The Case of Telecommunications Infrastructure
As the world becomes "smart", with "smart cities", "smart grids", "smart buildings", etc., it is also becoming increasingly reliant on its telecommunications infrastructure. To answer the needs of smart applications, the underlying infrastructure must be extremely reliable, sustainable and highly adaptable to the system's changing conditions. Current and forthcoming smart systems increasingly require telecommunication networks that are ubiquitous, all pervasive, highly performant and transparent to users. This smoothness in network operation requires a complex, large scale and highly heterogeneous telecommunications infrastructure. Wireless, optical, satellite, aerial, computing and storage technology must efficiently work together to provide the availability and the response time that is required by smart applications. Networks need to rearrange their resource offering in the nick of the time, and that is only possible with the increased use of virtualization to define network functions, alongside a form of centralized network intelligence based on the concept of Software Defined Networking (SDN). Thus, telecommunications infrastructure itself must be the smartest, for all the other smart systems relying on it to function properly in real time.
An example of such infrastructure is currently provided by the deployment of 5G in large cities that may set virtual networks, called "slices", to different smart systems, such as intelligent transportation, connected vehicles or telemedicine, each having applications with different real-time performance requirements. The challenge for operators is to ensure that those systems are always available, and that the response time provided by the infrastructure is suitable for each smart application. To help in this task, a citywide 4G/5G simulator has been developed at GERAD by Professor Brunilde Sansò's research group. The large-scale discrete event simulator recreates equipment specifications normalized by the telecommunications standards entities. It also uses real telecommunications and city infrastructure data to assess the response time of key applications and to detect problems in the network. One of the major challenges tackled by the research group is the large scale modelling of the system that may contain hundreds of base stations operating at the millisecond level in a citywide networking mode. Another challenge is the mapping of the citywide application into the simulated infrastructure. From the time-scale standpoint, the mapping implies assessing the application performance, in minutes or hours while simulating milliseconds, which involves advanced statistical and machine learning modelling. Another challenge arises from mobility applications, such as V2V or ITS that, in their path towards their destination, make use of different telecommunications equipment. Thus, the mapping needs advanced algorithms for distributed computations. Finally, machine learning methods are put in place to assess the performance of the applications and to identify network problems, such as dormant cells or denial of service attacks. An on-line version of the simulator can be accessed here.
A final thought on real-time decision-making and telecommunication infrastructure is that the "smartness", reliability and high availability of such infrastructure comes with a hidden price: increased energy consumption and environmental impact of data centres and network components. For years, Professor Sansò's group has been interested in ways of guaranteeing network reliability and availability while reducing energy consumption and environmental impact. Among others, the intelligent real-time operation of data centres, the design and operation of wireless access with energy constraints and the study of networks fed by solar energy. The next step currently being developed as an extension of the above-mentioned citywide simulator is how to integrate unreliable energy sources to insure real-time response of smart applications even in challenged and catastrophic conditions.
References:
Manzanilla-Salazar, O.G., Malandra, F., Mellah, H., Wette, C., Sansò, B., A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure. IEEE Access, 8, 61213-61225, 2020.
Seyedi, Y., Karimi, H., Wette, C., Sansò, B., A New Approach to Reliability Assessment and Improvement of Synchrophasor Communications in Smart Grids. IEEE Transactions on Smart Grid, 11(5), 4415-4426, 2020.
D'Amours, M., Girard, A., Sansò, B., Planning Solar in Energy-managed Cellular Networks. IEEE Access, 6, 65212-65226, 2018.
Malandra, F., Chiquette, L.O., Lafontaine-Bedard, L.P., Sansò, B., Traffic characterization and LTE performance analysis for M2M communications in smart cities. Pervasive and Mobile Computing, 48, 59-68, 2018.
Larumbe, F., Sansò, B., A Tabu Search Algorithm for the Location of Data Centers and Software Components in Green Cloud Computing Networks. IEEE Transactions of Cloud Computing, 1(1), 22-35, 2013.
Boiardi, S., Capone, A., Sansò, B., Radio planning of energy-aware cellular networks. Computer Networks, 57(13), 2564-2577, 2013.
