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The Journal of Supercomputing
Published in: The Journal of Supercomputing 2/2024

29-07-2023

Mobile crowd computing: potential, architecture, requirements, challenges, and applications

Authors: Pijush Kanti Dutta Pramanik, Saurabh Pal, Prasenjit Choudhury

Published in: The Journal of Supercomputing | Issue 2/2024

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Abstract

Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd computing (MCC) is the computing paradigm that uses public-owned SMDs to garner affordable high-performance computing (HPC). Though several empirical works have established the feasibility of mobile-based computing for various applications, there is a lack of comprehensive coverage of MCC. This paper aims to explore the fundamentals and other nitty–gritty of the idea of MCC in a comprehensive manner. Starting with an explicit definition of MCC, the enabling backdrops and the detailed architectural layouts of different models of MCC are presented, along with categorising different types of MCC based on infrastructure and application demands. MCC is compared extensively with other HPC systems (e.g. desktop grid, cloud, clusters and supercomputers) and similar mobile computing systems (e.g. mobile grid, mobile cloud, ad hoc mobile cloud, and mobile crowdsourcing). MCC being a complex system, various design requirements and considerations are extensively analysed. The potential benefits of MCC are meticulously mentioned, with special discussions on the ubiquity and sustainability of MCC. The issues and challenges of MCC are critically presented in light of further research scopes. Several real-world applications of MCC are identified and propositioned. Finally, to carry forward the accomplishment of the MCC vision, the future prospects are briefly elucidated.
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Footnotes
Literature
1.
Swedin EG, Ferro DL (2007) Computers: the life story of a technology, Baltimore. Johns Hopkins University Press, Maryland
2.
Foster I, Kesselman C (eds) (1998) The grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers, San Francisco
3.
Brynjolfsson E, Hofmann P, Jordan J (2010) Cloud computing and electricity: beyond the utility model. Commun ACM 53(5):32–34
4.
Korri T (2009) “Cloud computing: utility computing over the Internet,” In: TKK T-110.5190 Seminar on Internetworking
5.
Buyya R (2009) “Market-oriented cloud computing: vision, hype, and reality of delivering computing as the 5th utility,” In: 4th ChinaGrid Annual Conference, Yangtai, China
6.
7.
8.
Pramanik PKD, Pal S, Brahmachari A, Choudhury P (2018) Processing IoT data: from cloud to fog. It’s time to be down-to-earth. In: Karthikeyan P, Thangavel M (eds) Applications of security mobile analytic and cloud (SMAC) technologies for effective information processing and management. IGI Global, pp 124–148
9.
Black M, Edgar W (2009) “Exploring mobile devices as grid resources: using an x86 virtual machine to run BOINC on an iPhone,” In: 10th IEEE/ACM International Conference on Grid Computing, Melbourne, Australia
10.
Farooq U, Khalil W (2006) “A generic mobility model for resource prediction in mobile grids,” In: International Symposium on Collaborative Technologies and Systems, Las Vegas, USA
11.
Viswanathan H, Lee EK, Rodero I, Pompili D (2015) Uncertainty-aware autonomic resource provisioning for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(8):2363–2372
12.
Büsching F, Schildt S, Wolf L (2012) “DroidCluster: towards smartphone cluster computing - the streets are paved with potential computer clusters,” In: 32nd International Conference on Distributed Computing Systems Workshops, Macau, China
13.
Datla D, Chen X, Tsou T, Raghunandan S, Hasan SM, Reed J, Fette B, Dietrich CB, Kim JH, Bose T (2012) “Wireless distributed computing: a survey of research challenges.” IEEE Commun Magaz 50(1):144–152
14.
Shila DM, Shen W, Cheng Y, Tian X, Shen XS (2017) AMCloud: toward a secure autonomic mobile ad hoc cloud computing system. IEEE Wirel Commun 24(2):74–81
15.
Nishio T, Shinkuma R, Takahashi T, Mandayam NB (2013) “Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud,” In: First international workshop on Mobile cloud computing & networking, Bangalore, India
16.
Yaqoob I, Ahmed E, Gani A, Mokhtar S, Imran M, Guizani S (2016) Mobile ad hoc cloud: a survey. Wirel Commun Mob Comput 16(16):2572–2589
17.
Habak K, Ammar M, Harras KA, Zegura E (2015) “FemtoClouds: leveraging mobile devices to provide cloud service at the edge,” In: 8th International Conference on Cloud Computing, New York, USA
18.
Hirsch M, Mateos C, Zunino A (2018) Augmenting computing capabilities at the edge by jointly exploiting mobile devices: a survey. Futur Gener Comput Syst 88(November):644–662
19.
Hirsch M, Mateos C, Zunino A, Majchrzak TA, Grønli TM, Kaindl H (2021) “A simulation-based performance evaluation of heuristics for dew computing,” In: 54th Hawaii International Conference on System Sciences, Maui, Hawaii
20.
Hirsch M, Mateos C, Zunino A, Majchrzak TA, Grønli T-M, Kaindl H (2021) A task execution scheme for dew computing with state-of-the-art smartphones. Electronics 10(16):2006
21.
Loke SW, Napier K, Alali A, Fernando N, Rahayu W (2015) Mobile computations with surrounding devices: proximity sensing and multi layered work stealing. ACM Trans Embedded Comput Syst 14(2):1–25
22.
N. Fernando, S. W. Loke and W. Rahayu, “Honeybee: a programming framework for mobile crowd computing,” in Mobile and Ubiquitous Systems: Computing, Networking, and Services (MobiQuitous 2012). Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 120, K. Zheng, M. Li and H. Jiang, Eds., Berlin, Heidelberg, Springer, 2013, pp. 224–236.
23.
Fernando N, Loke SW, Rahayu W (2019) Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds. IEEE Transactions on Cloud Computing 7(2):329–343
24.
Zavodovski A, Corneo L, Johnsson A, Mohan N, Bayhan S, Zhou P, Wong W, Kangasharju J (2021) “Decentralizing computation with edge computing: potential and challenges,” In: Interdisciplinary Workshop on (de) Centralization in the Internet (IWCI'21), Germany
25.
Tanenbaum AS, Steen MV (2007) Distributed systems: principles and paradigms, 2nd edn. Pearson, New Jersy
26.
Quinn MJ (1994) Parallel computing: theory and practice. McGraw-Hill Education, India
27.
Baker M, Buyya R (1999) “Cluster computing at a glance”, in High Performance Cluster Computing - Architectures and Systems. USA, Prentice Hall PTR, New Jersey, pp 3–47
28.
Baker M, Buyya R (1999) Cluster computing: the commodity supercomputer. Journal of Software: Practice and Experience 29(6):551–576
29.
Mengistu TM, Che D (2020) Survey and taxonomy of volunteer computing. ACM Comput Surv 52(3):1–35
30.
Durrani MN, Shamsi JA (2014) Volunteer computing: requirements, challenges, and solutions. J Netw Comput Appl 39:369–380
31.
32.
Korpela EJ (2012) SETI@home, BOINC, and volunteer distributed computing. Annu Rev Earth Planet Sci 40:69–87
33.
Milojicic DS, Kalogeraki V, Lukose R, Nagaraja K, Pruyne J, Richard B, Rollins S, Xu Z (2003) “Peer-to-peer computing,” HP Laboratories Palo Alto
34.
Barkai D (2000) “An introduction to peer-to-peer computing,” In: Intel Developer Update Magazine, pp. 1–7
35.
