Named Data Networking for Efficient IoT-based Disaster Management in a Smart Campus

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.

Facilitating Successful Smart Campus Transitions: A Systems Thinking-SWOT Analysis Approach

“…This is evident in the Smart Campus project reportage in extant literature. Examples of such projects include the development of an anytime-anywhere learning within a Smart Campus environment [46], Smart parking [11,47,48], frameworks for modeling movements on a Smart Campus [49], development of platforms for energy management and optimization on campuses [50][51][52], dynamic timetabling systems [53], the use of apps for location directions and information dissemination purposes [54], real-time space utilization measurement [55], development of a context-aware Smart classroom [56][57][58][59], and the use of digital platforms for IoT-based disaster management [60].…”

An Information-Centric Semantic Data Collection Tree for Wireless Sensor Networks

“…Information-centric networking (ICN) [22][23][24][25] is considered a promising approach for Internet of Things [26][27][28]. Recently, a number of works have been investigated to explore the benefits of ICN for IoT [10,11,[29][30][31][32].…”

Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

“…In literature, some modern research studies political campaigns, religious activities, organizational professionalism, web mining, and many more social networks based on such three-people subgraph [11]. Listing and counting of triads in a social network are considered triad census using the subgraph method of graph theory [7,12,13]. e clustering coefficient, a robust graph theory method, highlights the degree of nodes likely to be part of a cluster.…”