Juan Sancho

Parking Easier by Using Context Information of a Smart City: Enabling Fast Search and Management of Parking Resources

In the great majority of cities it is difficult and hardly expensive to create more parking spaces for vehicles since they have almost reached its full occupancy. Combining this problem with an inefficient use of parking spaces leads to congestions due to aggregation of parking seekers and regular drivers. Recent advances in low-cost, low-power embedded systems bring the opportunity to develop new applications to solve these problems. In particular, Smart Cities greatly enrich their sustainability by introducing new resource management applications that rely in those constrained devices a significant part of the functionality of the system. The proposed Smart Parking solution consists mainly in the on-site deployment of an IoT solution to monitor and signalize the state of availability of each single parking space, as well as using context information generated by the city and its citizens to provide accurate responses to drivers demands. Furthermore, this system improves the management of parking resources by public authorities, for instance handling groups of parking spaces facilitating the whole city traffic management. The integration of this deployment into an existing live test-bed implies an easy task requiring just the data collection through the available means of the parking spaces availability. At the present time there exist living test-beds which can be used to integrate these new functionalities for experimentation on IoT data level, to gain a better knowledge and understanding of the M2M world, reducing costs, resources, pollution and time.

Context-aware stream processing for distributed IoT applications

Most of the IoT applications are distributed in nature generating large data streams which have to be analyzed in near real-time. Solutions based on Complex Event Processing (CEP) have the potential to extract high-level knowledge from these data streams but the use of CEP for distributed IoT applications is still in early phase and involves many drawbacks. The manual setting of rules for CEP is one of the major drawback. These rules are based on threshold values and currently there are no automatic methods to find the optimized threshold values. In real-time dynamic IoT environments, the context of the application is always changing and the performance of current CEP solutions are not reliable for such scenarios. In this regard, we propose an automatic and context aware method based on clustering for finding optimized threshold values for CEP rules. We have developed a lightweight CEP called μCEP to run on low processing hardware which can update the rules on the run. We have demonstrated our approach using a real-world use case of Intelligent Transportation System (ITS) to detect congestion in near real-time.

An integrated information lifecycle management framework for exploiting social network data to identify dynamic large crowd concentration events in smart cities applications

Abstract With the current availability of an extreme diversity of data sources and services, emerging from the Internet of Things and Cloud domains, the challenge is shifted towards identifying intelligent, abstracted and adaptive ways of correlating and combining the various levels of information. The purpose of this work is to demonstrate such a combination, on one hand at the service level, through integrating smart cities platforms for user level data, and on the other hand at Complex Event Processing, Storage and Analytics capabilities together with Twitter data. The final goal is to identify events of interest to the user such as Large Crowd Concentration (LCC) in a given area, in order to enrich application level information with related event identification that can enable more sophisticated actions on behalf of that user. The identification is based on observation of Twitter activity peaks compared to historical data on a dynamic time and location of interest. The approach is validated through a two-month experiment in the city of Madrid, identifying LCCs in sporting events around two sports venues and analyzing various approaches with relation to the needed thresholds definition.

Wireless sensor network simulation for security and performance analysis

During the last years, Wireless Sensor Networks (WSN) have been deployed at an accelerated rate. The complexity and low-power requirements of these networks have also been growing. Therefore, WSN developers are beginning to require efficient methodologies for network simulation and embedded SW performance analysis. These tools should also include security analysis. This security analysis has to evaluate the vulnerability of a WSN to the wide variety of attacks that these networks could suffer. WSN attacks could also affect power consumption and performance of the nodes software, thus security analysis has to be integrated into a complete performance analysis framework. This work proposes a methodology to simulate the most common and dangerous attacks that a WSN can suffer nowadays. The impact of these attacks on power consumption and software execution time are also analyzed. This provides developers with important information about the effects that one or multiple attacks could have on the WSN, helping them to develop more secure software.

Low Power Wireless Sensor Networks: Secure Applications and Remote Distribution of FW Updates with Key Management on WSN

Nearly 70 % of the average household utility bill could be influenced by Wireless Sensor Network (WSN) application to temperature and lighting. Home and building owners can use interactive energy management tools to create energy management profiles that are triggered by certain established consumption rates. However, security remains a critical issue. In this chapter, we will present how the new security features (both on hardware and software level) introduces improvements in the overall WSN picture, and we will explain with detail the new OTAP procedure. Furthermore, we will state all the new possibilities opened for Low Power WSN based on the enhancement of security, being a good candidate for applications restricted to much more powerful devices.

Real time energy efficiency optimization in connected electrical vehicles

Nowadays vehicles have evolved from a mean of transport to something much more complex and powerful. The paradigm change that electric vehicles have brought requires new tools and methodologies for improving not only the way vehicles behave, but also all the elements that are influenced by them. Moreover, electric vehicle are also connected to the Internet, thus they are able to access to a large repository of information and also to contribute to emerging opportunities that should be exploited. The application of Internet of Things and Big Data technologies in electric vehicles enables the development of new methodologies for offering personalised energy efficiency advices. JOSPEL aims at exploiting all these possibilities making them tangible through an energy consumption reduction reached by means of the provision of different services and improving the physical elements in the vehicle. This paper presents the ICT architecture that enables the achievement of those objectives by the digitalisation and analysis of vehicle information, and also to expose existing vehicle resources for achieving other benefits in different sectors.