Xu D, Li Y, Chen X, Li J, Hui P, Chen S, Crowcroft J (2018) A survey of opportunistic offloading. IEEE Communications Surveys & Tutorials 20(3):2198–2236
36.
Conti M, Kumar M (January 2010) Opportunities in opportunistic computing. Computer 43(1):42–50
37.
Kristensen MD (2010) “Scavenger: Transparent development of efficient cyber foraging applications,” In: IEEE International Conference on Pervasive Computing and Communications (PerCom), Mannheim, Germany
38.
Ahangar MRH, Taba MRE, Ghafouri A (2017) On a novel grid computing-based distributed brute-force attack scheme (GCDBF) by exploiting botnets. International Journal of Computer Network and Information Security 6:21–29
39.
Strickland JW, Freeh VW, Ma X, Vazhkudai SS (2005) “Governor: autonomic throttling for aggressive idle resource scavenging,” In: 2nd International Conference on Autonomic Computing (ICAC'05), Seattle, USA
40.
Rosales E, Sotelo G, Vega A, Díaz CO, Gómez CE, Castro H (2015) Harvesting idle CPU resources for desktop grid computing while limiting the slowdown generated to end-users. Clust Comput 18(4):1331–1350
41.
Pramanik PKD, Pal S, Pareek G, Dutta S, Choudhury P (2018) Crowd computing: the computing revolution. In: Lenart-Gansiniec R (ed) Crowdsourcing and knowledge management in contemporary business environments. IGI Global, pp 166–198
42.
Pramanik PKD, Choudhury P, Saha A (2017) “Economical supercomputing thru smartphone crowd computing: an assessment of opportunities, benefits, deterrents, and applications from India’s perspective,” In: 4th International Conference on Advanced Computing and Communication Systems (ICACCS-2017), Coimbatore, India
43.
Massari G, Zanella M, Fornaciari W (2016) “Towards distributed mobile computing”, in Mobile System Technologies Workshop (MST). Italy, Milan
44.
Marinelli EE (2009) “Hyrax: cloud computing on mobile devices using MapReduce,” Masters Thesis, Carnegie Mellon University, Pittsburgh
45.
Dou A, Kalogeraki V, Gunopulos D, Mielikainen T, Tuulos VH (2010) “Misco: a MapReduce framework for mobile systems,” In: 3rd International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '10), Samos Greece
46.
Kakantousis T, Boutsis I, Kalogeraki V, Gunopulos D, Gasparis G, Dou A (2012) “Misco: a system for data analysis applications on networks of smartphones using MapReduce,” In: IEEE 13th International Conference on Mobile Data Management (MDM), Bengaluru, India
47.
Lee S, Grover K, Lim A (2013) Enabling actionable analytics for mobile devices: performance issues of distributed analytics on Hadoop mobile clusters. J Cloud Comput Adv Syst Appl 2:15
48.
Arnold E (2011) AVRF: a framework to enable distributed computing using volunteered mobile resources, vol. Paper 127, University of Puget Sound
49.
Dong Z, Kong L, Cheng P, He L, Gu Y, Fang L, Zhu T, Liu C (2014) “REPC: reliable and efficient participatory computing for mobile devices,” In: Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Singapore
50.
Dumont C, Mourlin F, Nel L (2016) “A mobile distributed system for remote resource access,” In: 14th International Conference on Advances in Mobile Computing and Multi Media (MoMM '16), Singapore
51.
Salem HM (2019) “Distributed computing system on a smartphones-based Network,” In: Mazzara M, Bruel JM, Meyer B, Petrenko A (eds.), Software Technology: Methods and Tools (TOOLS 2019). Lecture Notes in Computer Science, vol. 11771, Springer, Cham, pp. 313–325.
52.
Sanches P, Silva JA, Teófilo A, Paulino H (2020) “Data-centric distributed computing on networks of mobile devices,” In: Malawski M, Rzadca K (Eds.), Parallel processing (Euro-Par 2020). Lecture notes in computer science, vol. 12247, Springer, Cham, p. 296–311
53.
Attia DE, ElKorany AM, Moussa AS (2016) High performance computing over parallel mobile systems. Int J Adv Comput Sci Appl 7(9):99–103
54.
Conti M, Giordano S, May M, Passarella A (2010) From opportunistic networks to opportunistic computing. IEEE Commun Magaz 48(9):126–139
55.
Murray G, Yoneki E, Crowcroft J, Hand S (2010) “The case for crowd computing,” In: 2nd ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds (MobiHeld '10), New Delhi, India
56.
Shi C, Lakafosis V, Ammar MH, Zegura EW (2012) “Serendipity: enabling remote computing among intermittently connected mobile devices,” In: 13th ACM international symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '12), South Carolina, USA
57.
Mtibaa A, Harras KA, Habak K, Ammar M, Zegura EW (2015) “Towards mobile opportunistic computing,” In: IEEE 8th International Conference on Cloud Computing, New York, USA
58.
Tapparello C, Funai C, Hijazi S, Aquino A, Karaoglu B, Ba H, Shi J, Heinzelman W (2015) “Volunteer computing on mobile devices: state of the art and future research directions,” In: Enabling Real-Time Mobile Cloud Computing through Emerging Technologies, IGI Global, pp. 153–181
59.
Lavoie E, Hendren L, Desprez F, Correia MP (2019) “Pando: personal volunteer computing in browsers,” In: 20th International Middleware Conference (Middleware '19), California, United States
60.
Jenviriyakul P, Chalumporn G, Achalakul T, Costa F, Akkarajitsakul K (2019) ALICE Connex: a volunteer computing platform for the time-of-flight calibration of the ALICE experiment. An opportunistic use of CPU cycles on android devices. Futur Gener Comput Syst 94:510–523
61.
Arslan MY, Singh I, Singh S, Madhyastha HV, Sundaresan K, Krishnamurthy SV (2012) Computing while charging: building a distributed computing infrastructure using smartphones. In: 8th International Conference on Emerging Networking Experiments and Technologies (CoNEXT '12), France
62.
Arslan MY, Singh I, Singh S, Madhyastha HV, Sundaresan K, Krishnamurthy SV (2015) CWC: a distributed computing infrastructure using smartphones. IEEE Trans Mob Comput 14(8):1587–1600
63.
Schildt S, Busching F, Jorns E, Wolf L (2013) “CANDIS: heterogeneous mobile cloud framework and energy cost-aware scheduling,” In: IEEE GreenCom iThings/CPSCom, Beijing
64.
Phan T, Huang L, Dulan C (2002) “Integrating mobile wireless devices into the computational grid,” In: 8th Annual International Conference on Mobile Computing and Networking (MobiCom '02), Atlanta, USA
65.
Phan T, Huang L, Dulan C (2002) “Challenge: integrating mobile wireless devices into the computational grid,” In: 8th Annual International Conference on Mobile Computing and Networking (MobiCom '02), New York, USA
66.
Gonzalez-Castano F, Vales-Alonso J, Livny M (April 2002) Condor grid computing from mobile handheld devices. Mob Comput Commun Rev 6(2):117–126
67.
Clarke BP, Humphrey M (2002) “Beyond the 'device as portal': meeting the requirements of wireless and mobile devices in the legion grid computing system,” In: 16th International Parallel and Distributed Processing Symposium (IPDPS 2002), Fort Lauderdale, FL, USA
68.
Chu DC, Humphrey M (2004) “Mobile OGSI.NET: grid computing on mobile devices,” In: 5th IEEE/ACM International Workshop on Grid Computing (associated with Supercomputing 2005), Pittsburgh, PA
69.
