Natural disasters are a large threat for people especially in developing countries such as Laos. ICT-based disaster management systems aim at supporting disaster warning and response efforts. However, the ability to directly communicate in both directions between local and administrative level is often not supported, and a tight integration into administrative workflows is missing. In this paper, we present the smartphone-based disaster and reporting system Mobile4D. It allows for bi-directional communication while being fully involved in administrative processes. We present the system setup and discuss integration into administrative structures in Lao PDR.
Several ICT frameworks and systems related to disasters are in use, most of them targeting the developed world. In developing countries, additional issues have to be faced. As @cite_3 point out, effective warning systems require "not only the use of ICTs, but also the existence of institutions that allow for the effective mobilization of their potential", so the effective inclusion of administrative units play a critical role.
@cite_3
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The Philippines is one of the countries in the world vulnerable to natural hazards because of its geographic location. It also lacks an efficient disaster management system that will help in times of need. One common scenario during disasters is that the activity of rescue and relief is not well-coordinated. For this reason, there is a need for a system that will help in the efficient provision of rescue and relief to disaster-affected areas. Since the use of smart phones is gaining interest in people, the disaster management system was implemented as a smart phone application using Google's Android operating system. The disaster management system Android application known as MyDisasterDroid determines the optimum route along different geographical locations that the volunteers and rescuers need to take in order to serve the most number of people and provide maximum coverage of the area in the shortest possible time. Genetic algorithm was applied for optimization and different parameters were varied to determine the most optimum route. Natural and man-made emergencies pose an ever-present threat to the society. In response to the growing number of recent disasters, such as the Indonesian volcanic eruption, Gulf of Mexico oil spill, Haitian earthquake, Pakistani floods, and in particular, the Red River crest that causes flood almost every year here in Fargo, North Dakota, we propose a community-based scalable cloud computing infrastructure for large-scale emergency management. This infrastructure will coordinate various organizations and integrate massive amounts of heterogeneous data sources to effectively deploy personnel and logistics to aid in search and rescue. The infrastructure also will aid in damage assessment, enumeration, and coordination to support sustainable livelihood by protecting lives, properties and the environment. In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims’ requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them. Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection. HighlightsMaps different types of anomalies with network attacks.Provides an up-to-date taxonomy of network anomaly detection.Evaluates effectiveness of different categories of techniques.Explores recent research related to publicly available network intrusion evaluation datasets.