Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping

Crowdsourced data can effectively observe environmental and urban ecosystem processes. The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems (EWS) to better perform while support decision-making to reduce the fatalities and economic losses due to inundation hazard. In this work, we develop a Data Assimilation (DA) method integrating Volunteered Geographic Information (VGI) and a 2D hydraulic model and we test its performances. The proposed framework seeks to extend the capabilities and performances of standard DA works, based on the use of traditional in situ sensors, by assimilating VGI while managing and taking into account the uncertainties related to the quality, and the location and timing of the entire set of observational data. The November 2012 flood in the Italian Tiber River basin was selected as the case study. Results show improvements of the model in terms of uncertainty with a significant persistence of the model updating after the integration of the VGI, even in the case of use of few-selected observations gathered from social media. This will encourage further research in the use of VGI for EWS considering the exponential increase of quality and quantity of smartphone and social media user worldwide

iTREE: Intelligent Traffic and Resource Elastic Energy scheme for Cloud-RAN

YesBy 2020, next generation (5G) cellular networks are expected to support a 1000 fold traffic increase. To meet such traffic demands, Base Station (BS) densification through small cells are deployed. However, BSs are costly and consume over half of the cellular network energy. Meanwhile, Cloud Radio Access Networks (C-RAN) has been proposed as an energy efficient architecture that leverage cloud computing technology where baseband processing is performed in the cloud. With such an arrangement, more energy gains can be acquired through statistical multiplexing by reducing the number of BBUs used. This paper proposes a green Intelligent Traffic and Resource Elastic Energy (iTREE) scheme for C-RAN. In iTREE, BBUs are reduced by matching the right amount of baseband processing with traffic load. This is a bin packing problem where items (BS aggregate traffic) are to be packed into bins (BBUs) such that the number of bins used are minimized. Idle BBUs can then be switched off to save energy. Simulation results show that iTREE can reduce BBUs by up to 97% during off peak and 66% at peak times with RAN power reductions of up to 27% and 18% respectively compared with conventional deployments

IoT Architecture for a sustainable tourism application in a smart city environment

In the past few years, the Smart Cities concept has become one of the main driving forces for the urban transition towards a low carbon environment, sustainable economy, andmobility. Tourism, as one of the fastest growing industries, is also an important generator of carbon emissions; therefore, the recently emerging sustainable tourism concept is envisioned as an important part of the Smart Cities paradigm.Within this context, the Internet-of-Things (IoT) concept is the key technological point for the development of smart urban environments through the use of aggregated data, integrated in a single decisional platform. This paper performs the first analysis on the feasibility of the use of an IoT approach and proposes a specific architecture for a sustainable tourism application. The architecture is tailored for the optimisation of the movement of cruise ship tourists in the city of Cagliari (Italy), by taking into consideration factors such as transport information and queue waiting times. A first set of simulations is performed using 67-point of interest, real transportation data, and an optimisation algorithm

Aggregated sensor payload submission model for token-based access control in the Web of Things

YesWeb of Things (WoT) can be considered as a merger of newly emerging paradigms of Internet of Things (IoT) and cloud computing. Rapidly varying, highly volatile and heterogeneous data traffic is a characteristic of the WoT. Hence, the capture, processing, storage and exchange of huge volumes of data is a key requirement in this environment. The crucial resources in the WoT are the sensing devices and the sensing data. Consequently, access control mechanisms employed in this highly dynamic and demanding environment need to be enhanced so as to reduce the end-to-end latency for capturing and exchanging data pertaining to these underlying resources. While there are many previous studies comparing the advantages and disadvantages of access control mechanisms at the algorithm level, vary few of these provide any detailed comparison the performance of these access control mechanisms when used for different data handling procedures in the context of data capture, processing and storage. This study builds on previous work on token-based access control mechanisms and presents a comparison of two different approaches used for handling sensing devices and data in the WoT. It is shown that the aggregated data submission approach is around 700% more efficient than the serial payload submission procedure in reducing the round-trip response time

On participatory service provision at the network edge with community home gateways

Edge computing is considered as a technology to enable new types of services which operate at the network edge. There are important use cases in ambient intelligence and the Internet of Things (IoT) for edge computing driven by huge business potentials. Most of today's edge computing platforms, however, consist of proprietary gateways, which are either closed or fairly restricted to deploy any third-party services. In this paper we discuss a participatory edge computing system running on home gateways to serve as an open environment to deploy local services. We present first motivating use cases and review existing approaches and design considerations for the proposed system. Then we show our platform which materializes the principles of an open and participatory edge environment, to lower the entry barriers for service deployment at the network edge. By using containers, our platform can flexibly enable third-party services, and may serve as an infrastructure to support several application domains of ambient intelligence.Peer ReviewedPostprint (author's final draft

IoT-Enhanced Learning Environment Optimization and Student Outcome

This proposed system leverages Internet of Things (IoT) technology to enhance the learning environment in educational settings through two synergistic techniques. Firstly, a search-based optimization algorithm, driven by a genetic-based approach, is implemented for scheduling courses and faculty within each department to improve overall student performance and departmental percentages. Secondly, a classification task is performed to predict student outcomes, employing Neural Networks (NN) including ResNet 50, ResNet34, and a hybrid ResNet34 and ResNet50 model. The classification is based on eye-gaze monitoring during active student engagement in class, using input video samples as training and testing datasets. The system integrates optimization, activity monitoring, and classification to create a comprehensive approach aimed at improving the overall learning environment and student outcomes in educational institutions

Designing a Smart City Internet of Things Platform with Microservice Architecture

The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standard-ization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the microservice architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces

Distributed Processing in Cloud Computing

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.Cloud computing offers a wide range of resources and services through the Internet that can been used for various purposes. The rapid growth of cloud computing has exempted many companies and institutions from the burden of maintaining expensive hardware and software infrastructure. With characteristics like high scalability, availability and fault tolerance, cloud computing meet the new era needs for massive data processing at an affordable cost. In our doctoral research we intend to study, analyze, evaluate and make proposals in order to further improve the performance of cloud computing.European Cooperation in Science and Technology. COS

Predicting topology propagation messages in mobile ad hoc networks: The value of history

This research was funded by the Spanish Government under contracts TIN2016-77836-C2-1-R,TIN2016-77836-C2-2-R, and DPI2016-77415-R, and by the Generalitat de Catalunya as Consolidated ResearchGroups 2017-SGR-688 and 2017-SGR-990.The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Peer ReviewedPostprint (published version