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020			_a9789811696909
024	7		_a10.1007/978-981-16-9690-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.59 _b2022 EB
100	1		_aTang, Guoming _eautor _0(orcid)0000-0001-9801-1055 _1https://orcid.org/0000-0001-9801-1055 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685221
245	1	0	_aGreenEdge _bNew Perspectives to Energy Management and Supply in Mobile Edge Computing _cby Guoming Tang, Deke Guo, Kui Wu
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (X, 114 páginas) _b1 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_a1 Introduction -- 2 Investigating Low-Battery Anxiety of Mobile Users -- 3 User Energy and LBA Aware Mobile Video Streaming -- 4 Optimal Backup Power Allocation for 5G Base Stations -- 5 Reusing Backup Batteries for Power Demand Reshaping in 5G -- 6 Software-Defined Power Supply to Geo-Distributed Edge DCs -- 7 Conclusions and Future Work.
520			_aThe 5G technology has been commercialized worldwide and is expected to provide superior performance with enhanced mobile broadband, ultra-low latency transmission, and massive IoT connections. Meanwhile, the edge computing paradigm gets popular to provide distributed computing and storage resources in proximity to the users. As edge services and applications prosper, 5G and edge computing will be tightly coupled and continuously promote each other forward. Embracing this trend, however, mobile users, infrastructure providers, and service providers are all faced with the energy dilemma. On the user side, battery-powered mobile devices are much constrained by battery life, whereas mobile platforms and apps nowadays are usually power-hungry. At the infrastructure and service provider side, the energy cost of edge facilities accounts for a large proportion of operating expenses and has become a huge burden. This book provides a collection of most recent attempts to tackle the energy issues in mobile edge computing from new and promising perspectives. For example, the book investigates the pervasive low-battery anxiety among modern mobile users and quantifies the anxiety degree and likely behavior concerning the battery status. Based on the quantified model, a low-power video streaming solution is developed accordingly to save mobile devices' energy and alleviate users' low-battery anxiety. In addition to energy management for mobile users, the book also looks into potential opportunities to energy cost saving and carbon emission reduction at edge facilities, particularly the 5G base stations and geo-distributed edge datacenters.
988			_aSpringer_Computer_2022
650		7	_2embne _9476230 _aInformática móvil
650		7	_2embne _9156434 _aProceso distribuido (Informática)
700	1		_aGuo, Deke _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683346
700	1		_aWu, Kui _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685222
776	0	8	_iPrinted edition: _z9789811696893
776	0	8	_iPrinted edition: _z9789811696916
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-9690-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI