Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US

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

IoT-inspired Framework for Real-time Prediction of Forest Fire

Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS

An approach of differential capacitor sensor for landslide monitoring

A lot of lives and properties are lost in every year all over the world, due to various geological catastrophes, landslide or land - slip is among one of them. Nowadays, both manual and electronic monitoring systems ar e used to predic t the landslide . The manual monitoring system is laborious, has many limitations and most of the cases it is not practicable. Conversely, most of the electronic systems are complex and expensive. Most of the natural calamities occur without prior notice as a result, it damages the monitoring instrument as well. The monitoring sensor system should be planned as a spread network with a simple positional identification device such as RFID; hence the system can send the real time data without interrupt. In addi tion, the network should have a self - recovery, self - directed operation and actual data transmission facility in a critical situation. A distributed node network needs a lot of sensors with complex structure and it is expensive too. This paper describes a s imple and low cost system which comprises an underground pretension cable with a capacitor gauge sensor attached at one end. A wireless sensor network has been proposed for a simple landslide monitoring system using RFID. The sensing node network can opera te by initializing mode, measuring mode and urgent mode. The system is able to select automatically any one of the operating mode depending on the situation , which makes it a robust and dynamic control of real time data transmission system. A mathematical model has been developed for the system and verified by simulation. The result shows that an early prediction of the landslide is possible by using the proposed system

ITIKI: Bridge between African indigenous knowledge and modern science on drought prediction

The now more rampant and severe droughts have become synonymous with Sub-Saharan Africa; they are a major contributor to the acute food insecurity in the Region. Though this scenario may be replicated in other regions in the globe, the uniqueness of the problem in Sub-Saharan Africa is to be found in the ineffectiveness of the drought monitoring and predicting tools in use in these countries. Here, resource-challenged National Meteorological Services are tasked with drought monitoring responsibility. The main form of forecasts is the Seasonal Climate Forecasts whose utilisation by small-scale farmers is below par; they instead consult their Indigenous Knowledge Forecasts. This is partly because the earlier are too supply-driven, too ""coarse"" to have meaning at the local level and their dissemination channels are ineffective. Indigenous Knowledge Forecasts are under serious threat from events such as climate variations and ""modernisation""; blending it with the scientific forecasts can mitigate some of this. Conversely, incorporating Indigenous Knowledge Forecasts into the Seasonal Climate Forecasts will improve its relevance (cultural and local) and acceptability, hence boosting its utilisation among small-scale farmers. The advantages of such a mutual symbiosis relationship between these two forecasting systems can be accelerated using ICTs. This is the thrust of this research: a novel drought-monitoring and predicting solution that is designed to work within the unique context of small-scale farmers in Sub-Saharan Africa. The research started off by designing a novel integration framework that creates the much-needed bridge (itiki) between Indigenous Knowledge Forecasts and Seasonal Climate Forecasts. The Framework was then converted into a sustainable, relevant and acceptable Drought Early Warning System prototype that uses mobile phones as input/output devices and wireless sensor-based weather meters to complement the weather stations. This was then deployed in Mbeere and Bunyore regions in Kenya. The complexity of the resulting system was enormous and to ensure that these myriad parts worked together, artificial intelligence technologies were employed: artificial neural networks to develop forecast models with accuracies of 70% to 98% for lead-times of 1 day to 4 years; fuzzy logic to store and manipulate the holistic indigenous knowledge; and intelligent agents for linking the prototype modules

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

Continuous monitoring of tree responses to climate change for smart forestry: a cybernetic web of trees

6openBothTrees are long-lived organisms that contribute to forest development over centuries and beyond. However, trees are vulnerable to increasing natural and anthropic disturbances. Spatially distributed, continuous data are required to predict mortality risk and impact on the fate of forest ecosystems. In order to enable monitoring over sensitive and often remote forest areas that cannot be patrolled regularly, early warning tools/platforms of mortality risk need to be established across regions. Although remote sensing tools are good at detecting change once it has occurred, early warning tools require ecophysiological information that is more easily collected from single trees on the ground. Here, we discuss the requirements for developing and implementing such a treebased platform to collect and transmit ecophysiological forest observations and environmental measurements from representative forest sites, where the goals are to identify and to monitor ecological tipping points for rapid forest decline. Long-term monitoring of forest research plots will contribute to better understanding of disturbance and the conditions that precede it. International networks of these sites will provide a regional view of susceptibility and impacts and would play an important role in ground-truthing remotely sensed data.openTognetti, Roberto; Valentini, Riccardo; Belelli Marchesini, Luca; Gianelle, Damiano; Panzacchi, Pietro; Marshall, John D.Tognetti, R.; Valentini, R.; Belelli Marchesini, L.; Gianelle, D.; Panzacchi, P.; Marshall, J.D