SpecSense: Crowdsensing for efficient querying of spectrum occupancy

Chakraborty, Ayon; Rahman, Md. Lutfor; Gupta, Himanshu; Das, Samir R.

doi:10.1109/infocom.2017.8057113

Cited by 69 publications

(40 citation statements)

References 23 publications

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“…On the other hand, correctly estimating radio frequency interference may degrade SINR when the bandwidth is varied from pulse to pulse [124]. Crowd sensing has elicited attention in recent years despite its issues, such as manifestation of abnormal data in crowd sensors [133], data incompleteness and inaccuracy [134], need for scalable radio-frequency spectrum monitoring, lowpower and low-cost sensors [135], fading, shadowing and noise uncertainty effects [127]. The suggested future directions for the crowd sensing method include finding ways to process the spectrum, streamlining contextual data, improving the spectrum decision quality and implementing the sensing application in various possible platforms other than Android [127].…”

Section: B Cognitive Radiomentioning

confidence: 99%

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

et al. 2020

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The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

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Section: B Cognitive Radiomentioning

confidence: 99%

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

et al. 2020

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“…We need an algorithm to interpolate the RSS field at the set of locations in . We start our investigation with two RSS interpolation methods, kriging interpolation and inverse distance weighting, that are known to perform well on the RSS field [6,11].…”

Section: Rss Interpolation Algorithmsmentioning

confidence: 99%

“…Kriging computes the interpolated RSS at ( , , , ) ∈ as, , = , , , ∀( , , , ) ∈ . The optimal weights, { , }, are obtained as described in [6]. For the RSS field to be intrinsically stationary, it is required that E[ , ] − E[ +ℎ, ] = 0, which is not the case for RSS, in general.…”

Section: Rss Interpolation Algorithmsmentioning

confidence: 99%

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Llocus

Sarkar

Baset

Singh

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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We present LLOCUS, a novel learning-based system that uses mobile crowdsourced RF sensing to estimate the location and power of unknown mobile transmitters in real time, while allowing unrestricted mobility of the crowdsourcing participants. We carefully identify and tackle several challenges in learning and localizing, based on RSS, in such a dynamic environment. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the power of a transmitter at an unknown location, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using three experiments in different indoor and outdoor environments. We find that LLOCUS reduces the localization error by 17-68% compared to several non-learning methods. CCS CONCEPTS • Networks → Location based services; Mobile ad hoc networks; Mobile and wireless security.

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“…In [27], a heuristic algorithm based on Particle Swarm Optimization is proposed to assign sensing tasks. In [28], a new infrastructure is created to integrate scalable spectrum monitoring combined with application support. The end-to-end system considering both spectrum sensing and connected applications can work seamlessly at scale.…”

Section: Related Workmentioning

confidence: 99%

Cost-Minimized Crowdsourced Spectrum Sensing

et al. 2019

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Cooperative spectrum sensing which enhances the sensing accuracy is an important research issue for cognitive radio networks, especially in complicated environment. Considering the extensive use of mobile intelligent terminals such as smart phones and tablets, crowdsourced spectrum sensing, which assigns spectrum sensing tasks to mobile terminals, can take advantage of mobile terminals' cooperation and obtain the accurate sensing results. In this paper, crowdsourced spectrum sensing is studied to propose assignment scheme of spectrum sensing tasks in large geographical areas. There may be several kinds of terrains affecting sensing in large-scale regions. Hence, according to the terrains, we divide a large region into several sub-regions and introduce sensing effect function to evaluate the sensing accuracy based on the number of sensing sub-regions. Furthermore, considering energy consumption is an important issue which mobile terminals focus on, we use the relative energy consumption to evaluate the cost of mobile terminals during spectrum sensing. Then, we formulate the crowdsourced sensing problem to minimize the total cost while keeping sensing effect not lower than the predefined threshold to maintain sensing accuracy. Since the problem is NP-hard, a heuristic algorithm is proposed to solve the crowdsourced sensing problem. At first, our algorithm arranges all sensing tasks in a priority queue based on their urgency. Then, sensing tasks are sequentially assigned to terminals with higher energy to prolong their survival time under makespan and energy constraints. To obtain the lowest system cost, we introduce remaining time and reassign sensing tasks from high-cost terminals to low-cost terminals based on the remaining time. Simulation results show our algorithm achieves higher performance than the other algorithms. INDEX TERMS Cost, crowdsourced, spectrum sensing.

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SpecSense: Crowdsensing for efficient querying of spectrum occupancy

Cited by 69 publications

References 23 publications

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

Llocus

Cost-Minimized Crowdsourced Spectrum Sensing

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