Kurkovsky S, Bhagyavati (2003) “Wireless grid enables ubiquitous computing,” In: 16th International Conference on Parallel and Distributed Computing Systems (PDCS-2003), Reno, NV
70.
Kurkovsky S, Bhagyavati, Ray A (2004) A collaborative problem-solving framework for mobile devices. In: 42nd Annual Southeast Regional Conference (ACM-SE 42), New York, USA
71.
Katsaros K, Polyzos GC (2007) “Optimizing operation of a hierarchical campus-wide mobile grid for intermittent wireless connectivity,” In: 15th IEEE Workshop on Local & Metropolitan Area Networks, Princeton, USA
72.
Sriraman RK (2014) “Grid computing on mobile devices: a point of view,” Altimetrik Insights
73.
Huerta-Canepa G, Lee D (2010) “A virtual cloud computing provider for mobile devices,” In: 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond (MCS '10), San Francisco, California
74.
A Khalifa A, Hassan R, Eltoweissy M (2011) “Towards ubiquitous computing clouds,” In: 3rd International Conference on Future Computational Technologies and Applications, Rome, Italy
75.
Khalifa A, Eltoweissy M (2012) “A global resource positioning system for ubiquitous clouds,” In: International Conference on Innovations in Information Technology (IIT), Abu Dhabi, UAE
76.
Khalifa A, Eltoweissy M (2013) “Collaborative autonomic resource management system for mobile cloud computing,” In: The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization, Valencia, Spain
77.
Khalifa A, Eltoweissy M (2013) “MobiCloud: a reliable collaborative mobilecloud management system,” In: 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, Austin, USA
78.
Miluzzo E, Cáceres R, Chen YF (2012) “Vision: mClouds–computing on clouds of mobile devices,” In: 3rd ACM workshop on Mobile cloud computing and services (MCS’12), Low Wood Bay, UK
79.
Khalifa A, Azab M, Eltoweissy M (2014) “Resilient hybrid mobile ad-hoc cloud over collaborating heterogeneous nodes,” In: 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, Miami, USA
80.
Funai C, Tapparello C, Ba H, Karaoglu B, Heinzelman W (2014) “Extending volunteer computing through mobile ad hoc networking,” In: IEEE Global Communications Conference, Austin, USA
81.
Remédios D, Teófilo A, Paulino H, Lourenço J (2015) “Mobile device-to-device distributed computing using data sets,” In: 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS), Coimbra, Portugal
82.
Yaqoob I, Ahmed E, Gani A, Mokhtar S, Imran M (2017) Heterogeneity-aware task allocation in mobile ad hoc cloud. IEEE Access 5:1779–1795
83.
Balasubramanian V, Karmouch A (2017) “An infrastructure as a service for mobile ad-hoc cloud,” In: IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA
84.
Loke SW (2017) Crowd+cloud machines. Crowd-powered mobile computing and smart things. Springer, Cham, pp 11–25
85.
Kumar MP, Bhat RR, Alavandar SR, Ananthanarayana VS (2018) “Distributed public computing and storage using mobile devices,” In: IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India
86.
Kündig S, Angelopoulos CM, Kuppannagari SR, Rolim J, Prasanna VK (2020) “Crowdsourced edge: a novel networking paradigm for the collaborative community,” In: 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina del Rey, USA
87.
88.
89.
Duda J, Dłubacz W (2013) “Distributed evolutionary computing system capable to use mobile devices,” In: Conference of Informatics and Management Sciences
90.
91.
Lavoie E, Hendren L (2019) “Personal volunteer computing,” In: 16th ACM International Conference on Computing Frontiers (CF '19), Alghero, Italy
92.
93.
NVIDIA, “The benefits of multiple CPU cores in mobile devices,” NVIDIA Corporation, 2010.
94.
“ARM and QUALCOMM: enabling the next mobile computing revolution with highly integrated ARMv8-A based SoCs,” ARM/Qualcomm, (2014)
95.
96.
97.
Asaduzzaman A, Gummadi D, Yip CM (2014) “A talented CPU-to-GPU memory mapping technique,” In: IEEE SOUTHEASTCON 2014, Lexington, KY
98.
Cullinan C, Wyant C, Frattesi T (2012) “Computing performance benchmarks among CPU, GPU, and FPGA,” MathWorks
99.
Nickolls J, Dally WJ (2010) The GPU computing era. IEEE Comput Soc 30(2):56–69
100.
Muralidharan N, Wunnava S, Noel A (2004) “The system on chip technology,” In: 2nd Latin American and Caribbean Conference for Engineering and Technology (LACCEI’2004), Miami, Florida
101.
102.
Rajovicxz N, Carpenterx PM, Geladox I, Puzovicx N, Ramirezxz A, Valero M (2013) “Supercomputing with commodity CPUs: are mobile SoCs ready for HPC?,” In: International Conference on High Performance Computing, Networking, Storage and Analysis (SC ’13), Denver, USA
103.
104.
Cisco (2016) “Cisco visual networking index: global mobile data traffic forecast update, 2015–2020,” Cisco
105.
GSMA Intelligence (2022) “The mobile economy 2022,” GSMA
106.
107.
108.
109.
Orange (2022) “Orange’s vision for 6G,” Orange
110.
111.
112.
Oppo (2021) “6G AI-cube intelligent networking”
113.
114.
Heydon R (2012) Bluetooth low energy: the developer’s handbook. Prentice Hall
115.
Pramanik PKD, Nayyar A, Pareek G (2019) WBAN: driving e-healthcare beyond telemedicine to remote health monitoring. Architecture and protocols. In: Hemanth DJ, Balas VE (eds) Telemedicine technologies: big data UK deep learning, robotics, mobile and remote applications for global healthcare. Elsevier, pp 89–119
116.
Falaki H, Mahajan R, Kandula S, Lymberopoulos D, Govindan R, Estrin D (2010) “Diversity in smartphone usage,” In: MobiSys’10, San Francisco, USA
117.
Wagner DT, Rice A, Beresford AR (2014) Device analyzer: understanding smartphone usage. Mobile and ubiquitous systems: computing, networking, and services, vol 131. Springer International Publishing, pp 195–208
118.
Schneider D, Moraes K, Souza JMD, Esteves MGP (20120 “CSCWD: five characters in search of crowds,” In: IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Wuhan, China
119.
Buyya R, Venugopal S (2005) “A gentle introduction to grid computing and technologies,” Database 2(R3)
120.
Jacob B, Brown M, Fukui K, Trivedi N (2005) Introduction to grid computing. IBM Redbooks
121.
Joseph J (2004) Grid computing. Pearson Education India
122.
Cerin C, Fedak G (2019) Desktop grid computing. Chapman and Hall/CRC
123.
Constantinescu-Fuløp Z (2008) “A desktop grid computing approach for scientific computing and visualization”
124.
Wu C, Buyya R, Ramamohanarao K (2020) Cloud pricing models: taxonomy, survey, and interdisciplinary challenges. ACM Comput Surv 52(6):1–36
125.
Jin H, Ibrahim S, Bell T, Gao W, Huang D, Wu S (2010) Cloud types and services. In: Furht B, Escalante A (eds) Handbook of cloud computing. Springer, Boston, MA, pp 335–355
126.
Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1:7–18
127.
Yeo CS, Buyya R, Pourreza H, Eskicioglu R, Graham P, Sommers F (2006) Cluster computing: high-performance, high-availability, and high-throughput processing on a network of computers. In: Zomaya AY (ed) Handbook of nature-inspired and innovative computing. Springer, Boston, MA, pp 521–551
128.
