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Heterogeneous Sensor Data Integration for Crowdsensing Applications
Abstract
This paper describes a conceptual solution for heterogeneous sensor data integration in crowdsensing applications and one experimental implementation for a health monitoring system in an educational environment using low cost hardware solution. Three kinds of protocols are integrated in this solution: HL7 for medical data, Observations & Measurements Model for environmental data and BACnet for buildings monitoring. This last protocol has the particularity that manages sensoring and acting. A Common Data Model is described for the integration of three kinds of data and protocols, and a validation test application is described.
... A solution for heterogeneous sensor data integration in crowdsensing applications applied to health monitoring in educational environments using low cost hardware has been proposed in [101]. To track common respiratory diseases among members of the educational community, an application has been developed to collect data from occupants of several educational buildings. ...
... Based on the GeoDADIS architecture, the data platform architecture depicted in Fig. 3.6 has been implemented in [101] to enable the integrated collection of above data. Similarly to GeoDADIS, the Adapter design pattern has been adopted to access specific interfaces of data acquisition channels and data services in a uniform manner, and the Observer design pattern has been used to enable publish/subscribe communication with the Analysis Application developed in [101]. ...
... Based on the GeoDADIS architecture, the data platform architecture depicted in Fig. 3.6 has been implemented in [101] to enable the integrated collection of above data. Similarly to GeoDADIS, the Adapter design pattern has been adopted to access specific interfaces of data acquisition channels and data services in a uniform manner, and the Observer design pattern has been used to enable publish/subscribe communication with the Analysis Application developed in [101]. Unlike GeoDADIS, the implemented platform does not provide An observation data analysis system enables the analysis of observation data through the proposed set of operations. ...
A myriad of data acquisition devices is observing every day more variables and generating a vast amount of data in almost every application domain. Environmental observation data is an essential portion of such generated data, whose spatio-temporal nature has posed interesting challenges in the area of Environmental Observation Data Management Systems. Two features are common to all these systems: spatio-temporal observations and heterogeneity. In the context of this Thesis, the Observations and Measurements (O&M) conceptual schema was adopted as the theoretical framework for the definition of the concept of observation. Heterogeneity specifically concerns the data acquisition part of the aforementioned systems, which need to access data produced by heterogeneous sensing following different software/hardware specifications that are accessed through several communication protocols. A major challenge is to provide the required flexibility to enable data acquisition from heterogeneous sensing devices and data dissemination through heterogeneous end-user applications. The system must provide simple and straightforward mechanisms for the incorporation of the following components: 1) new in-situ sensing devices, 2) new data dissemination services, and 3) different persistent data storage technologies. Focusing on observation data management, a system must provide the following general functionalities to effectively manage observation data: 1) management of conventional Entity/Relationship data related to non-observed properties of entities, 2) management of sampled data over temporal, spatial (1D and 2D) and spatio-temporal domains, 3) Support for observation data semantics, and 4) efficient implementation for large scale shared-nothing distributed hardware architectures. Moreover, the INSPIRE Directive of the European Union encourages the creation of a Spatial Data Infrastructure (SDI) to ensure the interoperability of spatial information systems in Europe. The application of INSPIRE in the Spanish legislative system forces public administrations to make their geographic data available through SDI services. Therefore, the new enriched geographical knowledge allows for the appearance of many applications in different areas of knowledge that require spatial analysis capabilities.Therefore, the main objective of this Thesis is the design and implementation of a generic framework for spatio-temporal observation data acquisition and declarative analytical processing. This overall goal can be divided into three independent specific objectives:
- Design and implementation of a generic observation data acquisition and dissemination server.
- Design of a framework for declarative spatio-temporal analysis in very large spatio-temporal data warehouses.
- Efficient implementation of spatio-temporal on-line analytical processing in large scale distributed shared-nothing hardware architectures.
The main contributions of this Thesis may be summarized as follows:
- Generalization of a data acquisition and dissemination server, with great applicability in many scientific and industrial domains, providing flexibility in the incorporation of different technologies for data acquisition, data persistence and data dissemination.
