To address the aftermath of ever more frequent natural disasters striking highly-populated urban areas, Humanitarian Assistance and Disaster Relief (HADR) operations increasingly involve the coordinated efforts of multiple agencies and in particular Civil-Military Cooperation (CIMIC). In such scenarios, rescuers operate in a disrupted environment, must maintain a high operational tempo, and need to quickly make high-impact decisions. Effectively supporting emergency responders through IT services that implement accurate situation awareness presents a formidable set of challenges, including the discovery and integration of surviving IT assets with purposely deployed ones; effective information prioritization; resilient communications; and secure information sharing. We believe that in the highly digital modern urban environment, often referred to as smart cities, these challenges can be effectively addressed only by integrating by design HADR support into the smart city middleware. This paper presents Aceso - a proof-of-concept smart city middleware that provides location-and context-sensitive services with full support for HADR operations. Aceso provides a set of functions, ranging from resource discovery to secure information sharing, that can be quickly activated in case of unpredictable and adverse events to facilitate HADR operations. Furthermore, Aceso leverages the Value-of-Information (VoI) methodology to handle the processing and dissemination of mission-critical information. To validate the capabilities of Aceso, we devised a fictional HADR scenario, set in the city of Helsinki, Finland, that involves the collaboration of multiple responder teams with different roles. The validation results confirm Aceso's usefulness in prioritizing the processing and dispatching of critical information and in realizing federation-wide sharing of this information among HADR teams.(c) 2022 Elsevier B.V. All rights reserved.

Enabling civil-military collaboration for disaster relief operations in smart city environments

Campioni, L
Primo
;
Poltronieri, F
Secondo
;
Stefanelli, C;Tortonesi, M
Penultimo
;
2023

Abstract

To address the aftermath of ever more frequent natural disasters striking highly-populated urban areas, Humanitarian Assistance and Disaster Relief (HADR) operations increasingly involve the coordinated efforts of multiple agencies and in particular Civil-Military Cooperation (CIMIC). In such scenarios, rescuers operate in a disrupted environment, must maintain a high operational tempo, and need to quickly make high-impact decisions. Effectively supporting emergency responders through IT services that implement accurate situation awareness presents a formidable set of challenges, including the discovery and integration of surviving IT assets with purposely deployed ones; effective information prioritization; resilient communications; and secure information sharing. We believe that in the highly digital modern urban environment, often referred to as smart cities, these challenges can be effectively addressed only by integrating by design HADR support into the smart city middleware. This paper presents Aceso - a proof-of-concept smart city middleware that provides location-and context-sensitive services with full support for HADR operations. Aceso provides a set of functions, ranging from resource discovery to secure information sharing, that can be quickly activated in case of unpredictable and adverse events to facilitate HADR operations. Furthermore, Aceso leverages the Value-of-Information (VoI) methodology to handle the processing and dissemination of mission-critical information. To validate the capabilities of Aceso, we devised a fictional HADR scenario, set in the city of Helsinki, Finland, that involves the collaboration of multiple responder teams with different roles. The validation results confirm Aceso's usefulness in prioritizing the processing and dispatching of critical information and in realizing federation-wide sharing of this information among HADR teams.(c) 2022 Elsevier B.V. All rights reserved.
2023
Campioni, L; Poltronieri, F; Stefanelli, C; Suri, N; Tortonesi, M; Wrona, K
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2500716
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