Baker M, Buyya R, Hyde D (1999) Cluster computing: a high-performance contender. Computer 32(7):79–83
129.
130.
Martínez A, Prieto S, Gallego N, Nou R, Giralt J, Cortes T (2010) “XtreemOS-MD: grid computing from mobile devices,” In: Cai Y, Magedanz T, Li M, Xia J, Giannelli C (eds) Mobile Wireless Middleware, Operating Systems, and Applications (MOBILWARE 2010). Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 48, Springer, Berlin, Heidelberg, pp 45–58.
131.
Wehner CB, Wehner MF, Snow SA (2010) “Mobile grid computing”. USA Patent US20100281095A1, 4 November 2010
132.
Furthmüller J, Waldhorst OP (2012) Survey on grid computing on mobile consumer devices. Grid and cloud computing: concepts methodologies tools and applications. IGI Global, pp 1197–1220
133.
Noor TH, Zeadally S, Alfazi A, Sheng QZ (2018) Mobile cloud computing: challenges and future research directions. J Netw Comput Appl 115:70–85
134.
Shiraz M, Sookhak M, Gani A, Shah SAA (2015) A study on the critical analysis of computational offloading frameworks for mobile cloud computing. J Netw Comput Appl 47:47–60
135.
Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Futur Gener Comput Syst 29(1):84–106
136.
Rahimi MR, Ren J, Liu CH, Vasilakos AV, Venkatasubramanian N (2014) Mobile cloud computing: a survey, state of art and future directions. Mobile Netw Appl 19:133–143
137.
Nayyer MZ, Raza I, Hussain SA (2018) A survey of cloudlet-based mobile augmentation approaches for resource optimization. ACM Comput Surv 51(5):1–28
138.
Greengard S (2011) Following the crowd. Commun ACM 54:20–22
139.
Vukovic M, Bartolini C (2010) Towards a research agenda for enterprise crowdsourcing. In: Margaria T, Steffen B (eds) leveraging applications of formal methods, verification, and validation. Springer, Berlin/Heidelberg, pp 425–434
140.
Buettner R (2015) “A systematic literature review of crowdsourcing research from a human resource management perspective,” In: 48th Annual Hawaii International Conference on System Sciences, Kauai, Hawaii
141.
Chatzimilioudis G, Konstantinidis A, Laoudias C, Zeinalipour-Yazti D (2012) Crowdsourcing with smartphones. IEEE Internet Comput 16(5):36–44
142.
Ray A, Chowdhury C, Bhattacharya S, Roy S (2022) A survey of mobile crowdsensing and crowdsourcing strategies for smart mobile device users. CCF Trans Pervasive Comput Interact 5(1):98–123
143.
Phuttharak J, Loke SW (2018) A review of mobile crowdsourcing architectures and challenges: toward crowd-empowered Internet-of-Things. IEEE Access 7:304–324
144.
Kong X, Liu X, Jedari B, Li M, Wan L, Xia F (2019) Mobile crowdsourcing in smart cities: technologies, applications, and future challenges. IEEE Internet Things J 6(5):8095–8113
145.
Guo B, Wang Z, Yu Z, Wang Y, Yen NY, Huang R, Zhou X (2015) Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput Surv 48(1):1–31
146.
147.
Anderson DP (2020) BOINC: a platform for volunteer computing. J Grid Comput 18:99–122
148.
Curiel M, Calle DF, Santamaría AS, Suarez DF, Flórez L (2018) Parallel processing of images in mobile devices using BOINC. Open Eng 8(1):87–101
149.
Maluk Mohamed M, Vijay Srinivas A, Janakiram D (2005) Moset: An anonymous remote mobile cluster computing paradigm. J Parallel Distrib Comput 65(10):1212–1222
150.
Kandappu T, Misra A, Cheng SF, Jaiman N, Tandriansiyah R, Chen C, Lau HC, Chander D, Dasgupta K (2016) “Campus-scale mobile crowd-tasking: deployment & behavioral insights,” In: 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW '16), San Francisco; USA
151.
McKnight LW, Howison J, Bradner S (2004) Guest editors’ introduction: wireless grids - distributed resource sharing by mobile, nomadic, and fixed devices. IEEE Internet Comput 8:24–31
152.
Pramanik PKD, Sinhababu N, Nayyar A, Masud M, Choudhury P (2021) Predicting resource availability in local mobile crowd computing using convolutional GRU. Comput Mater Contin 70(3):5199–5212
153.
Pramanik PKD, Bandyopadhyay G, Choudhury P (2020) Predicting relative topological stability of mobile users in a P2P mobile cloud. SN Appl Sci 2:1–13
154.
Li LSH, Ifeachor EC (2005) “Challenges of mobile ad-hoc grids and their applications in e-healthcare,” In: 2nd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2005)
155.
Dan MC, Gabriela MM, Ji Y, Ladislau B, Siegel HJ (2003) “Ad hoc grids: communication and computing in a power constrained environment,” In: IEEE International Conference on Performance, Computing, and Communications, Phoenix, USA
156.
Karra K (2012) Wireless distributed computing on the Android platform. Virginia Polytechnic Institute and State University
157.
Storm C (2012) Fault tolerance in distributed computing. Specification and analytical evaluation of heterogeneous dynamic quorum-based data replication Schemes. Springer, pp 3–79
158.
Cristian F, Aghili H, Strong HR, Dolev D (1995) Atomic broadcast: from simple message diffusion to Byzantine agreement. Inf Comput 118(1):158–179MathSciNet
159.
Cristian F (1991) Understanding fault-tolerant distributed systems. Commun ACM 34(2):56–78
160.
Sari A, Akkaya M (2015) Fault tolerance mechanisms in distributed systems. Int J Commun Netw Syst Sci 8(12):471–482
161.
Gärtner FC (1999) Fundamentals of fault-tolerant distributed computing in asynchronous environments. ACM Comput Surv 31(1):1–26
162.
Poola D, Salehi MA, Ramamohanarao K, Buyya R (2017) “A taxonomy and survey of fault-tolerant workflow management systems in cloud and distributed computing environments.” In: Mistrik I, Bahsoon R, Ali N, Heisel M, Maxim B (eds) Software architecture for big data and the cloud. Morgan Kaufmann, UK, pp 285–320
163.
Elnozahy EN, Alvisi L, Wang Y-M, Johnson DB (2002) A survey of rollback-recovery protocols in message-passing systems. ACM Comput Surv 34(3):375–408
164.
Alvisi L, Marzullo K (1995) “Message logging: pessimistic, optimistic, and causal,” In: 15th International Conference on Distributed Computing, Systems (ICDCS 1995), Vancouver
165.
Pramanik PKD, Choudhury P (2020) “Mobility-aware service provisioning for delay tolerant applications in a mobile crowd computing environment.” SN Appl Sci 2(3):1–17
166.
Mengistu T, Alahmadi A, Albuali A, Alsenani Y, Che D (2017) “A “no data center” solution to cloud computing,” In: IEEE 10th International Conference on Cloud Computing (CLOUD), Honololu, USA
167.
168.
Pramanik PKD, Sinhababu N, Kwak K-S, Choudhury P (2021) Deep learning based resource availability prediction for local mobile crowd computing. IEEE Access 9:116647–116671
169.
Pramanik PKD, Biswas S, Pal S, Marinković D, Choudhury P (2021) A comparative analysis of multi-criteria decision-making methods for resource selection in mobile crowd computing. Symmetry 13(9):1713
170.
