Xhafa F (2020) The vision of edges of internet as a compute fabric. Chapter 1. In: Xhafa F, Sangaiah AK (eds) Advances in edge computing: massive parallel processing and applications. Book series: advances in parallel computing series. IOS Press, Amsterdam
Ahmad A, Lavin A, Purdy S, Agha Z (2017) Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262:134–147
Carletti M, Masiero Ch, Beghi A, Susto GA (2019) A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study. Procedia Manuf 38:233–240
[+]
Xhafa F (2020) The vision of edges of internet as a compute fabric. Chapter 1. In: Xhafa F, Sangaiah AK (eds) Advances in edge computing: massive parallel processing and applications. Book series: advances in parallel computing series. IOS Press, Amsterdam
Ahmad A, Lavin A, Purdy S, Agha Z (2017) Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262:134–147
Carletti M, Masiero Ch, Beghi A, Susto GA (2019) A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study. Procedia Manuf 38:233–240
Corizzo R, Ceci M, Japkowicz N (2019) Anomaly detection and repair for accurate predictions in geo-distributed big data. Big Data Res 16:18–35
Xhafa F, Kilic B, Krause P (2020) Evaluation of IoT stream processing at edge computing layer for semantic data enrichment. Future Gener Comput Syst 105:730–736. https://doi.org/10.1016/j.future.2019.12.031
Onggo BS, Mustafee N, Smart A, Juan AA, Molloy O (2018) Symbiotic simulation system: hybrid systems model meets big data analytics. In: Proceedings of the 2018 winter simulation conference (WSC ’18). IEEE Press, pp 1358–1369
Hawley-Hague H, Boulton E, Hall A, Pfeiffer K, Todd Ch (2014) Older adults’ perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review. Int J Med Inform 83(6):416–426
Wang X, Ahn SH (2020) Real-time prediction and anomaly detection of electrical load in a residential community. Appl Energy 259:114145
Faulin J, Grasman SE, Juan A, Patrick Hirsch P (2019) Sustainable transportation and smart logistics. Decision-making models and solutions. Elsevier, Amsterdam
Hussain M, Alam MS, Beg S (2020) Vehicular fog computing-planning and design. Procedia Comput Sci 167:2570–2580
Zhang H, Lu X (2020) Vehicle communication network in intelligent transportation system based on Internet of Things. Special issue on internet of things and augmented reality in the age of 5G. Comput Commun 160:779–789
Fouchal H, Bourdy E, Wilhelm G, Ayaida M (2017) A validation tool for cooperative intelligent transport systems. J Comput Sci 22:283–288
Mfenjou ML, Abba Ari A, Njoya AN, Fots DJ, Kolyang M, Abdou W, Spies F (2019) Control points deployment in an intelligent transportation system for monitoring inter-urban network roadway. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.10.005 (in Press)
Santa J, Bernal-Escobedo L, Sanchez-Iborra R (2020) On-board unit to connect personal mobility vehicles to the IoT. FNC/MobiSPC, pp 173–180
Ferreira J, Alam M, Fernandes B, Silva L, Almeida J, Moura L, Costa R, Iovino G, Cordiviola E (2018) Cooperative sensing for improved traffic efficiency: the highway field trial. Comput Netw 143:82–97
Oracle Linux, Linux Containers (2019). https://docs.oracle.com/en/operating-systems/oracle-linux/6/adminsg/ol_about_containers.html. Accessed 20 Oct 2020
Gago MCF, Moyano F, López J (2017) Modelling trust dynamics in the internet of things. Inf Sci 396:72–82
Ferraris D, Gago M CF, López J (2018) A trust-by-design framework for the internet of things. In: 9th IFIP international conference on new technologies, mobility and security, NTMS 2018, Paris, France, 2018, pp 1–4
Sánchez L, Muñoz L, Galache JA, Sotres P, Santana JR, Gutiérrez V, Ramdhany R, Gluhak A, Krco S, Theodoridis E, Pfisterer D (2014) Smartsantander: Iot experimentation over a smart city testbed. Comput Netw 61:217–238
Santa J, Fernández PJ, Sanchez-Iborra R, Murillo JO, Skarmeta AF (2018) Offloading positioning onto network edge. Wirel Commun Mob Comput 2018:7868796:1–7868796:13
Wang X, Wu W, Qi D (2018) Mobility-aware participant recruitment for vehicle-based mobile crowdsensing. IEEE Trans Veh Technol 67(5):4415–4426
He Z, Cao J, Liu X (2015) High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility. In 2015 IEEE conference on computer communications, INFOCOM 2015, Hong Kong, 2015, pp 2542–2550
Shinkuma R, Takagi R, Inagaki Y, Oki E, Xhafa F (2020) Incentive mechanism for mobile crowdsensing in spatial information prediction using machine learning. In: AINA, pp 792–803
Coffman EG Jr, Csirik J, Galambos G, Martello S, Vigo D (2013) Bin packing approximation algorithms: survey and classification. In: Pardalos P, Du DZ, Graham R (eds) Handbook of combinatorial optimization. Springer, New York. https://doi.org/10.1007/978-1-4419-7997-1_35
Yesodha R, Amudha T (2012) A comparative study on heuristic procedures to solve bin packing problems. Int J Found Comput Sci Technol (IJFCST) 2:37–49. https://doi.org/10.5121/ijfcst.2012.2603
Makhorin A. GLPK (GNU Linear Programming Kit). http://www.gnu.org/software/glpk/glpk.html. Accessed 20 Oct 2020
COIN-OR (Common Infrastructure for Operations Research). http://www.coin-or.org. Accessed 20 Oct 2020
Ceselli A, Fiore M, Premoli M, Secci S (2019) Optimized assignment patterns in mobile edge cloud networks. Comput Oper Res 106:246–259
Gurobi Optimization, LLC, Gurobi optimizer reference manual (2020). http://www.gurobi.com. Accessed 20 Oct 2020
[-]