Antonio Manuel Ortiz

The Cluster Between Internet of Things and Social Networks: Review and Research Challenges

The cluster between Internet of Things (IoT) and social networks (SNs) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this paper first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this paper proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.

Fuzzy-logic based routing for dense wireless sensor networks

The task of routing data from a source to the sink is a critical issue in ad hoc and wireless sensor networks. In this paper, the use of fuzzy logic to perform role assignment during route establishment and maintenance is proposed. An incremental approach is presented and compared with similar existing routing protocols. Efficient routing approaches provide network load balance to extend network lifetime, efficiency improvements, and data loss avoidance. Experiments show promising results for our proposals and its suitability for operating with dense networks, obtaining quick route creation as well as energy efficiency.

Intelligent route discovery for ZigBee mesh networks

ZigBee in conjunction with IEEE 802.15.4 is considered the de facto standard for WSNs. The routing protocol for ZigBee mesh networks is AODV, an on-demand routing protocol which has been extensively used. However, it presents critical problems when it is used in large dense networks, mainly related to the high number of messages necessary to perform routing tasks, which causes a very high overhead. The high number of packet collisions, data loss, and its implicit energy expenditure, makes AODV unsuitable for certain applications that require efficient, fast and reliable communications. This study proposes the use of a novel fuzzy-logic-based metric to be used in the decision making process of AODV. This metric evaluates important node features during route creation and selects, hop by hop, the best option in order to obtain energy and delay efficient routes. The experiments with the improved AODV, AODV-FL show a reduction in the communication delay, number of packets and overhead, improving route efficiency and reducing packet overload.

Intelligent role-based routing for dense wireless sensor networks

The task of routing data from a source to the sink is a critical issue in ad hoc and wireless sensor networks. In this paper, the use of fuzzy logic to perform role assignment during route establishment and maintenance is proposed. An intelligent role-based approach is presented and compared with similar existing routing protocols. Experiments show promising results for our proposal and its suitability for operating with dense networks, obtaining quick path creation and energy efficiency.

Locating in Crowdsourcing-Based DataSpace: Wireless Indoor Localization without Special Devices

Locating a target in an indoor social environment with a Mobile Network is important and difficult for location-based applications and services such as targeted advertisements, geosocial networking and emergency services. A number of radio-based solutions have been proposed. However, these solutions, more or less, require a special infrastructure or extensive pre-training of a site survey. Since people habitually carry their mobile devices with them, the opportunity using a large amount of crowd-sourced data on human behavior to design an indoor localization system is rapidly advancing. In this study, we first confirm the existence of crowd behavior and the fact that it can be recognized using location-based wireless mobility information. On this basis, we design “Locating in Crowdsourcing-based DataSpace” (LiCS) algorithm, which is based on sensing and analyzing wireless information. The process of LiCS is crowdsourcing-based. We implement the prototype system of LiCS. Experimental results show that LiCS achieves comparable location accuracy to previous approaches even without any special hardware.

Smart routing mechanism for green ZigBee-based wireless sensor networks

Recent advances in communication networks have spurred interest on the definition of efficient self-configuration protocols. Such mechanisms are particularly of interest on the rapid deployment of wireless sensor networks whose nodes are characterized by their low computational and energy resources according to the green networking principles. In this paper, we introduce an adaptable fuzzy-logic assisted configuration mechanism for ZigBee networks; one of the most promising standards in the area of wireless sensor networks. Our results show that the use of fuzzy-logic principles can significantly improve the operation of tree-based routing mechanisms by reducing the set-up network time and the number of router nodes in the network, thus allowing a reduction in the energy consumed by nodes.

WiseObserver: a real experience with wireless sensor networks

This Wireless Sensor Network Data Visualization Framework tries to be a tool for the visualization and analysis of data collected by a wireless sensor network in a generic scope of application. It also tries to establish a sensor network control interface. The tool will include several facilities to treat sensor network data. It allows ht e generation of evolution charts, interpolation maps, evolution data videos, and report generation. It also includes modules to add external data not collected by nodes, but related to the network conditions. Node management will be possible thanks to the execution of commands in network nodes, to perform changes in network operation. All these functionalities have been implemented taking into account a reasonable performance level. It also guarantees reliability making use of security restrictions. The tool will be easily maintainable and will allow the portability between different hardware platforms, both regarding to the platform in which it will be executed and network node hardware, allowing data management and data treatment of different platforms.

An experimental testbed for smart cities applications

The concept of Smart Cities has recently attracted considerable attention. The basic idea about making a city as smart is to use communication technology to optimize a citys resource consumption. There are interesting research opportunities oriented to innovative applications and services based on Future Internet. Networked interaction will be a priority where people will be interacting which each other as well as with information. On the other hand, Smart City research explores the space problem through applications pilots and experimental platforms. For this reason, we propose a new testbed to perform experiments oriented to Smart Cities communications, integrating body area networks with wireless sensor networks. We have improved our first implementation to obtain a platform to emulate real communication possibilities in Smart Cities. We describe the main components of our testbed and we show an experimental example confirming that we are ready to participate in the Smart Cities evolution.

Reality mining: A prediction algorithm for disease dynamics based on mobile big data

Abstract Predicting disease dynamics during an epidemic is an important aspect of e-Health applications. In such prediction, Realistic Contact Networks (RCNs) have been widely used to characterize disease dynamics. The structure of such networks is dynamically changed during an epidemic. Capturing such kind of dynamic structure is the basis of prediction. With the popularity of mobile devices, it is possible to capture the dynamic change of the network structure. On this basis, in this study, we evaluate the impact of the network structure on disease dynamics, by analyzing massive spatiotemporal data collected by mobile devices. These devices are carried by the volunteers of Ebola outbreak areas. Based on the results of this evaluation, a model is designed to recognize the dynamic structure of RCNs. On the basis of this model, we propose a prediction algorithm for disease dynamics. By extensive experiments, we show that our algorithm improves the accuracy of the disease prediction.

The EcoSense Project: An Intelligent Energy Management System with a Wireless Sensor and Actor Network

Wireless sensor networks provide a new way of working for applications such as indoor monitoring, security or robotics. The Ecosense project aims to monitor all devices consuming energy in an intelligent building. We are developing this project in steps. Firstly, we have deployed a network equipped only with sensors (for temperature, humidity, luminosity, and electrical consumption, as well as presence detectors). Secondly, we will add an upper layer of manager agents (actors) to communicate and negotiate services. Afterwards, we will obtain conclusions about the sensed data and we will then extend a full wireless sensor network to cover the whole building. The network prototype will also be used to test power-and-time efficient protocols developed by us.