Pramanik PKD, Sinhababu N, Nayyar A, Choudhury P (2021) “Predicting device availability in mobile crowd computing using ConvLSTM,” In: 7th International Conference on Optimization and Applications (ICOA), Wolfenbüttel, Germany
171.
Zhou A, Wang S, Li J, Sun Q, Yang F (2016) Optimal mobile device selection for mobile cloud service providing. J Supercomput 72(8):3222–3235
172.
Shah SC, Park M-S (2011) An energy-efficient resource allocation scheme for mobile ad hoc computational grids. J Grid Comput 9:303–323
173.
174.
Basık F, Gedik B, Ferhatosmanoğlu H, Wu K-L (2021) Fair task allocation in crowdsourced delivery. IEEE Trans Serv Comput 14(4):1040–1053
175.
Kravtsov V, Carmeli D, Dubitzky W, Orda A, Schuster A, Silberstein M, Yoshpa B (2008) “Quasi-opportunistic supercomputing in grid environments,” In: 8th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2008), Cyprus
176.
Dogac A, Gokkoca E, Arpinar S, Koksal P, Cingil I, Arpinar B, Tatbul N, Karagoz P, Halici U, Altinel M (1998) Design and implementation of a distributed workflow management system: METUFlow. In: Doğaç A, Kalinichenko L, Özsu MT, Sheth A (eds) Workflow management systems and interoperability NSATO ASI Series, vol 164. Springer, Berlin, Heidelberg, pp 61–91
177.
Wang L, Jie W, Zhu H (2006) State-of-arts: workflow management for grid computing. In: Dev T (ed) Grid technologies: emerging from distributed architectures to virtual organizations. WIT Press, Southampton, pp 241–270
178.
Yu J, Buyya R (2005) A taxonomy of scientific workflow systems for grid computing. ACM SIGMOD Rec 34(3):44–49
179.
Alonso G, Günthör R, Kamath M, Agrawal D, El Abbadi A, Mohan C (1996) Exotica/FMDC: a workflow management system for mobile and disconnected clients. In: Barbara D, Jain R, Krishnakumar N (eds) Databases and Mobile Computing. Springer, Boston, pp 27–45
180.
Tang F, Guo M, Dong M, Li M, Guan H (2008) “Towards context-aware workflow management for ubiquitous computing,” In: International Conference on Embedded Software and Systems, Chengdu, China
181.
Tarkoma S, Siekkinen M, Lagerspetz E, Xiao Y (2014) Overview. Smartphone energy consumption: modeling and optimization. Cambridge University Press, Cambridge, pp 227–233
182.
183.
Yu J, Williams E, Ju M (2010) Analysis of material and energy consumption of mobile phones in China. Energy Policy 38(8):4135–4141
184.
185.
186.
Pramanik PKD, Pal S, Choudhury P (2019) Smartphone crowd computing: a rational solution towards minimising the environmental externalities of the growing computing demands. In: Das R, Banerjee M, De S (eds) Emerging Trends in Disruptive Technology Management. Chapman and Hall/CRC, New York, pp 45–80
187.
Pramanik PKD, Pal S, Choudhury P (2019) Green and sustainable high-performance computing with smartphone crowd computing: benefits, enablers, and challenges. Scal Comput Pract Exp 20(2):259–283
188.
Pramanik PKD, Sinhababu N, Mukherjee B, Padmanaban S, Maity A, Upadhyaya BK, Holm-Nielsen JB, Choudhury P (2019) Power consumption analysis, measurement, management, and issues: a state-of-the-art review on smartphone battery and energy usage. IEEE Access 7(1):182113–182172
189.
190.
Pei C, Wang Z, Zhao Y, Wang Z, Meng Y, Pei D, Peng Y, Tang W, Qu X (2017) Why it takes so long to connect to a WiFi access point? In: IEEE Conference on Computer Communications (IEEE INFOCOM), Atlanta, USA
191.
192.
193.
194.
Yang K, Zhang K, Ren J, Shen X (2015) Security and privacy in mobile crowdsourcing networks: challenges and opportunities. IEEE Commun Mag 53(8):75–81
195.
Feng W, Yan Z, Zhang H, Zeng K, Xiao Y, Hou YT (2018) A survey on security, privacy, and trust in mobile crowdsourcing. IEEE Internet Things J 5(4):2971–2992
196.
Ma Y, Sun Y, Lei Y, Qin N, Liu J (2020) A survey of blockchain technology on security, privacy, and trust in crowdsourcing services. World Wide Web 23:393–419
197.
Allahbakhsh M, Ignjatovic A, Benatallah B, Beheshti SMR, Bertino E, Foo N (2012) “Reputation management in crowdsourcing systems,” In: 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), Pittsburgh, USA
198.
Padmavathi DG, Shanmugapriya MD (2009) “A survey of attacks, security mechanisms and challenges in wireless sensor networks,” Int J Comput Sci Inf Secur, 4(1 & 2)
199.
Kaur M, Bansal MM (2015) A survey on security and privacy challenges in mobile grid computing. Int J Adv Cloud Comput Comput Sci 1(2):20–26
200.
Buttyan L, Hubaux JP (2010) “Enforcing service availability in mobile ad-hoc WANs,” In: First Annual Workshop on Mobile and Ad Hoc Networking and Computing (MobiHOC), Boston, USA
201.
Bibi I, Akhunzada A, Malik J, Khan MK, Dawood M (2022) Secure distributed mobile volunteer computing with Android. ACM Trans Internet Technol 22(1):1–21
202.
Rasool S, Iqbal M, Dagiuklas T, Ul-Qayyum Z, Li S (2019) Reliable data analysis through blockchain based crowdsourcing in mobile ad-hoc cloud. Mob Netw Appl 25:153–163
203.
Feng W, Yan Z (2019) MCS-chain: decentralized and trustworthy mobile crowdsourcing based on blockchain. Futur Gener Comput Syst 95:649–666
204.
205.
Lu Y, Tang Q, Wang G (2018) “ZebraLancer: private and anonymous crowdsourcing system atop open blockchain,” In: IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria
206.
Li M, Weng J, Yang A, Lu W, Zhang Y, Hou L, Liu J-N, Xiang Y, Deng RH (2019) CrowdBC: a blockchain-based decentralized framework for crowdsourcing. IEEE Trans Parallel Distrib Syst 30(6):1251–1266
207.
Seebacher S, Schüritz R (2017) “Blockchain technology as an enabler of service systems: a structured literature review,” In: International Conference on Exploring Services Science, Italy
208.
Bellini E, Iraqi Y, Damiani E (2020) Blockchain-based distributed trust and reputation management systems: a survey. IEEE Access 8:21127–21151
209.
Huang C, Wang Z, Chen H, Hu Q, Zhang Q, Wang W, Guan X (2021) RepChain: a reputation based secure, fast and high incentive blockchain system via sharding. IEEE Internet Things J 8(6):4291–4304
210.
Shahid A, Sarfraz U, Malik MW, Iftikhar MS, Jamal A, Javaid N (2020) Blockchain-based reputation system in agri-food supply chain. In: Barolli L, Amato F, Moscato F, Enokido T, Takizawa M (eds) Advanced information networking and applications (AINA 2020). Advances in intelligent systems and computing, vol 1151. Springer, Cham, pp 12–21
211.
Sun Y, Zhang N (2017) A resource-sharing model based on a repeated game in fog computing. Saudi J Biol Sci 24(3):687–694
212.
