A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Use of telecommunication technologies for remote, continuous monitoring of patients can enhance effectiveness of emergency ambulance care during transport from rural areas to a regional center hospital. However, the communication along the various routes in rural areas may have wide bandwidth ranges from 2G to 4G; some regions may have only lower satellite bandwidth available. Bandwidth fluctuation together with real-time communication of various clinical multimedia pose a major challenge during rural patient ambulance transport.; AB@The availability of a pre-transport route-dependent communication bandwidth database is an important resource in remote monitoring and clinical multimedia transmission in rural ambulance transport. Here, we present a geo-communication dataset from extensive profiling of 4 major US mobile carriers in Illinois, from the rural location of Hoopeston to the central referral hospital center at Urbana. In collaboration with Carle Foundation Hospital, we developed a profiler, and collected various geographical and communication traces for realistic emergency rural ambulance transport scenarios. Our dataset is to support our ongoing work of proposing "physiology-aware DASH", which is particularly useful for adaptive remote monitoring of critically ill patients in emergency rural ambulance transport. It provides insights on ensuring higher Quality of Service (QoS) for most critical clinical multimedia in response to changes in patients' physiological states and bandwidth conditions. Our dataset is available online for research community.Comment: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17), Pages 158-163, Taipei, Taiwan, June 20 - 23, 201

Context-Aware Voip congestion control service

Published in The African Journal of Information and Communication, Issue no 11 2010/2011IP networks can have difficulty coping with delay-sensitive VoIP traffics during emergency situations caused by fires and related disasters. During emergencies there is a huge increase in voice and video traffic, causing a huge strain on the network. The strain on the network is as a result of both essential and non-essential traffic. In such crisis situations, calls originating from or destined for rescue personnel, such as doctors and police, are considered essential. Any other calls from eyewitnesses and the public are considered non-essential, since they degrade the quality of service for the emergency response teams by consuming the scarce network resources. Providing the rescue team with the quality of service that they require necessitates network access restriction for non-essential traffic. In this paper, the authors present a voice and video service that uses Context-Awareness and Semantic Web technologies to restrict network access to privileged users during crisis situations. The service monitors the network for crisis conditions, enables the network to respond appropriately when a crisis occurs, detects the end of the crisis and reverts to its default state.IP networks can have difficulty coping with delay-sensitive VoIP traffics during emergency situations caused by fires and related disasters. During emergencies there is a huge increase in voice and video traffic, causing a huge strain on the network. The strain on the network is as a result of both essential and non-essential traffic. In such crisis situations, calls originating from or destined for rescue personnel, such as doctors and police, are considered essential. Any other calls from eyewitnesses and the public are considered non-essential, since they degrade the quality of service for the emergency response teams by consuming the scarce network resources. Providing the rescue team with the quality of service that they require necessitates network access restriction for non-essential traffic. In this paper, the authors present a voice and video service that uses Context-Awareness and Semantic Web technologies to restrict network access to privileged users during crisis situations. The service monitors the network for crisis conditions, enables the network to respond appropriately when a crisis occurs, detects the end of the crisis and reverts to its default state

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Context-aware VoIP congestion control service

IP networks can have difficulty coping with delay-sensitive VoIP traffics during emergency situations caused by fires and related disasters. During emergencies there is a huge increase in voice and video traffic, causing a huge strain on the network. The strain on the network is as a result of both essential and non-essential traffic. In such crisis situations, calls originating from or destined for rescue personnel, such as doctors and police, are considered essential. Any other calls from eyewitnesses and the public are considered non-essential, since they degrade the quality of service for the emergency response teams by consuming the scarce network resources. Providing the rescue team with the quality of service that they require necessitates network access restriction for non-essential traffic. In this paper, the authors present a voice and video service that uses Context-Awareness and Semantic Web technologies to restrict network access to privileged users during crisis situations. The service monitors the network for crisis conditions, enables the network to respond appropriately when a crisis occurs, detects the end of the crisis and reverts to its default state

Towards a Smarter organization for a Self-servicing Society

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. In this paper, a platform is introduced by combining social-oriented communities and complex-event processing concepts: SELFSERV. Its aim is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track on Emergent Technologies for Ambient Assisted Living (ETAAL

Semiotics:Semantic model-driven development for IoT interoperability of emergency services

Modern early warning systems (EWSs) use Internet-of-Things (IoT) technologies to realize real-time data acquisition, risk detection and message brokering between data sources and warnings' destinations. Interoperability is crucial for effective EWSs, enabling the integration of components and the interworking with other EWSs. IoT technologies potentially improve the EWS efficiency and effectiveness, but this potential can only be exploited if interoperability challenges are properly addressed. The three main challenges for interoperability are: (1) achieving semantic integration of a variety of data sources and different representations; (2) supporting time- and safety-critical applications with performance and scalability; and (3) providing data analysis for effective responses with personalized information requirements. In this paper, we describe the “SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS” (SEMIoTICS) framework, which supports the development of semantic interoperable IoT EWSs. The framework has been validated with a pilot performed with accident use cases at the port of Valencia. The validation results show that it fulfils the requirements that we derived from the challenges above.</p