- Definition of a new hybrid logical-functional paradigm to formalize a novel data model for the integrated management of entity and sampled data.
- Definition of a novel spatio-temporal declarative data analysis language for the previous data model.
... It is very typical to recognize the type and integration of such data. In [10] proposed model for blending of multisource data, like A Data Model for Integrating Heterogeneous, we referred Medical Data in the Health-e-Child Project data model for fusing data as a data model. a conceptual solution for heterogeneous sensor data integration [10] in crowd sensing applications is presented to combine different kinds of protocols for different types of data. ...
... In [10] proposed model for blending of multisource data, like A Data Model for Integrating Heterogeneous, we referred Medical Data in the Health-e-Child Project data model for fusing data as a data model. a conceptual solution for heterogeneous sensor data integration [10] in crowd sensing applications is presented to combine different kinds of protocols for different types of data. Three kinds of protocols such as HL7 for medical data, BACnet for building monitoring, and Observation and Measurement model for environmental data, are integrated into one data model to manage sensors and actions. ...
... Dao et al. [18] proposed a complex event model (EM) and constructed an information process platform to collect and analyze heterogeneous sensor data flow, so as to provide the support for the application of information fusion system. A conceptual model [19] that oriented the crowdsensing applications provided an integration model for heterogeneous data on protocol layer. It integrated the medical data, observations and measurements model, and environmental data into a data model (DM) to manage multiple sensor data and actions effectively. ...
With the transformation of Internet of Things to Web of Things (WoT), a variety of applications are required to deal with huge volumes of real-time and heterogeneous data. However, in most applications, due to the weak semantics of data itself and the loose combination with specific scenarios, it is sometimes difficult to depict the spatiotemporal feature of the scenario in an application only through the data. In this paper, a spatiotemporal semantic scenario meta-model-based configurable platform is proposed for the development of WoT applications to address this issue, based on the data configuration, event stream configuration, and service encapsulation, the entire WoT scenario can be depicted with an abstract data model and related rules, and the business process can be changed by redefining corresponding rules when requirements change. A case study is given to verify the feasibility of our platform. The result shows that the platform can provide background support for WoT applications in a promising way. IEEE
With the enrichment of types and numbers of sensing terminals, more and more devices such as mobile phones, smart wearables, mobile robots, drones have appeared in our life, enabling the development of Mobile Crowd Sensing (MCS) technology. MCS systems has gradually changed from isomorphic sensing to heterogeneous collaborative sensing, and finally evolved into a heterogeneous multi-source sensing mode of the fusion of humans, machines and objects (things). However, state-of-the-art systems/frameworks do not well support efficient interactions and Heterogeneous Crowd Agents (HCA) management in Heterogeneous MCS (H-MCS) systems. With this in mind, this article aims at two major gaps: Efficient interaction and collaboration of HCA, automated modeling and flexible management of HCA. To deal with the challenges, we design an ontology-based interaction and management middleware (CrowdManager). Three core modules that constitute the middleware: HCA Information Extraction and Representation (HER), Ontology-based HCA Construction & Management (OCM), and Communication and Interaction Module (CIM) are well demonstrated. Extensive comparative evalution suggests that our approach not only brings rich and efficient HCA management and interactive functions to H-MCS systems, but also reduces communication time and various resource occupancy rates by more than 50%.
Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework.
Ubiquitous home healthcare systems have been playing an increasingly significant role in the treatment and management of chronic diseases, such as diabetes and hypertension, but progress has been hampered by the lack of standardization in the exchange of medical health care information. In an effort to establish standardization, this paper proposes a home healthcare monitoring system data exchange scheme between the HL7 standard and the IEEE1451 standard. IEEE1451 is a standard for special sensor networks, such as industrial control and smart homes, and defines a suite of interfaces that communicate among heterogeneous networks. HL7 is the standard for medical information exchange among medical organizations and medical personnel. While it provides a flexible data exchange in health care domains, it does not provide for data exchange with sensors. Thus, it is necessary to develop a data exchange schema to convert data between the HL7 and the IEEE1451 standard. This paper proposes a schema that can exchange data between HL7 devices and the monitoring device, and conforms to the IEEE 1451 standard. The experimental results and conclusions of this approach are presented and show the feasibility of the proposed exchange schema.
