CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things

The Internet of Things (IoT) aims at interconnecting everyday objects (including both things and users) and then using this connection information to provide customized user services. However, IoT does not work in its initial stages without adequate acquisition of user preferences. This is caused by cold-start problem that is a situation where only few users are interconnected. To this end, we propose CRUC scheme - Cold-start Recommendations Using Collaborative Filtering in IoT, involving formulation, filtering and prediction steps. Extensive experiments over real cases and simulation have been performed to evaluate the performance of CRUC scheme. Experimental results show that CRUC efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy Procedia, http://www.elsevier.com/locate/procedia/, 201

Internet of Things data contextualisation for scalable information processing, security, and privacy

The Internet of Things (IoT) interconnects billions of sensors and other devices (i.e., things) via the internet, enabling novel services and products that are becoming increasingly important for industry, government, education and society in general. It is estimated that by 2025, the number of IoT devices will exceed 50 billion, which is seven times the estimated human population at that time. With such a tremendous increase in the number of IoT devices, the data they generate is also increasing exponentially and needs to be analysed and secured more efficiently. This gives rise to what is appearing to be the most significant challenge for the IoT: Novel, scalable solutions are required to analyse and secure the extraordinary amount of data generated by tens of billions of IoT devices. Currently, no solutions exist in the literature that provide scalable and secure IoT scale data processing. In this thesis, a novel scalable approach is proposed for processing and securing IoT scale data, which we refer to as contextualisation. The contextualisation solution aims to exclude irrelevant IoT data from processing and address data analysis and security considerations via the use of contextual information. More specifically, contextualisation can effectively reduce the volume, velocity and variety of data that needs to be processed and secured in IoT applications. This contextualisation-based data reduction can subsequently provide IoT applications with the scalability needed for IoT scale knowledge extraction and information security. IoT scale applications, such as smart parking or smart healthcare systems, can benefit from the proposed method, which  improves the scalability of data processing as well as the security and privacy of data.   The main contributions of this thesis are: 1) An introduction to context and contextualisation for IoT applications; 2) a contextualisation methodology for IoT-based applications that is modelled around observation, orientation, decision and action loops; 3) a collection of contextualisation techniques and a corresponding software platform for IoT data processing (referred to as contextualisation-as-a-service or ConTaaS) that enables highly scalable data analysis, security and privacy solutions; and 4) an evaluation of ConTaaS in several IoT applications to demonstrate that our contextualisation techniques permit data analysis, security and privacy solutions to remain linear, even in situations where the number of IoT data points increases exponentially

Optimising data placement and traffic routing for energy saving in Backbone Networks

The energy consumption of network elements has become a big concern due to the exponential traffic growth and the rapid expansion of communication infrastructures. To deal with this problem, we propose a new approach called Backbone network Energy Saving based on Traffic engineering (BEST), which reduces the power consumption of network elements at the backbone level through jointly optimising data placement and traffic routing. Based on analysis on traffic characteristics, BEST firstly optimises the placement of data services such that the pairwise traffic demands can be better coordinated with the pairwise network costs, in order to minimise the traffic burden imposed on the network elements. Then, BEST optimises the routing of traffic flows and tries to find the minimum-power network subset that must remain active to fulfill the traffic requirements. Efficient heuristics are given by BEST to find an admissible solution when the problem size is very large. The simulation results illustrate the efficacy and efficiency of BEST in energy conservation on backbone networksThis work was supported in part by the National Science Foundation of China (grant nos. 61202430, 61303245 and 61103185), the Science and Technology Foundation of Beijing Jiaotong University (grant no. 2012RC040).Fang, W.; Wang, Z.; Lloret, J.; Zhang, D.; Yang, Z. (2014). Optimising data placement and traffic routing for energy saving in Backbone Networks. Transactions on Emerging Telecommunications Technologies. 25(9):914-925. https://doi.org/10.1002/ett.2774S914925259Zhang MG Yi C Liu B Zhang BC GreenTE: power-aware traffic engineering Proceedings of the 18 th IEEE International Conference on Network Protocols (ICNP 2010) 2010 21 30Cianfrani, A., Eramo, V., Listanti, M., Polverini, M., & Vasilakos, A. V. (2012). An OSPF-Integrated Routing Strategy for QoS-Aware Energy Saving in IP Backbone Networks. 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