When stricken by a catastrophic natural disaster, emergency rescue operation is very critical to many lives. Many people trapped in the disastrous areas under collapsed buildings or landslides may have a large chance to survive if they are rescued in "Golden 72 Hours". Victims rescued from the debris and evacuated from their home jammed in shelters need life support material to support their lives. They also need to communicate to each other for various reasons. However, communication systems were usually down due to various reasons. The loss of communication systems and information networks made the rescue and relief operation extremely difficult. Many people died before they got a chance to be rescued. This paper analyzes the causes that paralyzed the entire communication systems in Jiji/Taiwan Earthquake and proposes a MANET based communication platform as well as a Rescue Information System for Earthquake Disasters that can support a large number of rescue volunteers under catastrophic natural disasters.

When stricken by a catastrophic natural disaster, emergency rescue operation is very critical to many lives. Many people trapped in the disastrous areas under collapsed buildings or landslides may have a large chance to survive if they are rescued in "Golden 72 Hours". People evacuated from their home jammed in highways or dome shelters need to communicate to each other for various reasons. However, communication systems were usually down due to various reasons. The loss of communication systems made the rescue operation extremely difficult. Many people died before they got a chance to be rescued. This paper analyzes the causes that paralyzed the entire communication systems in Jiji earthquake and proposes a MANET based emergency communication and information system that can support a large number of rescue volunteers under catastrophic natural disasters.

Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model’s performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 $\mathsf{\mu }$ g/ ${\mathrm{m}}^3$ and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 $\mathsf{\mu }$ g/ ${\mathrm{m}}^3$ which is significantly low.

Delay-tolerant networking (DTN) is a network architecture characterized by the lack of continuous connectivity. Messages are delivered by moving nodes in a store-and-forward manner. In such a network, the mobility models of nodes play an important role in DTNs, because messages can only be delivered when two or more nodes contact each other. In general, mobility models can be categorized into synthetic and trace models. Synthetic models are based on mathematical models that generate the mobility models. Trace models record people's daily movements in the real world; these models faithfully render the actual situation of people's movements in their lives. However, there is a challenge in studying human mobility, and the mobility models of humans affect the performance of routing protocols in DTNs. In this article, we design an Android application to collect the mobility traces of college students in a campus environment, called NCCU Trace Data. We design a mobility model that traces students' movement, and this model can be imported into the ONE simulator to verify routing protocols. More importantly, it can be used to evaluate the performance of a social-based routing method. Finally, we evaluate our routing method and compare it to other routing methods in each mobility model. The simulation results show that our newly designed mobility model is a step closer to the real environment.

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