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69Intelligent Guards against Disasters (iGaDs) could process and respond to alert and warning messages from responsible authorities and thus help in preparing to respond to disasters, but making such smart devices and systems dependable and affordable enough for pervasive use in future smart living environments will require strong standards. C ountries and intergovernmental organizations worldwide now have built or are building disaster management information systems to strengthen their capabilities for coping with emergencies. In the European Union, projects such as Osiris and Sany have provided standards and tools to make diverse sensor networks and sensor web services interoperable, thus facilitating their use in emergency alert and response systems. In the US, the Integrated Public Alert and Warning System Open Platform for Emergency Networks (IPAWS-Open) provides services to receive, authenticate, and route standardsbased messages from alerting authorities to all types of alert systems via all communication pathways, including public broadcast, cellular networks, and the Internet. The XML-based Common Alerting Protocol (CAP) enables message exchange among emergency information systems, automatic reporting by sensor systems to analysis centers, and aggregation and correlation of warnings from multiple sources. More importantly, smart devices and applications can authenticate, process, and act upon IPAWS-distributed, CAP-conforming alert messages. The "Disaster Manage-

Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

Due to the popularity of indoor positioning technology, indoor navigation applications have been deployed in large buildings, such as hospitals, airports, and train stations, to guide visitors to their destinations. A commonly-used user interface, shown on smartphones, is a 2D floor map with a route to the destination. The navigation instructions, such as turn left, turn right, and go straight, pop up on the screen when users come to an intersection. However, owing to the restrictions of a 2D navigation map, users may face mental pressure and get confused while they are making a connection between the real environment and the 2D navigation map before moving forward. For this reason, we developed ARBIN, an augmented reality-based navigation system, which posts navigation instructions on the screen of real-world environments for ease of use. Thus, there is no need for users to make a connection between the navigation instructions and the real-world environment. In order to evaluate the applicability of ARBIN, a series of experiments were conducted in the outpatient area of the National Taiwan University Hospital YunLin Branch, which is nearly 1800 m2, with 35 destinations and points of interests, such as a cardiovascular clinic, x-ray examination room, pharmacy, and so on. Four different types of smartphone were adopted for evaluation. Our results show that ARBIN can achieve 3 to 5 m accuracy, and provide users with correct instructions on their way to the destinations. ARBIN proved to be a practical solution for indoor navigation, especially for large buildings.

Disaster warning and surveillance systems have been widely applied to help the public be aware of an emergency. However, existing warning systems are unable to cooperate with household appliances or embedded controllers; that is, they cannot provide enough time for preparedness and evacuation, especially for disasters like earthquakes. In addition, the existing warning and surveillance systems are not responsible for collecting sufficient information inside a building for relief workers to conduct a proper rescue action after a disaster happens. In this paper, we describe the design and implementation of a proof of concept prototype, named the active disaster response system (ADRS), which automatically performs emergency tasks when an earthquake happens. ADRS can interpret Common Alerting Protocol (CAP) messages, published by an official agency, and actuate embedded controllers to perform emergency tasks to respond to the alerts. Examples of emergency tasks include opening doors and windows and cutting off power lines and gas valves. In addition, ADRS can maintain a temporary network by utilizing the embedded controllers; hence, victims trapped inside a building are still able to post emergency messages if the original network is disconnected. We conducted a field trial to evaluate the effectiveness of ADRS after an earthquake happened. Our results show that compared to manually operating emergency tasks, ADRS can reduce the operation time by up to 15 s, which is long enough for people to get under sturdy furniture, or to evacuate from the third floor to the first floor, or to run more than 100 m.

A heterogeneous multi-processor (HeMP)

Experiences with past major disasters tell us that people with wireless devices and social network services can serve effectively as mobile human sensors. A disaster warning and response system can solicit eye-witness reports from selected participants and use information provided by them to supplement surveillance sensor coverage. This paper describes a natural formulation of the participant selection problem that the system needs to solve in order to select participants from available people given their qualities as human sensors and the costs of deploying them. For this, we developed a greedy algorithm, named PSP-G, that first calculates the benefit-to-cost (B2C) factor of each participant. It then dispatches participants to regions according to participants' B2C. We compared PSP-G with the two well-known optimization methods, BARON and BONMIN. The results show that PSP-G delivers a near optimal solution with a low time complexity. In particular, the time PSP-G needs can be merely one tenth of the execution time of the existing optimization methods, which makes PSP-G a practical solution for emergency needs in disaster areas.

Many disaster warning and response systems can improve their surveillance coverage of the threatened area by supplementing in situ and remote physical sensor data with crowdsourced human sensor data captured and sent by people in the area. This paper presents fusion methods which enable a crowdsourcing enhanced system to use human sensor data and physical sensor data synergistically to improve its sensor coverage and the quality of its decisions. The methods are built on results of classical statistical detection and estimation theory and use value fusion and decision fusion of human sensor data and physical sensor data in a coherent way. They are the building blocks of a central fusion unit in a crowdsourcing support system for disaster surveillance and early warning applications.Index Terms-Crowdsourcing, multiple sensor fusion, statistical detection and estimation.