The Internet of Things

Foundation for Smart Cities, eHealth, and Ubiquitous Computing

Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka, Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka

• Publisher: Chapman and Hall/CRC
• Year: 2017
• ISBN:
  9781351652094
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Networking
• Index: Computer Science

This book provides a dual perspective on the Internet of Things and ubiquitous computing, along with their applications in healthcare and smart cities. It also covers other interdisciplinary aspects of the Internet of Things like big data, embedded Systems and wireless Sensor Networks. Detailed coverage of the underlying architecture, framework, and state-of the art methodologies form the core of the book.

Section IV

Future Research, Scope, and Case Studies of the Internet of Things and Ubiquitous Computing

16

Infection Tracing in i-Hospital

Mimonah Al Qathrady, Ahmed Helmy, and Khalid Lmuzaini

CONTENTS

16.1 Introduction and Background

16.2 i-Hospital Nodes Tracking and Processing High-Level Framework

16.2.1 Encounter Sensing and Collection Side

16.2.1.1 Sensing Devices

16.2.1.2 Network Architectures

16.2.1.3 Filters and Aggregation

16.2.1.4 Sending the Tracking and Sensing Information to the Server

16.2.2 Server Side

16.2.3 Application Side

16.3 Infection Tracing: The Infection-Tracing Framework

16.3.1 Encounters Side

16.3.1.1 Encounter Collection and Processing

16.3.1.2 Encounter Range

16.3.1.3 Direct/Indirect Encounter

16.3.1.4 Encounter with Human and Objects

16.3.2 Tracing Side

16.3.2.1 Tracing Parameters

16.3.2.2 Disease Spread Modeling

16.3.2.3 Infection Tracing

16.3.3 Evaluation for i-Hospital Infection Tracing System

16.3.3.1 Evaluation Metrics

16.3.3.2 Simulation and Evaluation

16.4 Conclusion

References

16.1 Introduction and Background

Infection transmission risks are present in all hospital settings as well as other communities. According to the CDC (2007), humans—patients, healthcare personnel, or visitors—constitute the primary source of infectious agents, along with other objects such as contaminated equipment. Direct contact and indirect contact with individuals or objects are the primary methods of transmission of infectious agents. Also, Middle East Respiratory Syndrome has an outbreak in South Korea was traced to a patient who has been admitted to a hospital (Tina, 2016), where the virus is transmitted to 82 other people, mostly visitors and fellow patients. Moreover, according to a survey that was based on a large sample of U.S. acute care hospitals, about 1 in 25 hospital patients have at least one health-care-associated infection (Magill et al., 2014). These, as well as cases of H1N1 Swine Flu and Ebola, in which more than 1,300 patients were admitted in a Singapore Hospital because of H1N1 (Subramony et al., 2010). Also, there are more than 4K reported deaths in West Africa in the first nine months of the Ebola epidemic (Team, 2014). These cases provide concrete examples that motivate our work of infection tracing.

When there is a case of epidemic disease, it is vital to identify the original sources to control and prevent further spread of infection by tracing the sources and then tracing forward from the sources and identifying the population with higher risk including the nodes that contacted the sources and might be infected as well.

As many smart hospitals and communities are equipped with mobile and sensing devices to track the objects’ movement and their encounters, we propose to enhance these technologies and act immediately whenever a case of infection is detected. Our target is an intelligent hospital that deploys Internet of Things and able to track the encounter between individuals and objects.

Using a mobile application to collect encounter information has been used in the past different studies for different purposes. In iTrust (Kumar & Helmy, 2012), the encounter information is filtered and ranked for social reasons to identify trusted encounters. It ranks the encountered nodes based on the duration and frequency of encounters.

Other researchers have worked on studying the infection transmission and dynamics in different populations. They captured the encounters between peoples using sensing devices. A wireless sensor network technology was used to obtain close proximity data between individuals during a typical day at an American high school; then, the data are used to construct the social network relevant for infectious disease transmission (Salathé et al., 2010). Also, face-to-face interactions between the attendees at a conference were captured using radio-frequency identification (RFID), the spread of epidemics along these interactions was simulated (Stehlé et al., 2011).

