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Every year, natural disasters cause major loss of human life, damage to infrastructure and significant economic impact on the areas involved. Geospatial Scientists aim to help in mitigating or managing such hazards by computational modeling of these complex events, while Information Communication Technology (ICT) supports the execution of various models addressing different aspects of disaster management. The execution of natural hazard models using traditional ICT foundations is not possible in a timely manner due to the complex nature of the models, the need for large-scale computational resources as well as intensive data and concurrent-access requirements. Cloud Computing can address these challenges with near-unlimited capacity for computation, storage and networking, and the ability to offer natural hazard modeling systems as end services has now become more realistic than ever. However, researchers face several challenges in adopting and utilizing Cloud Computing technologies in this area. Moreover, accessing the Cloud services during the disaster where the communication and power supply can break down, is still an open challenge. As such, this survey paper discusses these challenges, needs and existing problems to reflect the current research trends and outlines a conceptual Cloud-based solution framework for more effective natural hazards modeling and management systems using Cloud infrastructure in conjunction with other technologies such as Internet of Things(IoT) networks, fog and edge computing. We draw a clear picture of the current research state in the area and suggest further research directions for future systems.

The rise of robots and robotics has proved to be a benefaction to humankind in different aspects. Robotics evolved from a simple button, has seen massive development over the years. Consequently, it has become an integral part of human life as robots are used for a wide range of applications ranging from indoor uses to interplanetary missions. Recently, the use of social robots, in commercial indoor spaces to offer help or social interaction with people, has been quite popular. As such, taking the increasing use of social robots into consideration, many works have been carried out to develop the robots to make them capable of acting like humans. The notion behind this development is the need for robots to offer services without being asked. Social robots should think more like humans and suggest possible and suitable actions by analyzing the environment where they are. Belief–desire–intention (BDI) is one of the most popular models for developing rational agents based on how humans act based on the information derived from an environment. As such, this work defines a foundation architecture to integrate a BDI framework into a social robot to add “act like a human” feature for proactive behaviors. The work validates the proposed architecture by developing a vision-based proactive action using the PROFETA BDI framework in an indoor social robot, Waldo, operated by the robot operating system (ROS).

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