ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

Owing to the increase in the number of people with disabilities, as a result of either accidents or old age, there has been an increase in research studies in the area of ubiquitous computing and the Internet of Things. They are aimed at monitoring health, in an efficient and easily accessible way, as a means of managing and improving the quality of life of this section of the public. It also involves adopting a Health Homes policy based on the Internet of Things and applied in smart home environments. This is aimed at providing connectivity between the patients and their surroundings and includes mechanisms for helping the diagnosis and prevention of accidents and/or diseases. Monitoring gives rise to an opportunity to exploit the way computational systems can help to determine the real-time emotional state of patients. This is necessary because there are some limitations to traditional methods of health monitoring, for example, establishing the behavior of the user’s routine and issuing alerts and warnings to family members and/or medical staff about any abnormal event or signs of the onset of depression. This article discusses how a layer-based architecture can be used to detect emotional factors to assist in healthcare and the prevention of accidents within the context of Smart Home Health. The results show that this process-based architecture allows a load distribution with a better service that takes into account the complexity of each algorithm and the processing power of each layer of the architecture to provide a prompt response when there is a need for some intervention in the emotional state of the user

Evaluating the impact of the number of access points in mobile robots localization using artificial neural networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space

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\ud 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\ud that were carried out show that the optimization methodology proposed is able to reduce the drift of pesticides by fine-tuning of control rules.FAPESP (processes ID 2012/22550-0)Office of Naval Research Global (No. 62909-14-1-N241)CAPES (Capes Foundation, Ministry of Education of Brazil)CNPq (Brazilian National Counsel of Technological and Scientific Development

An energy efficient joint localization and synchronization solution for wireless sensor networks using unmanned aerial vehicle

Localization and synchronization are fundamental services for many applications in wireless sensor networks (WSNs), since it is often required to know the sensor nodes’ position and global time to relate a given event detection to a specific location and time. However, the localization and synchronization tasks are often performed after the sensor nodes’ deployment on the sensor field. Since manual configuration of sensor nodes is usually an impractical activity, it is necessary to rely on specific algorithms to solve both localization and clock synchronization problems of sensor nodes. With this in mind, in this work we propose a joint solution for the problem of 3D localization and time synchronization in WSNs using an unmanned aerial vehicle (UAV). A UAV equipped with GPS flies over the sensor field broadcasting its geographical position. Therefore, sensor nodes are able to estimate their geographical position and global time without the need of equipping them with a GPS device. Through simulation experiments, we show that our proposed joint solution reduces time synchronization and localization errors as well as energy consumption when compared to solutions found in the literature.FAPESP (processes 2012/22550-0 and 2013/05403-66)CNPq, CAPES, FAPEMIG (process APQ-01947-12)Natural Sciences and Engineering Research Council of Canada (NSERC

AGORA-GeoDash: a geosensor dashboard for real-time flood risk monitoring

Flood management is an important approach to reduce damage caused by floods. In this context, technological \ud architectures which work in real-time are needed. However, Brazil has faced many structural difficulties in \ud obtaining updated information on the current state of its rivers. To address this problem, this paper outlines a \ud geosensor dashboard called AGORA-GeoDash, which processes data streams from wireless sensor networks \ud and makes them available in the form of a set of performance indicators that are essential to support real-time \ud decision-making in flood risk monitoring. The dashboard was built on open-source frameworks, made use of \ud geoservices that comply with the standards of Open Geospatial Consortium, and established a Wireless Sensor \ud Network which monitors the rivers of São Carlos/SP in Brazil. The analysis of the indicators available in two \ud rainfall events revealed that the dashboard can provide the key information required for the decision-making \ud process involved in flood risk managementFAPESP processos n. 2008/58161-1, 2011/23274-3, 2012/18675-1, 2012/22550-0CNPq processo n. 307637/2012-3FINEP (MAPLU) 01.10.0701.0

A practical evaluation of smartphone application on mesh networks

This paper presents a mesh architecture proposal\ud called Mobile mEsh Network to Aid in CountEring\ud drug TRAffiCKing (M.E.N.A.C.E-TRACK). This project was\ud born from the hypothesis we could establish a covert network\ud channel independent of the cell phone companies infrastructures.\ud Therefore, law enforcement agencies could establish\ud connection with field personnel, in a fault tolerant fashion\ud allowing the transmission of multimedia data (instead of only\ud voice). The main contribution for this paper is the strategies\ud involved to configure smartphones on the MANET side of\ud this system. We present the main difficulties and one possible\ud solution to implement ad hoc mode on our testbed so we can\ud enable a MANET organization on M.E.N.A.C.E-TRACK.FAPEG (número edital 006/2012

Exploiting smart contracts in PBFT-based blockchains: A case study in medical prescription system

Smart contracts allow application developers to automate business processes through a decentralized computation architecture. Contemporary blockchain platforms such as Ethereum and Hyperledger Fabric offer support for smart contracts through consensus mechanisms such as Proof-of-Work (PoW) or other types of transaction validation and ordering services. This article exploits smart contracts in the Byzantine Fault Tolerant (BFT) blockchain platforms. In particular, we explore Tendermint and Hyperledger Besu, BFT blockchain platforms, and apply them to a decentralized e-prescription case study to evaluate their effectiveness. We adopt Hyperledger Besu and Tendermint in this research, given that both are BFT-based blockchains. Also, it is noteworthy that smart contracts in BFT blockchain platforms such as Tendermint are not well established and not widely adopted yet. Our article empirically evaluates the performance of smart contracts in Tendermint and Hyperledger Besu using a decentralized medical prescription case study and compares their results with Ethereum, a PoW blockchain. Our results demonstrate that BFT blockchain platforms are efficient for multistakeholder applications such as e-prescription and supply chains. To the best of our knowledge, this is the first study investigating the implementation of smart contracts in BFT blockchain platforms, such as Tendermint and Hyperledger Besu