In this paper we focus on mobile crowdsensing applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe and measure phenomena over a larger geographic area. Such applications, e.g., environmental monitoring or crowdsourced traffic monitoring, involve numerous individuals that on the one hand continuously contribute sensed data to application servers, and on the other hand consume the information of interest to observe a phenomenon typically in their close vicinity. Energy-efficient and context-aware orchestration of the sensing process with data transmission from sensors through mobile devices into the cloud, as well as from the cloud to mobile devices such that information of interest is served to users in real-time, is essential for such applications, primarily due to battery limitations of both mobile devices and wearable sensors. In addition, the latency of data propagation represents their key quality measure from the user's perspective. Publish/subscribe middleware offers the mechanisms to deal with those challenges: It enables selective real-time acquisition and filtering of sensor data on mobile devices, efficient continuous processing of large data volumes within the cloud, and near real-time delivery of notifications to mobile devices. This paper presents our implementation of a publish/subscribe middleware system which is tailored to the requirements of mobile and resource-constrained environments with a goal to reduce the overall energy consumption in such environments, and proposes a general architecture for mobile crowdsensing applications. We demonstrate the usability of both the architecture and middleware through our application for air quality monitoring, and discuss the energy footprint of the proposed solution.
The homogeneous access to sensor data in data monitoring and analysis applications is gaining much interest nowadays. To tackle this problem from an application independent perspective, the design and implementation of a framework called GeoDADIS for the development of data acquisition and dissemination servers is discussed in the present paper. Those servers are of common use in monitoring applications as they perform as gateways between decision support and data visualization technologies used in application developments and the heterogeneous collection of protocols and interfaces available in the industrial area for sensor data access. To achieve its objective, the architecture of GeoDADIS consists of: (i) a bottommost data acquisition layer that communicates with sensors, (ii) a middle kernel layer that provides general purpose functionality related to data management and system control and (iii) a topmost external interaction layer that enables the access from applications. The frameworks design does extensive use of the adapter (wrapper) design pattern to ease the incorporation of new data acquisition channels at the data acquisition layer and new data and remote control services in the external interaction layer. This makes GeoDADIS a very flexible and general purpose tool with broad application in many data monitoring domains.
The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts
across many areas of modern day living. This offers the ability to measure,
infer and understand environmental indicators, from delicate ecologies and
natural resources to urban environments. The proliferation of these devices in
a communicating-actuating network creates the Internet of Things (IoT),
wherein, sensors and actuators blend seamlessly with the environment around us,
and the information is shared across platforms in order to develop a common
operating picture (COP). Fuelled by the recent adaptation of a variety of
enabling device technologies such as RFID tags and readers, near field
communication (NFC) devices and embedded sensor and actuator nodes, the IoT has
stepped out of its infancy and is the the next revolutionary technology in
transforming the Internet into a fully integrated Future Internet. As we move
from www (static pages web) to web2 (social networking web) to web3 (ubiquitous
computing web), the need for data-on-demand using sophisticated intuitive
queries increases significantly. This paper presents a cloud centric vision for
worldwide implementation of Internet of Things. The key enabling technologies
and application domains that are likely to drive IoT research in the near
future are discussed. A cloud implementation using Aneka, which is based on
interaction of private and public clouds is presented. We conclude our IoT
vision by expanding on the need for convergence of WSN, the Internet and
distributed computing directed at technological research community.
The book is an introduction to the idea of design patterns in software engineering, and a catalog of twenty-three common patterns. The nice thing is, most experienced OOP designers will find out they've known about patterns all along. It's just that they've never considered them as such, or tried to centralize the idea behind a given pattern so that it will be easily reusable.