Islam L, Alvi ST, Uddin MN, Rahman M (2019) “Obstacles of mobile crowdsourcing: a survey,” In: IEEE Pune Section International Conference (PuneCon), Pune, India
213.
214.
Zhang X, Yang Z, Sun W, Liu Y, Tang S, Xing K, Mao X (2016) Incentives for mobile crowd sensing: a survey. IEEE Commun Surv Tutor 18(1):54–67
215.
Muldoon C, O’Grady MJ, O’Hare GMP (2018) A survey of incentive engineering for crowdsourcing. Knowl Eng Rev 33:E2
216.
217.
Hu C, Xiao M, Huang L, Gao G (2016) “Truthful incentive mechanism for vehicle-based nondeterministic crowdsensing,” In: IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, China
218.
Ju Z, Huang C, Chen Y, Ma L (2017) “A truthful auction mechanism for resource provisioning in mobile crowdsensing,” In: IEEE 36th International Performance Computing and Communications Conference (IPCCC), San Diego, USA
219.
Fan Y, Sun H, Liu X (2015) “Truthful incentive mechanisms for dynamic and heterogeneous tasks in mobile crowdsourcing,” In: IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, Italy
220.
Huang C, Yu H, Berry RA, Huang J (2022) Using truth detection to incentivize workers in mobile crowdsourcing. IEEE Trans Mob Comput 21(6):2257–2270
221.
Li Q, Cao H, Wang S, Zhao X (2020) A reputation-based multi-user task selection incentive mechanism for crowdsensing. IEEE Access 8:74887–74900
222.
Sun J, Hou F, Ma S (2015) “Reputation-aware incentive mechanism for participatory sensing,” In: IEEE/CIC International Conference on Communications in China (ICCC), Shenzhen, China
223.
Ma X, Ma J, Li H, Jiang Q, Gao S (2016) RTRC: a reputation-based incentive game model for trustworthy crowdsourcing service. China Commun 13(12):199–215
224.
Jiang L-Y, He F, Wang Y, Sun L-J, Huang H-P (2017) Quality-aware incentive mechanism for mobile crowd sensing. J Sens 18(11):2589–2603
225.
Peng D, Wu F, Chen G (2015) “Pay as how well you do: a quality based incentive mechanism for crowdsensing,” In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '15), Hangzhou, China
226.
Wang J, Tang J, Yang D, Wang E, Xue G (2016) “Quality-aware and fine-grained incentive mechanisms for mobile crowdsensing,” In: IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, Japan
227.
228.
229.
Jain A, Tyagi N (2013) Collision detection and avoidance in railways using WiMAX. Indian J Comput Sci Eng 3(6):789–795
230.
231.
232.
233.
234.
235.
Satyanarayanan M (2010) “Mobile computing: the next decade,” In: 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond (MCS’10), New York, USA
236.
Kate A, Goldberg I (2010) “Distributed private-key generators for identity based cryptography,” In: Garay JA, De Prisco R (eds.), Security and Cryptography for Networks. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol. 6280, pp. 436–453
237.
Chang T, Chen C, Hsiao H, Lai G (2018) The cryptanalysis of WPA & WPA2 using the parallel-computing with GPUs. In: You I, Leu FY, Chen HC, Kotenko I (eds) Mobile internet security (MobiSec 2016), vol 797. Communications in computer and information science. Springer, Singapore, pp 118–127
238.
Yong-lei L, Zhi-gang J (2015) Distributed method for cracking WPA/WPA2-PSK on multi-core CPU and GPU architecture. Int J Commun Syst 28(4):723–742
239.
Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39
240.
Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646
241.
Pramanik PKD, Choudhury P (2018) IoT data processing: the different archetypes and their security & privacy assessments. In: Shandilya K, Chun SA, Shandilya S, Weippl E (eds) Internet of Things (IoT) security: fundamentals techniques and applications. SRiver Publishers, pp 37–54
242.
Du R, Santi P, Xiao M, Vasilakos AV, Fischione C (2019) The sensable city: a survey on the deployment and management for smart city monitoring. IEEE Commun Surv Tutor 21(2):1533–1560
243.
244.
Lee SW, Yabuuchi N, Gallant BM, Chen S, Kim BS, Hammond PT, Shao-Horn Y (2010) High-power lithium batteries from functionalized carbon-nanotube electrodes. Nat Nanotechnol 5(7):531
245.
Bulut E, Ahsen ME, Szymanski BK (2014) “Opportunistic wireless charging for mobile social and sensor networks,” In: IEEE Globecom Workshops (GC Wkshps), Austin, USA
246.
Nikoletseas S, Raptis TP, Raptopoulos C (2017) Wireless charging for weighted energy balance in populations of mobile peers. Ad Hoc Netw 60:1–10
247.
248.
Gao Y, Yan Z, Gray JL, He X, Wang D, Chen T, Huang Q, Li YC, Wang H, Kim SH, Mallouk TE, Wang D (2019) Polymer–inorganic solid–electrolyte interphase for stable lithium metal batteries under lean electrolyte conditions. Nat Mater 18:384–389
249.
Fan X, Hu E, Ji X, Zhu Y, Han F, Hwang S, Liu J, Bak S, Ma Z, Gao T, Liou S-C, Bai J, Yang X-Q, Mo Y, Xu K, Su D, Wang C (2018) High energy-density and reversibility of iron fluoride cathode enabled via an intercalation-extrusion reaction. Nat Commun 9:2324
250.
Spingler FB, Wittmann W, Sturm J, Rieger B, Jossen A (2018) Optimum fast charging of lithium-ion pouch cells based on local volume expansion criteria. J Power Sour 393:152–160
251.
Pham VH, Boscoboinik JA, Stacchiola DJ, Self EC, Manikandan P, Nagarajan S, Wang Y, Pol VG, Nanda J, Paek E, Mitlin D (2019) Selenium-sulfur (SeS) fast charging cathode for sodium and lithium metal batteries. Energy Storage Mater 20:71–79
252.
Zheng J, Engelhard MH, Mei D, Jiao S, Polzin BJ, Zhang J-G, Xu W (2017) Electrolyte additive enabled fast charging and stable cycling lithium metal batteries. Nat Energy 2:1–8
253.
Zou W, Xia F-J, Song J-P, Wu L, Chen L-D, Chen H, Liu Y, Dong W-D, Wu S-J, Hu Z-Y, Liu J, Wang H-E, Chen L-H, Li Y, Peng D-L, Su B-L (2019) Probing and suppressing voltage fade of Li-rich Li1.2Ni0.13Co0.13Mn0.54O2 cathode material for lithium-ion battery. Electrochim Acta 318:875–882
254.
Zhang Q, Xu Z, Lu B (2016) Strongly coupled MoS2–3D graphene materials for ultrafast charge slow discharge LIBs and water splitting applications. Energy Storage Mater 4:84–91
255.
Wu P, Shao G, Guo C, Lu Y, Dong X, Zhong Y, Liu A (2019) Long cycle life, low self-discharge carbon anode for Li-ion batteries with pores and dual-doping. J Alloy Compd 802:620–627
256.
Hao M, Li J, Park S, Moura S, Dames C (2018) Efficient thermal management of Li-ion batteries with a passive interfacial thermal regulator based on a shape memory alloy. Nat Energy 3:899–906
257.
Wang Y, Zhu D, Yang Y, Lee K, Mishra R, Go G, Oh S-H, Kim D-H, Cai K, Liu E, Pollard SD, Shi S, Lee J, Teo KL, Wu Y, Lee K-J, Yang H (2019) Magnetization switching by magnon-mediated spin torque through an antiferromagnetic insulator. Science 366(6469):1125
258.
