Bruno S. Faiçal

Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition

Currently, there is an increasing number of patients that are treated in-home, mainly in countries such as Japan, USA and Europe. As well as this, the number of elderly people has increased significantly in the last 15 years and these people are often treated in-home and at times enter into a critical situation that may require help (e.g. when facing an accident, or becoming depressed). Advances in ubiquitous computing and the Internet of Things (IoT) have provided efficient and cheap equipments that include wireless communication and cameras, such as smartphones or embedded devices like Raspberry Pi. Embedded computing enables the deployment of Health Smart Homes (HSH) that can enhance in-home medical treatment. The use of camera and image processing on IoT is still an application that has not been fully explored in the literature, especially in the context of HSH. Although use of images has been widely exploited to address issues such as safety and surveillance in the house, they have been little employed to assist patients and/or elderly people as part of the home-care systems. In our view, these images can help nurses or caregivers to assist patients in need of timely help, and the implementation of this application can be extremely easy and cheap when aided by IoT technologies. This article discusses the use of patient images and emotional detection to assist patients and elderly people within an in-home healthcare context. We also discuss the existing literature and show that most of the studies in this area do not make use of images for the purpose of monitoring patients. In addition, there are few studies that take into account the patients emotional state, which is crucial for them to be able to recover from a disease. Finally, we outline our prototype which runs on multiple computing platforms and show results that demonstrate the feasibility of our approach.

The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides

The application of pesticides and fertilizers in agricultural areas is of crucial importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of their speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g., crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Weather conditions, such as the intensity and direction of the wind while spraying, add further complexity to the problem of maintaining control. In this paper, we describe an architecture to address the problem of self-adjustment of the UAV routes when spraying chemicals in a crop field. We propose and evaluate an algorithm to adjust the UAV route to changes in wind intensity and direction. The algorithm to adapt the path runs in the UAV and its input is the feedback obtained from the wireless sensor network (WSN) deployed in the crop field. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. The results show that the use of the feedback information from the sensors to make adjustments to the routes could significantly reduce the waste of pesticides and fertilizers.

Exploiting the use of unmanned aerial vehicles to provide resilience in wireless sensor networks

A wireless sensor network is liable to suffer faults for several reasons, which include faulty nodes or even the fact that nodes have been destroyed by a natural disaster, such as a flood. These faults can give rise to serious problems if WSNs do not have a reconfiguration mechanism at execution. It should be noted that many WSNs designed to detect natural disasters are deployed in inhospitable places and depend on multihop communication to allow the data to reach a sink node. As a result, a fault in a single node can leave a part of the system inoperable until the node recovers from this failure. In light of this, this article outlines a solution that entails employing unmanned aerial vehicles to reduce the problems arising from faults in a sensor network when monitoring natural disasters like floods and landslides. In the solution put forward, UAVs can be transported to the site of the disaster to mitigate problems caused by faults (e.g., by serving as routers or even acting as a data mule). Experiments conducted with real UAVs and with our WSN-based prototype for flood detection (already deployed in São Carlos, State of São Paulo, Brazil, have proven that this is a viable approach.

Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory

AbstractMonitoring natural environments is a challenging task on account of their hostile features. The use of wireless sensor networks (WSNs) for data collection is a feasible method since these domains lack any infrastructure. However, further studies are required to handle the data collected for a better modeling of behavior and thus make it possible to forecast impending disasters. In light of this, in this paper an analysis is conducted on the use of data gathered from urban rivers to forecast flooding with a view to reducing the damage it causes. The data were collected by means of a WSN in São Carlos, São Paulo State, Brazil, which gathered and processed data about the river level and rainfall by means of machine learning techniques and employing chaos theory to model the time series; this meant that the inputs of the machine learning technique were the time series gathered by the WSN modeled on the basis of the immersion theorem. The WSNs were deployed by our group in the city of São Carlos where there have been serious problems caused by floods. After the data interdependence had been established by the immersion theorem, the artificial neural networks were investigated to determine their degree of accuracy in the forecasting models.

Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields

The use of pesticides in agriculture is essential to maintain the quality of large-scale production. The spraying of these products by using aircraft speeds up the process and prevents compacting of the soil. However, adverse weather conditions (e.g. The speed and direction of the wind) can impair the effectiveness of the spraying of pesticides in a target crop field. Thus, there is a risk that the pesticide can drift to neighboring crop fields. It is believed that a large amount of all the pesticide used in the world drifts outside of the target crop field and only a small amount is effective in controlling pests. However, with increased precision in the spraying, it is possible to reduce the amount of pesticide used and improve the quality of agricultural products as well as mitigate the risk of environmental damage. With this objective, this paper proposes a methodology based on Particle Swarm Optimization (PSO) for the fine-tuning of control rules during the spraying of pesticides in crop fields. This methodology can be employed with speed and efficiency and achieve good results by taking account of the weather conditions reported by a Wireless Sensor Network (WSN). In this scenario, the UAV becomes a mobile node of the WSN that is able to make personalized decisions for each crop field. The experiments that were carried out show that the optimization methodology proposed is able to reduce the drift of pesticides by fine-tuning of control rules.

An Energy-Aware System for Decision-Making in a Residential Infrastructure Using Wireless Sensors and Actuators

This work proposes an intelligent decision system for a residential infrastructure based on wireless sensors and actuator networks, called ResiDI. ResiDI is equipped with battery-powered nodes to ensure that they are deployable anywhere in the house without the need for wiring, drilling or any pre-existing infrastructure. The key intelligence of ResiDI is distributed in the decider nodes, which are able to make decisions locally without the need to send traffic from the sensor nodes to the sink. The network intelligence core is based on a neural network that seeks to improve the accuracy of the decision-making, together with a temporal correlation mechanism that is targeted at reducing the energy consumption. When compared with an approach adopted in the literature, the results show that ResiDI is efficient in different scenarios in all evaluations performed.

Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields: An Approach for Dynamic Environments

Brazil is an agricultural nation whose process of spraying pesticides is mainly carried out by using aircrafts. However, the use of aircrafts with on-board pilots has often resulted in chemicals being sprayed outside the intended areas. The precision required for spraying on crop fields is often impaired by external factors, like changes in wind speed and direction. To address this problem, ensuring that the pesticides are sprayed accurately, this paper proposes the use of artificial neural networks (ANN) on programmable UAVs. For such, the UAV is programmed to spray chemicals on the target crop field considering dynamic context. To control the UAV ight route planning, we investigated several optimization techniques including Particle Swarm Optimization (PSO). We employ PSO to find near-optimal parameters for static environments and then train a neural network to interpolate PSO solutions in order to improve the UAV route in dynamic environments. Experimental results showed a gain in the spraying precisio...

An Accurate Flood Forecasting Model Using Wireless Sensor Networks and Chaos Theory: A Case Study with Real WSN Deployment in Brazil

Monitoring natural environments is a challenging task on account of their hostile features. The use of wireless sensor networks (WSN) for data collection is a viable method since these domains lack any infrastructure. Further studies are required to handle the data collected to provide a better modeling of behavior and make it possible to forecast impending disasters. These factors have led to this paper which conducts an analysis of the use of data gathered from urban rivers to forecast future flooding with a view to reducing the damage they cause. The data were collected by means of a WSN in Sao Carlos, Sao Paulo State, Brazil and were handled by employing the Immersion Theorem. The WSN were deployed by our group in the city of Sao Carlos due to numerous problems with floods. After discovering the data interdependence, artificial neural networks were employed to establish more accurate forecasting models.

Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation

The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost.

Enhancing reliability in Wireless Sensor Networks for adaptive river monitoring systems: Reflections on their long-term deployment in Brazil

Abstract Several adaptive systems have been proposed that are based on the concepts of smart cities, which can be successfully adapted to natural disasters or other public safety concerns. Since these systems are embedded in a critical and dynamic environment, it is really important to have an infrastructure that is capable of providing real-time environmental information. This paper discusses two research questions that arise from adaptive ubicomp systems: (i) what are the key requirements to provide a reliable WSN-based system (e.g. a river monitoring system)? and (ii) how can an adaptable and reliable WSN-based system be developed? This paper seeks to respond to the former question with the aid of the RESS standard platform. The latter question is answered by employing a generic approach for adaptation. The term “critical systems”, means that any error may result in the loss of human life. We devised the RESS standard after deploying the WSN-based river monitoring system in Brazil for five years. Our prototype underwent several trials, sometimes leading to failure or damage, before we came up with a more reliable solution, which is outlined in this article. Finally, while our RESS platform is policy-free, it is extensible/adaptable and hence can naturally be adapted to new policies.