in its vicinity. Such a radio automatically detects available channels in the wireless spectrum, then accordingly changes its transmission or reception parameters to allow more concurrent wireless communications in a given spectrum band at one location. This process is a form of dynamic spectrum management. In recent years, the development of intelligent, adaptive wireless devices called cognitive radios, together with the introduction of secondary spectrum licensing, has led to a new paradigm in communications: cognitive networks. Cognitive networks are wireless networks that consist of several types of users: often a primary user and secondary users. These cognitive users employ their cognitive abilities to communicate without harming the primary users. The study of cognitive networks is relatively new and many questions are yet to be answered. This chapter furthers the study.

scholarly journals Spectrum Management Schemes for Internet of Remote Things (IoRT) Devices in 5G Networks via GEO Satellite

2019 ◽  
Vol 11 (12) ◽  
pp. 257 ◽  
Author(s):  
Gbolahan Aiyetoro ◽  
Pius Owolawi
Keyword(s):  
Mobile Networks ◽  
Performance Metrics ◽  
Dynamic Scheduling ◽  
Spectrum Management ◽  
Satellite Networks ◽  
Network Access ◽  
5G Networks ◽  
New Paradigm ◽  
Air Interface ◽  
Iot Devices

The rapid growth of not just mobile devices but also Internet of Things (IoT) devices has introduced a new paradigm in mobile networks. This evolution and the continuous need to provide spectrum efficient, high data rates, low latency, and low energy consumption radio access networks have led to the emergence of fifth generation (5G) networks. Due to technical and economical limitations, the satellite air interface is expected to complement the 5G terrestrial air interface in the provision of 5G services including IoT communications. More importantly, it is on this premise that the 5G satellite air interface is expected to provide network access to IoT devices in rural and remote areas termed Internet of Remote Things (IoRT). While this remains an interesting solution, several radio resource management issues exist. One of them, spectrum management, in the 5G satellite as it affects IoRT communications, remains unclear. Hence, the aim of this paper is to investigate and recommend the spectrum management scheme that will be most suitable not only for Human-to-Human communications but also Machine-to-Machine communications in 5G satellite networks. In order to conduct this investigation, a new dynamic scheduling scheme that will be suitable for such a scenario is proposed in this paper. The investigation is conducted through simulations, using throughput, delay, spectral efficiency, and fairness index as the performance metrics.

Privacy Preserving in Smart Cities Using Various Computing Technologies

2022 ◽  
pp. 47-54
Author(s):  
T. N. Gayathri ◽  
M. Rajasekharababu
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Privacy Preserving ◽  
Synthetic Dataset ◽  
Smart Environment ◽  
Sensitive Data ◽  
Daily Lives ◽  
Simulation Results ◽  
Iot Devices ◽  
High Possibility

IoT has influenced our daily lives through various applications. The high possibility of sensing and publishing sensitive data in the smart environment leads to significant issues: (1) privacy-preserving and (2) real-time services. Privacy is a complex and a subjective notion as its understanding and perception differ among individuals, hence the observation that current studies lack addressing these challenges. This chapter proposes a new privacy-preserving method for IoT devices in the smart city by leveraging ontology, a data model, at the edge of the network. Based on the simulation results using Protege and Visual Studio on a synthetic dataset, the authors find that the solution provides privacy at real-time while addressing heterogeneity issue so that many IoT devices can afford it. Thus, the proposed solution can be widely used for smart cities.

scholarly journals NovaGenesis Applied to Information-Centric, Service-Defined, Trustable IoT/WSAN Control Plane and Spectrum Management

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3160 ◽  
Author(s):  
Antônio Alberti ◽  
Marília Bontempo ◽  
José dos Santos ◽  
Arismar Sodré ◽  
Rodrigo Righi
Keyword(s):  
Ieee 802.15.4 ◽  
Spectrum Management ◽  
Control Plane ◽  
Wireless Devices ◽  
Sensors And Actuators ◽  
Radio System ◽  
Dynamic Spectrum Management ◽  
Iot Devices ◽  
Real Scenario ◽  
First Time

We integrate, for the first time in the literature, the following ingredients to deal with emerging dynamic spectrum management (DSM) problem in heterogeneous wireless sensors and actuators networks (WSANs), Internet of things (IoT) and Wi-Fi: (i) named-based routing to provide provenance and location-independent access to control plane; (ii) temporary storage of control data for efficient and cohesive control dissemination, as well as asynchronous communication between software-controllers and devices; (iii) contract-based control to improve trust-ability of actions; (iv) service-defined configuration of wireless devices, approximating their configurations to real services needs. The work is implemented using NovaGenesis architecture and a proof-of-concept is evaluated in a real scenario, demonstrating our approach to automate radio frequency channel optimization in Wi-Fi and IEEE 802.15.4 networks in the 2.4 GHz bands. An integrated cognitive radio system provides the dual-mode best channel indications for novel DSM services in NovaGenesis. By reconfiguring Wi-Fi/IoT devices to best channels, the proposed solution more than doubles the network throughput, when compared to the case of mutual interference. Therefore, environments equipped with the proposal provide enhanced performance to their users.

Game Theory for Cognitive Radio Networks

2014 ◽  
pp. 172-187 ◽  
Keyword(s):  
Cognitive Radio ◽  
Cognitive Abilities ◽  
Cognitive Radios ◽  
Primary User ◽  
Spectrum Management ◽  
Cognitive Networks ◽  
Wireless Devices ◽  
New Paradigm ◽  
Secondary Users ◽  
Dynamic Spectrum Management

A cognitive radio is an intelligent radio that can be programmed and configured dynamically. Its transceiver is designed to use the best wireless channels in its vicinity. Such a radio automatically detects available channels in the wireless spectrum, then accordingly changes its transmission or reception parameters to allow more concurrent wireless communications in a given spectrum band at one location. This process is a form of dynamic spectrum management. In recent years, the development of intelligent, adaptive wireless devices called cognitive radios, together with the introduction of secondary spectrum licensing, has led to a new paradigm in communications: cognitive networks. Cognitive networks are wireless networks that consist of several types of users: often a primary user and secondary users. These cognitive users employ their cognitive abilities to communicate without harming the primary users. The study of cognitive networks is relatively new and many questions are yet to be answered. This chapter furthers the study.

scholarly journals An Adaptive Scheduler for Real-Time Operating Systems to Extend WSN Nodes Lifetime

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Roberto Rodriguez-Zurrunero ◽  
Ramiro Utrilla ◽  
Elena Romero ◽  
Alvaro Araujo
Keyword(s):  
Operating System ◽  
Real Time ◽  
Operating Systems ◽  
Research Area ◽  
Rechargeable Batteries ◽  
Wireless Sensor ◽  
Portable Devices ◽  
Wireless Devices ◽  
Changing Environments ◽  
Tasks Scheduling

Wireless Sensor Networks (WSNs) are a growing research area as a large of number portable devices are being developed. This fact makes operating systems (OS) useful to homogenize the development of these devices, to reduce design times, and to provide tools for developing complex applications. This work presents an operating system scheduler for resource-constraint wireless devices, which adapts the tasks scheduling in changing environments. The proposed adaptive scheduler allows dynamically delaying the execution of low priority tasks while maintaining real-time capabilities on high priority ones. Therefore, the scheduler is useful in nodes with rechargeable batteries, as it reduces its energy consumption when battery level is low, by delaying the least critical tasks. The adaptive scheduler has been implemented and tested in real nodes, and the results show that the nodes lifetime could be increased up to 70% in some scenarios at the expense of increasing latency of low priority tasks.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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