Tomizawa Y, Sasaki K, Kuroda A, Takeda R, Kaito Y (2016) Experimental and numerical study on phase change material (PCM) for thermal management of mobile devices. Appl Therm Eng 98:320–329
259.
Gao Y, Li X, Li J, Gao Y (2017) “A dynamic-trust-based recruitment framework for mobile crowd sensing,” In: IEEE International Conference on Communications (ICC), Paris, France
260.
Wang K, Qi X, Shu L, Deng D-J, Rodrigues JJPC (2016) Toward trustworthy crowdsourcing in the social internet of things. IEEE Wirel Commun 23(5):30–36
261.
Tan L, Xiao H, Shang X, Wang Y, Ding F, Li W (2020) “A blockchain-based trusted service mechanism for crowdsourcing system,” In: IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium
262.
Meftah L, Rouvoy R, Chrisment I (2021) Empowering mobile crowdsourcing apps with user privacy control. J Parallel Distrib Comput 147:1–15
263.
264.
Lin C, He D, Zeadally S, Kumar N, Choo K-KR (2020) SecBCS: a secure and privacy-preserving blockchain-based crowdsourcing system. Sci China Inf Sci 63:1–14MathSciNet
265.
266.
267.
Ma Q, Gao L, Liu YF, Huang J (2016) “A contract-based incentive mechanism for crowdsourced wireless community networks,” In: 14th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Tempe, USA
268.
Jaimes LG, Chakeri A, Lopez J, Raij A (2015) “A cooperative incentive mechanism for recurrent crowd sensing,” In: SoutheastCon, Fort Lauderdale, USA
269.
Yang X, Zhang J, Peng J, Lei L (2021) Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing. Int J Distrib Sens Netw 17(6):15501477211023010
270.
Ueyama Y, Tamai M, Arakawa Y, Yasumoto K (2014) “Gamification-based incentive mechanism for participatory sensing,” In: IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary
271.
Zhang Q, Zhang Q, Liu X, Dai J, Zhang X (2019) “The evolutionary game analysis of incentive mechanism for crowd sensing of public environment,”In: Journal of Physics: Conference Series, vol. 1187, no. 5
272.
Ahuja N, Eshaghian-Wilner MM, Ge Z, Liu R, Pati ASN, Ravicz K, Schlesinger M, Wu SH, Xie K (2016) Wireless power for implantable devices: a technical review. In: Eshaghian-Wilner MM (ed) Wireless computing in medicine: from nano to cloud with its ethical and legal implications. Wiley, pp 187–209
273.
Pang L, Li G, Yao X, Lai Y (2019) An incentive mechanism based on a Bayesian game for spatial crowdsourcing. IEEE Access 7:14340–14352
274.
Luo S, Sun Y, Ji Y, Zhao D (2016) Stackelberg game based incentive mechanisms for multiple collaborative tasks in mobile crowdsourcing. Mob Netw Appl 21:506–522
275.
Yang D, Xue G, Fang X, Tang J (2012) “Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing,” In: 18th Annual International Conference on Mobile Computing and Networking (Mobicom '12), Istanbul, Turkey
276.
Zhao N, Fan M, Tian C, Fan P (2017) Contract-based incentive mechanism for mobile crowdsourcing networks. Algorithms 10(3):104
277.
Zhang Y, Jiang C, Song L, Pan M, Dawy Z, Han Z (2017) Incentive mechanism for mobile crowdsourcing using an optimized tournament model. IEEE J Sel Areas Commun 35(4):880–892
278.
Zhang Y, Gu Y, Song L, Pan M, Dawy Z, Han Z (2015) “Tournament based incentive mechanism designs for mobile crowdsourcing,” In: IEEE Global Communications Conference (GLOBECOM), San Diego, USA
279.
Yang D, Xue G, Fang X, Tang J (2016) Incentive mechanisms for crowdsensing: crowdsourcing with smartphones. IEEE/ACM Trans Netw 24(3):1732–1744
280.
281.
Zhang H, Liu B, Susanto H, Xue G (2015) “Auction-based incentive mechanisms for dynamic mobile ad-hoc crowd service,” arXiv, vol. 1503.06819v1 [cs.NI]
282.
Liu Y, Li H, Zhao G, Duan J (2018) “A reverse auction based incentive mechanism for mobile crowdsensing,” In: IEEE International Conference on Communications (ICC), Kansas City, USA
283.
Jin H, Su L, Chen D, Nahrstedt K, Xu J (2015) “Quality of information aware incentive mechanisms for mobile crowd sensing systems,” In: 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '15), Hangzhou, China
284.
Zhou T, Jia B, Li W (2019) “A reverse auction incentive mechanism based on the participant’s behavior in crowdsensing,” In: Li J, Liu Z, Peng H, (Eds.), Security and Privacy in New Computing Environments (SPNCE 2019). Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 284, Springer, Cham, p 637–646
285.
Yang G, He S, Shi Z, Chen J (2017) Promoting cooperation by the social incentive mechanism in mobile crowdsensing. IEEE Commun Mag 55(3):86–92
286.
Jaimes LG, Vergara-Laurens I, Chaker A (2014) “SPREAD, a crowd sensing incentive mechanism to acquire better representative samples,” In: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary
287.
Khatib RFE, Zorba N, Hassanein HS (2018) “A fair reputation-based incentive mechanism for cooperative crowd sensing,” In: IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE
288.
Zhang X, Xue G, Yu R, Yang D, Tang J (2017) Countermeasures against false-name attacks on truthful incentive mechanisms for crowdsourcing. IEEE J Sel Areas Commun 35(2):478–485
289.
Kamhoua GAK (2019) “Mitigating colluding attacks in online social networks and crowdsourcing platforms,” PhD Thesis, Florida International University
290.
291.
Gong Y, Wei L, Guo Y, Zhang C, Fang Y (2016) Optimal task recommendation for mobile crowdsourcing with privacy control. IEEE Internet Things J 3(5):745–756
292.
Zhao B, Tang S, Liu X, Zhang X, Chen W-N (2021) iTAM: bilateral privacy-preserving task assignment for mobile crowdsensing. IEEE Trans Mob Comput 20(12):3351–3366
293.
Shu J, Jia X, Yang K, Wang H (2021) Privacy-preserving task recommendation services for crowdsourcing. IEEE Trans Serv Comput 14(1):235–247
294.
Chi Z, Wang Y, Huang Y, Tong X (2017) The novel location privacy-preserving CKD for mobile crowdsourcing systems. IEEE Access 6:5678–5687
295.
Qiu G, Shen Y, Cheng K, Liu L, Zeng S (2021) Mobility-aware privacy-preserving mobile crowdsourcing. Sensors 21(7):2474
296.
Zhu S, Hu H, Li Y, Li W (2019) “Hybrid blockchain design for privacy preserving crowdsourcing platform,” In: IEEE International Conference on Blockchain, Atlanta, USA
297.
Wang J, Sun G, Gu Y, Liu K (2020) ConGradetect: blockchain-based detection of code and identity privacy vulnerabilities in crowdsourcing. J Syst Architect 114:101910
298.
Xu X, Liu Q, Zhang X, Zhang J, Qi L, Dou W (2019) A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans Comput Soc Syst 6(6):1407–1419
299.
Shu J, Jia X (2016) “Secure task recommendation in crowdsourcing,” In: IEEE Global Communications Conference (GLOBECOM), Washington, DC
300.
