217 research outputs found
Role of Machine Learning, Deep Learning and WSN in Disaster Management: A Review and Proposed Architecture
Disasters are occurrences that have the potential to adversely affect a community via casualties, ecological damage, or monetary losses. Due to its distinctive geoclimatic characteristics, India has always been susceptible to natural calamities. Disaster Management is the management of disaster prevention, readiness, response, and recovery tasks in a systematic manner. This paper reviews various types of disasters and their management approaches implemented by researchers using Wireless Sensor Networks (WSNs) and machine learning techniques. It also compares and contrasts various prediction algorithms and uses the optimal algorithm on multiple flood prediction datasets. After understanding the drawbacks of existing datasets, authors have developed a new dataset for Mumbai, Maharashtra consisting of various attributes for flood prediction. The performance of the optimal algorithm on the dataset is seen by the training, validation and testing accuracy of 100%, 98.57% and 77.59% respectively
Design and implementation of flood monitoring and warning system based on internet of things
Floods are unavoidable phenomena that can cause massive loss of people's lives and the destruction of infrastructure. Flash floods rise rapidly in a flood-prone area, which results in property damage, but the impact on human lives is somewhat preventable by the presence of monitoring systems. Although there are many systems widely in practice by disaster management agencies in monitoring flood levels, most of these systems are limited in range. For example, some systems implementing the Long-Range Wide Area Network (LoRaWAN) have a maximum distance of 300m from the gateway. However, the maximum distance that LoRaWAN can reach is 1.5km. Then, the study on the parameter that involved in LoRaWAN for the Flood Monitoring and Warning System (FMWS) is limited. Furthermore, in most developing countries, the conventional flood gates in water canals are manually operated and suffer from the lack of real-time monitoring of water levels which might lead to an overflow in the channels and flash floods. On top of that, the lack of real-time data analysis in the system that can be accessed is one of the limitations in Malaysia. Therefore, this research design and implementation multiple LoRa-based smart sensors with a LoRaWAN gateway as a network testbed for monitoring flood levels and evaluating the parameter of LoRaWAN. Then, the LoRaWAN’s activation was compared and analysed to identify the best activation for the FMWS. Lastly, the real-time assessment of the risk due to the flood level has been enabled on the Tago.IO dashboard for triggering an early flood warning. The proposed FMWS with LoRaWAN uses an ultrasonic sensor with an Arduino microcontroller to measure water level, Long-Range (LoRa) as a communication module, and a single gateway. The end nodes have been tested in several scenarios to test the FMWS’s communication performance in terms of Received Signal Strength Indication (RSSI), Signal Noise Ratio (SNR), delay, and the Percentages of Data Received (PDR). The design of the sensing node involved the hardware and software with the solar panel as the power source. A 3 Dimension (3D) model for the end node was developed for casing the sensing node. The testing area for testing the performance of LoRaWAN is a 2km radius. Throughout the testing, the proposed system communicates up to 2km in single and multiple node cases. On top of that, the multiple nodes have higher overall SNR value compared to the single node where 56% of all result are positive for multiple nodes while the single node exhibit only 50%. In addition, the RSSI and SNR have impact on the PDR. However, the delay inversely perorational with RSSI, SNR and PDR values. The recommended activation for FMWS is Activation By-Personalization, (ABP) since it is over complete control, especially for achieving a high PDR. Lastly, the data on Tago.IO was accessed via webpages and Tago.IO mobile application. In conclusion, the FMWS able to communicate to the gateway at 1.5km distance. However, the higher the SF, the higher the network's performance at long distances. The ABP is the activation that is suitable for the proposed FMWS. Lastly, the warning system will trigger once the water level reaches the warning level
Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas
This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network
Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas
This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network
Flood monitoring and warning systems: A brief review
Floods and excessive rainfall are unavoidable phenomena that can cause massive loss of people's lives and destruction of infrastructure. Flash floods rise rapidly in flood-prone areas, resulting in property damage, but the impact on human lives is relatively preventable by the presence of monitoring systems. Although there are many systems widely in practice by disaster management agencies in monitoring flood levels, most of these systems are limited range and sophisticated to be used and maintained. Furthermore, in most developing countries, the conventional flood gates in water canals are manually operated and suffer from the lack of real-time monitoring of water levels, leading to an overflow in the channels and flash floods. On top of that, the lacking accurate data analysis in the system that can be accessed is one of the limitations of the conventional flood monitoring and warning systems (FMWS). Therefore, in this paper, we have explored and reviewed the existing methods of flood monitoring and emphasizing their structure and sensing techniques. We have also classified and compared their advantages and limitations and accordingly suggested new solutions and improvements by utilizing new technologies based on the Internet of Things. This paper introduces a detailed mini-review of sensing methods in the existing flood systems as reported in previous studies to serve as a quick guide to researchers who are engaging in this field. Based on the review, conclusions have been draw
Hitch Hiker 2.0: a binding model with flexible data aggregation for the Internet-of-Things
Wireless communication plays a critical role in determining the lifetime of Internet-of-Things (IoT) systems. Data aggregation approaches have been widely used to enhance the performance of IoT applications. Such approaches reduce the number of packets that are transmitted by combining multiple packets into one transmission unit, thereby minimising energy consumption, collisions and congestion. However, current data aggregation schemes restrict developers to a specific network structure or cannot handle multi-hop data aggregation. In this paper, we propose Hitch Hiker 2.0, a component binding model that provides support for multi-hop data aggregation. Hitch Hiker uses component meta-data to discover remote component bindings and to construct a multi-hop overlay network within the free payload space of existing traffic flows. Hitch Hiker 2.0 provides end-to-end routing of low-priority traffic while using only a small fraction of the energy of standard communication. This paper extends upon our previous work by incorporating new mechanisms for decentralised route discovery and providing additional application case studies and evaluation. We have developed a prototype implementation of Hitch Hiker for the LooCI component model. Our evaluation shows that Hitch Hiker consumes minimal resources and that using Hitch Hiker to deliver low-priority traffic reduces energy consumption by up to 32 %
Advancing Urban Flood Resilience With Smart Water Infrastructure
Advances in wireless communications and low-power electronics are enabling a new generation of smart water systems that will employ real-time sensing and control to solve our most pressing water challenges. In a future characterized by these systems, networks of sensors will detect and communicate flood events at the neighborhood scale to improve disaster response. Meanwhile, wirelessly-controlled valves and pumps will coordinate reservoir releases to halt combined sewer overflows and restore water quality in urban streams. While these technologies promise to transform the field of water resources engineering, considerable knowledge gaps remain with regards to how smart water systems should be designed and operated. This dissertation presents foundational work towards building the smart water systems of the future, with a particular focus on applications to urban flooding. First, I introduce a first-of-its-kind embedded platform for real-time sensing and control of stormwater systems that will enable emergency managers to detect and respond to urban flood events in real-time. Next, I introduce new methods for hydrologic data assimilation that will enable real-time geolocation of floods and water quality hazards. Finally, I present theoretical contributions to the problem of controller placement in hydraulic networks that will help guide the design of future decentralized flood control systems. Taken together, these contributions pave the way for adaptive stormwater infrastructure that will mitigate the impacts of urban flooding through real-time response.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163144/1/mdbartos_1.pd
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Enabling Resilience in Cyber-Physical-Human Water Infrastructures
Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments
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