Preferences help
enabled [disable] Abstract
Number of results
2016 | 41 | 1-304
Article title

UGC Sponsored Two Day National Conference on Internet of Things 18th and 19th February 2016

Title variants
Languages of publication
The main objectives of social internet of things is to separate the two levels of people and things to allow objects to have their own socail network, to allow humans to impose rules to protect their privacy and only access the result of autonomous inter- object interactions occouring on the object’s social network. Smart object will not make a difference, but social objects will make it. The main contributions is to identify the apporiate policies for the establishment and the management of social relationsship between objects.To describe a possible architecure for the internet of things that includes the functionalities to intergrate things into socail network.
Physical description
  • Division of Surface Electrochemistry and Engineering, Koszalin University of Technology, Racławicka 15-17, PL 75-620 Koszalin, Poland,
  • [1] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters, ”ACM Commun. Vol. 51, no. 1, pp. 107-113, 2008.
  • [2] M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica, “Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters,” in Proc. USENIX Conf. Hot Topics Cloud Comput. 2012, p. 10.
  • [3] L. Neumeyer, B. Robbins, A. Nair, and A. Kesari, “S4: Distributed stream computing platform,” in Proc. IEEE Int. Conf. Data Min. Workshops, 2010, pp. 170-177.
  • [4] B. Satzger, W. Hummer, P. Leitner, and S. Dustdar, “Esc: Towards an elastic stream computing platform for the cloud,” in Proc. IEEE Int. Conf. Cloud Comput., Jul. 2011, pp. 348-355.
  • [5] M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen, and R. F. Freund, “Dynamic mapping of a class of independent tasks onto heterogeneous computing systems,” in J. Parallel Distrib. Comput., Vol. 59, no. 2, pp. 107-131, 1999.
  • [6] Z. Wu, X. Liu, Z. Ni, D. Yuan, and Y. Yang, “A market-oriented hierarchical scheduling strategy in cloud workflow systems,” J. Supercomput., Vol. 63, no. 1, pp. 256-293, 2013.
  • [7] F. Zamfirache, M. Frincu, and D. Zaharie, “Population-based metaheuristics for tasks scheduling in heterogeneous distributed systems,” in Proc. 7th Int. Conf. Numerical Methods Appl., 2011, Vol. 6046, pp. 321-328.
  • [8] R. Castro Fernandez, M. Migliavacca, E. Kalyvianaki, and P. Pietzuch,“Integrating scale out and fault tolerance in stream processing using operator state management,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2013, pp. 725-736.
  • [9] R. Tolosana-Calasanz, J. Angel Ba~nares, C. Pham, and O. Rana,“End-to-end qos on shared clouds for highly dynamic, large-scalesensing data streams,” in Proc. IEEE/ACM Int. Symp. Cluster, Cloud Grid Comput., 2012, pp. 904-911.
  • [10] Andres Quiroz, Hyunjoo Kim, Manish Parashar, Nathan Gnanasambandam, Naveen Sharma, “Towards Autonomic Workload Provisioning for Enterprise Grids and Clouds”, 10th IEEE/ACM International Conference on Grid Computing, 2009.
  • [11] Ye Hu, Johnny Wong, Gabriel Iszlai and Marin Litoiu, “Resource Provisioning for Cloud Computing”, Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research, Pages 101-111, 2009.
  • [12] Saeid Abrishami, Mahmoud Naghibzadeh, Dick H.J. Epema, “Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds”, Future Generation Computer Systems, Volume 29, Issue 1, January 2013, Pages 158-169.
  • [13] W. Dawoud, I. Takouna, and C. Meinel, ”Infrastructure as a Service Security: Challenges and Solutions,” in Proc the 7th International Conference on Informatics and Systems 2010 (INFOS’10), Cairo, March 2010, pp. 1-8.
  • [14] Ming Mao, Marty Humphrey, “Auto-Scaling To Minimize Cost And Meet Application Deadlines In Cloud Workflows”, Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, 2011.
  • [15] K. Tsakalozos, H. Kllapi, E. Sitaridi, M. Roussopoulous, D. Paparas, and A. Delis, ”Flexible Use of Cloud Resources through Profit Maximization and Price Discrimination,” in Proc of the 27th IEEE International Conference on Data Engineering (ICDE 2011), April 2011, pp.75-86.
  • [16] R. Jeyarani, N. Nagaveni, R. Vasanth Ramc, ”Design and implementation of adaptive power-aware virtual machine provisioner (APA- VMP) using swarm intelligence”, Journal of Future Generation Computer Systems, Volume 28, Issue 5, May 2012, pp. 811-821, DOI:/10.1016/j.future.2011.06.002.
  • [17] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., 1995, Vol. 4, pp. 1942–1948.
  • [18] I. De Falco, R. Del Balio, E. Tarantino, and R. Vaccaro, Improving search by incorporating evolution principles in parallel tabu search, in 1994 IEEE Conference on Evolutionary Computation, Vol. 2, pp. 823-828, 1994.
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.