Qin H, Zhang Y, Li B (2017) “Truthful mechanism for crowdsourcing task assignment,” In: IEEE 10th International Conference on Cloud Computing (CLOUD), Honololu, USA
301.
Khanfor A, Hamrouni A, Ghazzai H, Yang Y, Massoud Y(2020) “A trustworthy recruitment process for spatial mobile crowdsourcing in large-scale social IoT,” In: IEEE Technology & Engineering Management Conference (TEMSCON), Novi, USA
302.
Halabi T, Zulkernine M (2019) “Reliability-driven task assignment in vehicular crowdsourcing: a matching game,” In: 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Portland, USA
303.
Wu H, Düdder B, Wang L, Sun S, Xue G (2022) Blockchain-based reliable and privacy-aware crowdsourcing with truth and fairness assurance. IEEE Internet Things J 9(5):3586–3598
304.
Bahutair M, Bouguettaya A, Neiat AG (2019) “Adaptive trust: usage-based trust in crowdsourced IoT services,” In: IEEE International Conference on Web Services (ICWS), Milan, Italy
305.
Bahutair M, Bouguettaya A, Neiat AG (2020) “Just-in-time memoryless trust for crowdsourced IoT services,” In: IEEE International Conference on Web Services (ICWS), Beijing, China
306.
Bahutair M, Bouguettaya A, Neiat AG (2022) Multi-perspective trust management framework for crowdsourced IoT services. IEEE Trans Serv Comput 15(4):2396–2409
307.
Liu K, Chen W, Zhang Z (2020) “Blockchain-empowered decentralized framework for secure and efficient software crowdsourcing,” In: IEEE World Congress on Services (SERVICES), Beijing, China
308.
Feng W, Yan Z, Yang LT, Zheng Q (2022) Anonymous authentication on trust in blockchain-based mobile crowdsourcing. IEEE Internet Things J 9(16):14185–14202
309.
Li C, Qu X, Guo Y (2021) TFCrowd: a blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness. EURASIP J Wirel Commun Netw 1:2021
310.
Watanabe K, Fukushi M, Horiguchi S (2010) Expected-credibility-based job scheduling for reliable volunteer computing. IEICE Trans Inf Syst 93(2):306–314
311.
Watanabe K, Fukushi M (2010) “Generalized spot-checking for sabotage-tolerance in volunteer computing systems,” In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, Melbourne, Australia
312.
Sarmenta LFG (2001) “Volunteer computing,” PhD Thesis, Massachusetts Institute of Technology
313.
Watanabe K, Fukushi M, Kameyama M (2011) Adaptive group-based job scheduling for high performance and reliable volunteer computing. J Inf Process 19:39–51
314.
Ahmed T, Bhouri M, Groulx D, White MA (2019) Passive thermal management of tablet PCs using phase change materials: intermittent operation. Appl Sci 9(5):902
315.
Wang C, Hua L, Yan H, Li B, Tu Y, Wang R (2020) A thermal management strategy for electronic devices based on moisture sorption-desorption processes. Joule 4(2):435–447
316.
Singh AK, Dey S, McDonald-Maier K, Basireddy KR, Merrett GV, Al-Hashimi BM (2020) Dynamic energy and thermal management of multi-core mobile platforms: a survey. IEEE Des Test 37(5):25–33
317.
Kim YG, Kim M, Kong J, Chung SW (2020) An adaptive thermal management framework for heterogeneous multi-core processors. IEEE Trans Comput 69(6):894–906
318.
Chetoui S, Reda S (2020) Coordinated self-tuning thermal management controller for mobile devices. IEEE Des Test 37(5):34–41
319.
Iranfar A, Terraneo F, Csordas G, Zapater M, Fornaciari W, Atienza D (2020) “Dynamic thermal management with proactive fan speed control through reinforcement learning,” In: Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France
320.
Park J, Lee S, Cha H (2018) “App-oriented thermal management of mobile devices,” In: International Symposium on Low Power Electronics and Design (ISLPED '18), Seattle, USA
321.
Feng X, Ren D, He X, Ouyang M (2020) Mitigating thermal runaway of lithium-ion batteries. Joule 4(4):743–770
322.
Abinav K, Rajeshwar PP, Punnoose JS, Daniel J, Sreekanth M (2017) Heat transfer enhancement in a smart phone. Int J Eng Res Appl 7(4):12–23
323.
Perreault LL, Colò F, Meligrana G, Kim K, Fiorilli S, Federico B, Jijeesh RN, Chiara V-B, Justyna F, Freddy K, Claudio G (2018) Spray-dried mesoporous mixed Cu-Ni Oxide@Graphene nanocomposite microspheres for high power and durable Li-ion battery anodes. Adv Energy Mater 8(35):1802438
324.
Xing W (2018) High energy/power density, safe lithium battery with nonflammable electrolyte. ECS Trans 85(13):109–114MathSciNet
325.
Mainar AR, Colmenares LC, Grande H-J, Blázquez JA (2018) Enhancing the cycle life of a zinc–air battery by means of electrolyte additives and zinc surface protection. Batteries 4(3):46
326.
Efrén FG, Espinosa-Medina G, Ramón DDLZ, Rosa-Zapata ADDl, González-Fernández JV (2019) “Analysis and design of a simple wireless charger for mobile phones,” In: IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico
327.
Lu X, Wang P, Niyato D, Kim DI, Han Z (2016) Wireless charging technologies: fundamentals, standards, and network applications. IEEE Commun Surv Tutor 18(2):1413–1452
328.
Saraereh OA, Alsaraira A, Khan I, Choi BJ (2020) A hybrid energy harvesting design for on-body Internet-of-Things (IoT) networks. Sensors 20(2):407
329.
Fan X, Chen J, Yang J, Bai P, Li Z, Wang ZL (2015) Ultrathin, rollable, paper-based triboelectric nanogenerator for acoustic energy harvesting and self-powered sound recording. ACS Nano 9(4):4236–4243
330.
Jain N, Fan X, Leon-Salas WD, Lucietto AM (2018) “Extending battery life of smartphones by overcoming idle power consumption using ambient light energy harvesting,” In: IEEE International Conference on Industrial Technology (ICIT), Lyon, France
331.
Zhu X, Li Y, Fang L, Chen P (2020) An improved proof-of-trust consensus algorithm for credible crowdsourcing blockchain services. IEEE Access 8:102177–102187
332.
Asghari M (2018) “Dynamic pricing and task assignment in real-time spatial crowdsourcing platforms,” PhD Thesis, University of Southern California
333.
Tong Y, Wang L, Zhou Z, Chen L, Du B, Ye J (2018) “Dynamic pricing in spatial crowdsourcing: a matching-based approach,” In: International Conference on Management of Data (SIGMOD '18), Houston, USA
334.
Bulut E, Hernandez S, Dhungana A, Szymanski BK (2018) “Is crowdcharging possible?,” In: 27th International Conference on Computer Communication and Networks (ICCCN), Hangzhou, China
335.
Wang H, Nguyen DN, Hoang DT, Dutkiewicz E, Cheng Q (2018) “Real-time crowdsourcing incentive for radio environment maps: a dynamic pricing approach,” In: IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE
Metadata
Title
Mobile crowd computing: potential, architecture, requirements, challenges, and applications
Authors
Pijush Kanti Dutta Pramanik
Saurabh Pal
Prasenjit Choudhury
Publication date
29-07-2023
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 2/2024
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-023-05545-0

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