Emergent situations for smart cities: A survey

A smart city is a community that uses communication and information technology to improve sustainability, livability, and feasibility. As any community, there are always unexpected emergencies, which must be treated to preserve the regular order. However, a smart system is needed to be able to respond effectively to these emergent situations. The contribution made in this survey is twofold. Firstly, it provides a comprehensive exhaustive and categorized overview of the existing surveys for smart cities.  The categorization is based on several criteria such as structures, benefits, advantages, applications, challenges, issues, and future directions. Secondly, it aims to analyze several studies with respect to emergent situations and management to smart cities. The analysis is based on several factors such as the challenges and issues discussed, the solutions proposed, and opportunities for future research. The challenges include security, privacy, reliability, performance, scalability, heterogeneity, scheduling, resource management, and latency. Few studies have investigated the emergent situations of smart cities and despite the importance of latency factor for smart city applications, it is rarely discussed

Crowdsourcing-based Disaster Management using Fog Computing in Internet of Things Paradigm

In internet of things (IoT) paradigm, crowdsourcing is the process of obtaining and analyzing information or input to a particular task or project generated by a number of sources such as sensors, mobile devices, vehicles and human. Cloud computing is widely used for the services such as analyzing crowdsourced data and application implementation over the IoT. Nowadays, every country and human are prone to natural and artificial disasters. Early detection about disasters such as earthquakes, fire, storms, and floods can save thousands of people’s life and effective preventive measure can be taken for the public safety. All the crowdsourced data which are providing the information of a certain geographic region are analyzed in a cloud platform. But, by the time the crowdsourced data makes its way to the cloud for analysis, the opportunity to act on it might be gone. Moreover, thousands of people’s life will be lost. Therefore, fog computing is the new and efficient way to analyze such critical crowdsourced IoT data of disasters. In this paper, in order to detect and take necessary steps for public safety during a disaster, we propose a crowdsourcing-based disaster management using fog computing (CDMFC) model in IoT. Further, we also proposed a data offloading mechanism for our CDMFC model to send disaster-related IoT data to the fog even if a direct link to the fog is not available. Our proposed CDMFC model and its data offloading mechanism can detect real-time disasters and disseminate early information for public safety as compared to the conventional cloud computing based disaster management models