| dc.identifier.uri | http://hdl.handle.net/11401/77262 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Thesis | |
| dcterms.abstract | Given the exponential increase in mobile data traffic, there is a growing fear of an im- pending spectrum crunch. Shared use of the so-called ‘Whitespace’ spectrum offers a solution. Whitespace spectra are those that are already licensed for specific use but is understood to be ill-utilized over space and time. Examples include spectra used by ter- restrial TV broadcast or various radars. Shared use of such Whitespace spectra requires that incumbent licensed devices (primary) would have priority. One straightforward mechanism to detect the existence of primary transmissions is spectrum sensing. We are envisioning a spectrum sensing model where mobile devices have built-in spectrum sensing capabilities and upload such data on a cloud-based server that in turn builds a spatial map of spectrum occupancy. This enables spatial granular data collection via crowd-sourcing, for example. However, such mobile device-based spectrum sensing is challenging as the mobile devices are generally resource constrained. On the other hand, spectrum sensing is energy and computation intensive. In this thesis, we perform a measurement study to understand the general feasibility of the mobile sensing approach. For the experiments, we use several low-power software radio platforms that are powered by USB so that they can be interfaced to a mobile phone/tablet class device with an appropriate USB support. We develop appropriate software/hardware testbeds to carry out latency and energy measurements for mobile-based spectrum sensing on such platforms for a suite of well-known sensing algorithms operating in the TV white space. We describe the setup, discuss insights gained from these measurements and different possible optimizations such as the use of pipelining or use of GPU. Finally, we demonstrate the end-to-end operation by showcasing a system comprising of i) a number of distributed mobile spectrum sensors and ii) an indoor small cell comprising of an access point and client device (secondaries) operating in TV band iii) and a cloud-based spectrum server that builds spectrum map based on collected sensing data and instruct/allow secondaries to operate in multiple non-interfering Whitespace channels based on availability at the location. | |
| dcterms.abstract | Given the exponential increase in mobile data traffic, there is a growing fear of an im- pending spectrum crunch. Shared use of the so-called ‘Whitespace’ spectrum offers a solution. Whitespace spectra are those that are already licensed for specific use but is understood to be ill-utilized over space and time. Examples include spectra used by ter- restrial TV broadcast or various radars. Shared use of such Whitespace spectra requires that incumbent licensed devices (primary) would have priority. One straightforward mechanism to detect the existence of primary transmissions is spectrum sensing. We are envisioning a spectrum sensing model where mobile devices have built-in spectrum sensing capabilities and upload such data on a cloud-based server that in turn builds a spatial map of spectrum occupancy. This enables spatial granular data collection via crowd-sourcing, for example. However, such mobile device-based spectrum sensing is challenging as the mobile devices are generally resource constrained. On the other hand, spectrum sensing is energy and computation intensive. In this thesis, we perform a measurement study to understand the general feasibility of the mobile sensing approach. For the experiments, we use several low-power software radio platforms that are powered by USB so that they can be interfaced to a mobile phone/tablet class device with an appropriate USB support. We develop appropriate software/hardware testbeds to carry out latency and energy measurements for mobile-based spectrum sensing on such platforms for a suite of well-known sensing algorithms operating in the TV white space. We describe the setup, discuss insights gained from these measurements and different possible optimizations such as the use of pipelining or use of GPU. Finally, we demonstrate the end-to-end operation by showcasing a system comprising of i) a number of distributed mobile spectrum sensors and ii) an indoor small cell comprising of an access point and client device (secondaries) operating in TV band iii) and a cloud-based spectrum server that builds spectrum map based on collected sensing data and instruct/allow secondaries to operate in multiple non-interfering Whitespace channels based on availability at the location. | |
| dcterms.available | 2017-09-20T16:52:18Z | |
| dcterms.contributor | Das, Samir R | en_US |
| dcterms.contributor | Gupta, Himanshu | en_US |
| dcterms.contributor | Balasubramanium, Aruna. | en_US |
| dcterms.creator | Gupta, Udit Kumar | |
| dcterms.dateAccepted | 2017-09-20T16:52:18Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:18Z | |
| dcterms.description | Department of Computer Science. | en_US |
| dcterms.extent | 84 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/77262 | |
| dcterms.issued | 2015-05-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:18Z (GMT). No. of bitstreams: 1
Gupta_grad.sunysb_0771M_12502.pdf: 12497792 bytes, checksum: 0c7d2fd081ffd72f4478969a51203400 (MD5)
Previous issue date: 2015 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Computer science | |
| dcterms.subject | Energy, Latency, Mobile Devices, Performance Evaluation, Spectrum Sensing, Whitespace | |
| dcterms.title | A Feasibility Study of Distributed Spectrum Sensing using Mobile Devices | |
| dcterms.type | Thesis | |
