Edge and fog computing for healthcare

Many health facilities use advanced IoT solutions to implement smart health care applications using real-time data from sensors, meters, and many other devices. Other exciting health IoT initiatives include reducing waiting time in emergency rooms, tracking assets and people who travel to hospitals, providing proactive alerts about medical devices that may fail, and preventing threats and unauthorized entry and departure from health facilities using video security cameras and electronic ID-enabled security doors. These advances are undoubtedly impressive but dealing with the vast amount of data generated is a challenge.

These potential benefits of using IoT solutions in health care systems come with challenges concerning storing, disseminating, and processing vast amounts of sensed data. Many healthcare applications use the power and elasticity of the cloud for data storage and processing. However, time-sensitive health care applications cannot bear transmitting data streams to cloud servers for processing because of unacceptably high latencies and high network bandwidth requirements. Instead of transporting data to cloud servers for processing and storage, end devices and sensors should pass the data to edge computing devices to aggregate, process, or analyze that data to minimize costs and lower latency while controlling network bandwidth. A substantial benefit of this operation is reducing data that must be transmitted and stored in the cloud. Many issues must be addressed, such as scheduling applications’ tasks on fog and cloud servers and security and privacy at all data exchange and processing levels.

Data Analytics in Smart Cities

As a result of urban growth and migration waves, cities are experiencing tremendous pressure as their infrastructures have to cope with increasing demand for supply on water, energy, transportation, healthcare, education, and safety. City stakeholders are using digital technologies to reduce costs, improve the quality of services delivered to citizens, balance budgets, and enhance the efficiency of various city systems.

The lack of analytics and data processing as the diverse city components and systems generate it results in making city utilities and services operate sub-optimally, limiting the creation of value-added services, increasing transport costs, etc. New technologies, such as edge/fog computing, and blockchain, offer opportunities to mitigate these impacts and transform cities into smart cities through smart and innovative planning, management, and operation.

The main challenges in managing smart city components lie in their ability to locally aggregate and pre-process data streams generated by their various IoT devices and sensors, process and analyze data at the edge of the network to respond in a timely-manner to time-sensitive smart city applications, utilize big data analytics to get deep insights from collected data and harness the city governance and ensure secure distributed storage and sharing of IoT Data. This project aims to address these challenges.

Processing of Urban Data Streams

Massive amounts of data are continuously collected from various sensors and IoT devices across the city and people’s wearable devices and smartphones. These sensors and devices monitor the operations of various city systems as diverse as water, energy, transportation, and the environment. These large volumes of structured and unstructured data are so broad and complex to manage with traditional data management tools and methods. Besides, social networks such as Twitter, Facebook, and Google+ are becoming a new source of real-time information concerning human activities in smart cities. Social network users are regarded as social sensors, and these data sources are currently insufficiently leveraged or used by planning authorities.

As the volume and velocity of stream data to store and process might vary over time, flexible data management systems or services are required to permit near real-time processing of data originating from sensor networks, IoT devices, and smartphones possibly fused with other data such as open data. The data processing chain may involve operations like filtering, aggregating, and ultimately storing resulting data for further processing by data analytics applications. Moreover, successful management of smart city systems lies in the ability to federate their data, process data streams generated by their various IoT devices and sensors, and utilize big data analytics to harness their governance.

Effective management and processing of urban data streams is a fundamental component of achieving the goals of the smart city. These goals include tackling the problems, reducing resource consumption and costs, engaging actively with citizens, and making informed decisions that will enhance the environment and economic outcomes leading to improved quality of urban life. By processing urban data streams, it will be possible to identify the most significant events and patterns and make appropriate decisions in near real-time. It will allow responding to urgent situations with both speed and precision.

Building a Pipeline for the Processing of Data Streams

Over the last few years, near-real-time systems have witnessed tremendous growth. In these data-intensive systems and applications, data is modeled as transient data streams rather than persistent database relations. These systems are proliferating in financial applications, network monitoring, social media, online shopping, gaming, smart buildings, smart cities, smart factories, and so on. In the data stream model, individual data items may be sensor readings, web page visits, relational tuples, network measurements, etc.

This project aims to design and develop a data pipeline to collect and process data streams. The pipeline will facilitate data analysis, building applications, and mashups that will take the benefit of data streams. Other advanced applications include notification of alerts, real-time event detection, and building monitoring dashboards. A usage scenario will consider acquiring and analyzing data streams from social media networks like Meetup and LinkedIn.

Integration of Bioinformatics Data and Services Using the Service Oriented Architecture and Mashups

The Service Oriented Architecture (SOA) embodied by Web services has emerged as a key technology for providing services over the Web. Web services are interoperable across platforms and neutral to languages, which makes them suitable for access from heterogeneous environments. Web services technology has the potential to be a significant component in the integration endeavor because it provides a higher layer of abstraction that hides implementation details from applications. It allows applications to invoke other applications’ functions through well-defined, easy-to-use interfaces. Each organization is free to concentrate on its competence and leverage the services other research groups provide. Furthermore, and through the composition of Web Services, scientists will be able to implement their protocols easily.

Traditionally, significant programming effort has been required to parse and integrate heterogeneous datasets before enabling scientists to answer interesting questions. The heterogeneity includes different data formats, information models, and terminologies. Recently, a new breed of Web-based data-integration tools has been developed to simplify this process. They are called ‘‘mashups.” These mashup tools have been designed to empower end-users to extract, format, and remix data across multiple Web sites.

In this work, we investigate how these emerging technologies may be applied in the Life Sciences to achieve data and services integration.

Context-Aware Service Provisioning to Mobile Users

Accessing services using portable devices, such as PDAs and Smartphones, is increasingly becoming ubiquitous. Modern mobile devices are often fully equipped with broad capabilities. These devices support wireless communication options, including Wi-Fi, Bluetooth, GPRS, and EDGE. They also come with advanced multimedia capabilities, including streaming and the ability to play several audio and video formats. These devices now present browsing capabilities beyond the simple WAP protocol to support HTML-based Web sites.

Due to these technological developments, mobile users increasingly require access to various services as they move from one location to another, from one device to another, and from one network to another. They require services tailored to their needs and novel means for locating relevant content, where relevance has a user-specific definition. Using context is essential to cope and timely react to changes in such environments, achieving adaptability, reliability, and seamless service provisioning.

This project investigates special features of Service Oriented Computing, especially Web services technology, and mobile computing, to design a context-aware platform for supporting mobile users with personalized services. The platform should handle different types of context sources (e.g., sensors, readers, agents), offer sophisticated mechanisms to match the mobile user’s preferences with services available at the visited location, and provide these services in a personalized and adaptive manner to the user conditions.