Mobile phone devices and Bluetooth have been used in other works as well to collect the encounters and study or map the epidemic disease. Mobile phones are used to collect human contact data and record information such as locality, user symptoms for flu or cold, and human interactions. Then, the data were used to develop mathematical models for the spread of infectious diseases (Yoneki & Crowcroft, 2014). The sensed interaction by mobile phone and Bluetooth data is proved to be suitable for modeling the spread of disease and increase the predictive power of epidemic models (Farrahi et al., 2015). Also, GPS in mobile phones was used for tracking the epidemic on the map; after the patient is diagnosed with Dengue fever, the patient’s location is used to draw the epidemic map (Reddy et al., 2015)

The previous researches have focused on modeling the disease transmission, which is only one component of the i-hospital infection-tracing framework.

More intelligent hospitals in the future will deploy Internet of Things which consists of heterogeneous devices. As a result, the encounter collection framework in this chapter is designed to be used with different heterogeneous devices as it will be the case with the Internet of Things. The sensing devices will gather the encounter data. Thus, the applications that use the framework are intended to be technology independent. They could potentially work with data collected from various technologies, for example, RFID or mobile phones.

Another important part of our infection-tracing framework is tracing back to infection sources. The concept of tracing back has been usually used as a defense mechanism in computer networks to identify the source of attacks (Peng et al., 2007). Kim and Helmy (2010) introduced a protocol framework to trace back the attackers in mobile multihop networks; the problem is complicated by mobility.

Tracing back the attackers in a computer network is less complicated than tracing the disease infection for several reasons: the main reasons are related to the nature of the agents’ spread methods and ranges. There may be a different range (and method) for the disease spread, sensing, and communication. Such differences must be taken into consideration during the design, simulation, and analysis. Also, to make the attack happen in computer networks, there must be some kind of communication between nodes, so information about the communicating nodes can be saved. On the other hand, when the disease spreads from one person to another, it is impossible to record the exact moment of the infected nodes during the transmission process.

Trace back and forward for a source of problem and subjects that have been affected by a problem were used in the foodborne outbreak to identify the source of a product that was implicated in the foodborne outbreak (Weiser et al., 2013). Tracing back the supply chain is different than tracing the disease. This is because the supply chain database has certain data, whereas the certain data when the infection transmitted between two individuals are not available.

Several researchers have tried to solve the problem of identifying a source of different kind of infection like rumors when it spread in the society. For example, Shah and Zaman (2011) obtained an estimator for the rumor source based on the infected nodes and the underlying network structure. Others considered the problem of identifying an infection source based on an observed set of infected nodes in a network (Luo & Tay, 2013). The works of finding the source out of the infected population usually have knowledge about the group of infected nodes and attempt to figure out which one is the source.

The idea of infection tracing in this chapter has more challenging scope where the knowledge about infected nodes is not available, and the system attempts to find the sources and population at risk after knowing only one case.

Contact tracing is an important mean of controlling infectious diseases. Armbruster and Brandeau (2007a) developed a simulation model for contact tracing and used it to explore the effectiveness of different contact-tracing policies in a budget-constrained setting. A simulation model of contact tracing is used to evaluate the cost and effectiveness of different levels of contact tracing (Armbruster & Brandeau, 2007b).

This chapter will explain the infection tracing framework that exploits the Internet of Things in the intelligent hospital that collects encounter information. Also, it will explain how this encounter data can be used to trace automatically the source of infection and population at risks as soon as one case of infection is detected.

An i-hospital system that traces back from infected node to identify the original sources of infection will control and prevent further spread of infection and then guide the trace to identify infected population even before the patients report their cases.

The first part of the chapter is about an i-hospital high-level framework that gathers the encounter data from different heterogeneous devices of the Internet of Things (IoT), processes and utilizes them for various applications. It focuses on the encounter collection and processing.

The second part is about the infection-tracing framework. It shows how the encounter data that collected from IoT can be utilized to tackle the tracing problem in the intelligent hospital. Then, the disease infection source trace back problem is...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Editors
• Contributors
• Section I Introduction to the Internet of Things: Definition and Basic Foundation
• Section II Frameworks for the Internet of Things: An Architectural Perspective
• Section III Interdisciplinary Aspects of the Internet of Things
• Section IV Future Research, Scope, and Case Studies of the Internet of Things and Ubiquitous Computing
• Index