NSF CNS 1637371: EAGER: Creating a Community Infrastructure for Interoperable Emergency Connectivity
- Principle Investigator: Kaikai Liu
- Awarded Amount: $199,921.00
- NSF Program: S&CC: Smart & Connected Commun
- Project page: [nsf page]
First responders to disasters need a complete picture of the community’s status in order to accurately assess the condition of the inhabitants and organize available resources to save lives, protect the environment and prevent further damage in the community. In normal circumstances public safety services rely on 9-1-1 calls and social media to gather information from residents about community conditions. However, under disaster conditions, these normal communication methods will be interrupted, including landline and cell phones, internet connectivity and power. In these circumstances, novel systems must be available to substitute for the lost connectivity, to allow residents to connect to the public safety answering point, and to allow the Emergency Operations Center to collect and aggregate critical information across sectors to ensure that lifesaving operations are conducted expeditiously.
The solution to managing risks to disaster-prone communities includes integrating existing technologies, applications, data and e-services in sustainable networks that will support emergency communications even in catastrophic events. This research proposes to develop a community infrastructure for interoperable emergency connectivity that can operate in austere conditions, provide its own power, and create linkages throughout the community and across jurisdictional boundaries. This project will deploy the edge devices in local communities with multi-modal communication modules as well as an external long range radio. The proposed resilient and participatory networking framework on top of the remote edge devices will enable collaborative communication as well as participatory sensing. To solve current deficiencies in the ability of allowing city emergency responders to control and automate the remote edge devices, this project extends existing cloud orchestration frameworks to edge devices that are agnostic to the network media. For this demonstration project, the central cloud deployed in the City of San Jose’s Emergency Operations Center will control the remote edge devices, and be responsible for resilient quality testing, automatic validation, disaster assessment, resource allocation, and the automation of remote edge devices.
Summary of Research Activity
To enable the community infrastructure for interoperable emergency connectivity, we have developed two edge infrastructure nodes with supporting software frameworks: 1) we developed a community gateway node from scratch for interoperable emergency connectivity, codenamed SJGateway and 2) we developed a new edge infrastructure with multi-modal AI computing capability as well as interoperable connectivity, codenamed SJAIedge. Our developed SJGateway and SJAIedge are designed to work cooperatively to meet various current and future needs of a community infrastructure including multi-modal sensing, localization, smart surveillance, interoperable connectivity, on-board machine learning, low-latency inference, multi-tenant support, and high energy efficiency design. Our goal is to propose effective strategies so that our infrastructures are better utilized with interoperable connectivity. Our developed SJGateway and SJAIedge can be potentially deployed on wide areas, for example, utility poles, streets, water lines, buses, trains, and traffic lights. Community stakeholders or smart services providers can all use our system as their infrastructure, providing data, activity, and location awareness. The interoperability feature of our system can help them save their existing investment. For example, our system can interoperate with their current deployed network (WiFi, Ethernet) and IoT solutions (LTE-M, LoRA, Bluetooth 5.0, Zigbee, XBee, 6LoWPAN, sub-1Ghz). They also can test new features by enabling or plugging into different computing and networking modules. Developers can select their favorite configurations, which will in turn lower the engineering cost.