Ali, Zain, Shah, Munam Ali, Almogren, Ahmad, Ud Din, Ikram, Maple, Carsten and Khattak, Hasan Ali (2020) Named data networking for efficient IoT-based disaster management in a smart campus. Sustainability, 12 (8). 3088. doi:10.3390/su12083088 ISSN 2071-1050.

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Abstract

Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS.

Item Type:	Journal Article
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare T Technology > TD Environmental technology. Sanitary engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Emergency management -- Data processing, Disaster relief -- Data processing, Smart cities , Internet of things
Journal or Publication Title:	Sustainability
Publisher:	MDPI
ISSN:	2071-1050
Official Date:	12 April 2020
Dates:	Date Event 12 April 2020 Published 8 April 2020 Accepted
Volume:	12
Number:	8
Article Number:	3088
DOI:	10.3390/su12083088
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	30 April 2020
Date of first compliant Open Access:	30 April 2020
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID UNSPECIFIED King Saud University http://dx.doi.org/10.13039/501100002383

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