Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions

Khan, Athar Ali; Rehmani, Mubashir Husain; Rachedi, Abderrezak

doi:10.1109/mwc.2017.1600404

Cited by 410 publications

(222 citation statements)

References 11 publications

Supporting

Mentioning

221

Contrasting

Unclassified

Order By: Relevance

“…A survey about context awareness in IoT, while addressing various techniques, models, methods, functionalities, middleware solutions and applications related to it is presented in [25]. The architectures and frameworks of cognitive radio-based IoT systems are surveyed in [26], while highlighting open issues, research challenges According to Figure 6b, the network lifetime is defined as the time spent by device nodes in the IoT network until the moment when only 1% of the nodes remain alive. The saturation time is like the network lifetime, except its measurement begins from the point when the first node dies.…”

Section: Related Literature Reviewsmentioning

confidence: 99%

“…A survey about context awareness in IoT, while addressing various techniques, models, methods, functionalities, middleware solutions and applications related to it is presented in [25]. The architectures and frameworks of cognitive radio-based IoT systems are surveyed in [26], while highlighting open issues, research challenges and applications related to it. The research efforts in IoT-based smart environments have been surveyed on the basis of network types, local area wireless standards, technologies and human life [27].…”

Section: Related Literature Reviewsmentioning

confidence: 99%

See 1 more Smart Citation

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

Kumar

Kaiwartya

et al. 2017

Energies

View full text Add to dashboard Cite

Abstract:Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.

show abstract

Section: Related Literature Reviewsmentioning

confidence: 99%

“…A survey about context awareness in IoT, while addressing various techniques, models, methods, functionalities, middleware solutions and applications related to it is presented in [25]. The architectures and frameworks of cognitive radio-based IoT systems are surveyed in [26], while highlighting open issues, research challenges and applications related to it. The research efforts in IoT-based smart environments have been surveyed on the basis of network types, local area wireless standards, technologies and human life [27].…”

Section: Related Literature Reviewsmentioning

confidence: 99%

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

Kumar

Kaiwartya

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…Applications, such as cloudified mobile networks [1], virtual infrastructure [2], and industrial communication, also exploit heterogeneous architecture to support ubiquitous, flexible, and reliable connectivity [3]. Such proliferation of wireless base stations and data offloading strategies are to be expected in the near future, presumably yielding the two-tier approach to network design [4]. More specifically, a tier of IoT oriented femtocell base stations coexists with a tier of pre-existing macrocell base stations (MBSs) and shares the licensed spectrum.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Tian

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.

show abstract

“…In addition, spectrum scarcity is also a big issue. IoT objects are expected to grow into large numbers, and it will be very difficult to allocate spectrum bands to all these objects [4]. To address the above problems, recent trends in research are shifting to cognitive radio networks (CRNs), viewing them as a potential solution for IoT applications.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

Jinyi

Yang

Yoo

2018

Mobile Information Systems

View full text Add to dashboard Cite

e Internet of ings (IoT) is the interconnection of different objects through the internet using different communication technologies. e objects are equipped with sensors and communications modules. e cognitive radio network is a key technique for the IoT and can effectively address spectrum-related issues for IoT applications. In our paper, a novel method for IoT sensor networks is proposed to obtain the optimal positions of secondary information gathering stations (SIGSs) and to select the optimal operating channel. Our objective is to maximize secondary system capacity while protecting the primary system. In addition, we propose an appearance probability matrix for secondary IoT devices (SIDs) to maximize the supportable number of SIDs that can be installed in a car, in wearable devices, or for other monitoring devices, based on optimal deployment and probability. We derive fitness functions based on the above objectives and also consider signal to interference-plus-noise ratio (SINR) and position constraints. e particle swarm optimization (PSO) technique is used to find the best position and operating channel for the SIGSs. In a simulation study, the performance of the proposed method is evaluated and compared with a random resources allocation algorithm (parts of this paper were presented at the ICTC2017 conference (Wen et al., 2017)).

show abstract

Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions

Cited by 410 publications

References 11 publications

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

Contact Info

Product

Resources

About