Igor Leão dos Santos

WSNs clustering based on semantic neighborhood relationships

We propose a semantic clustering model based on a fuzzy inference system to find out the semantic neighborhood relationships in wireless sensor networks in order to both reduce energy consumption and improve the data accuracy. As a case study we describe a structural health monitoring application which was used to illustrate and assess the proposed model. We conduct experiments in order to evaluate the proposal in two different scenarios of damage with different data aggregation methods. We also compared our proposal, using the same data set, with a deterministic clustering method and with the LEACH algorithm. The results indicate that our approach is an energy-efficient clustering method for WSNs, outperforming both the deterministic clustering and LEACH algorithms in about 70% and 47% of energy savings respectively. The energy saving comes from the fact that we have a more efficient in-network data aggregation process since by exploiting the semantic relation between sensor nodes we can potentially aggregate more similar data and consequently, decrease the data redundancy (thus minimizing transmissions). Nodes that are semantically unrelated can operate in low-duty cycle, further reducing the energy consumption. Moreover, our proposal has the potential to improve the data accuracy provided for the application where accuracy is a QoS requirement in typical WSN applications.

System modelling and performance evaluation of a three-tier Cloud of Things

Abstract The emergent paradigm of fog computing advocates that the computational resources can be extended to the edge of the network, so that the transmission latency and bandwidth burden caused by cloud computing can be effectively reduced. Moreover, fog computing can support and facilitate some kinds of applications that do not cope well with some features of cloud computing, for instance, applications that require low and predictable latency, and geographically distributed applications. However, fog computing is not a substitute but instead a powerful complement to the cloud computing. This paper focuses on studying the interplay and cooperation between the edge (fog) and the core (cloud) in the context of the Internet of Things (IoT). We first propose a three-tier system architecture and mathematically characterize each tier in terms of energy consumption and latency. After that, simulations are performed to evaluate the system performance with and without the fog involvement. The simulation results show that the three-tier system outperforms the two-tier system in terms of the assessed metrics.

A localized algorithm for Structural Health Monitoring using wireless sensor networks

Structural Health Monitoring (SHM) has been proving to be a suitable application domain for wireless sensor networks, whose techniques attempt to autonomously evaluate the integrity of structures, occasionally aiming at detecting and localizing damage. In this paper, we propose a localized algorithm supported by multilevel information fusion techniques to enable detection, localization and extent determination of damage sites using the resource constrained environment of a wireless sensor network. Each node partakes in different network tasks and has a localized view of the whole situation, so collaboration mechanisms and multilevel information fusion techniques are key components of this proposal to efficiently achieve its goal. Experimental results with the MICAz mote platform showed that the algorithm performs well in terms of network resources utilization.

A Control and Decision System for Smart Buildings

The employment of a control and decision process supported by wireless sensor and actuator networks (WSANs) is a promising way to improve energy efficiency of Smart Buildings. We present and evaluate CONDE, a decentralized system for decision and control for Smart Building applications based on WSANs. Performed experiments shown that since data is processed within the network instead of transmitted to a central location, there is a gain in terms of the system response time and resource consumption. Therefore, CONDE improves the energy efficiency of both the monitored building and the WSAN infrastructure, when compared to centralized approaches. Moreover, it exploits integration of applications at the decision level to further improve the system efficiency.

A Decentralized Damage Detection System for Wireless Sensor and Actuator Networks

The unprecedented capabilities of monitoring and responding to stimuli in the physical world of wireless sensor and actuator networks (WSAN) enable these networks to provide the underpinning for several Smart City applications, such as structural health monitoring (SHM). In such applications, civil structures, endowed with wireless smart devices, are able to self-monitor and autonomously respond to situations using computational intelligence. This work presents a decentralized algorithm for detecting damage in structures by using a WSAN. As key characteristics, beyond presenting a fully decentralized (in-network) and collaborative approach for detecting damage in structures, our algorithm makes use of cooperative information fusion for calculating a damage coefficient. We conducted experiments for evaluating the algorithm in terms of its accuracy and efficient use of the constrained WSAN resources. We found that our collaborative and information fusion-based approach ensures the accuracy of our algorithm and that it can answer promptly to stimuli (1.091 s), triggering actuators. Moreover, for 100 nodes or less in the WSAN, the communication overhead of our algorithm is tolerable and the WSAN running our algorithm, operating system and protocols can last as long as 468 days.

Semantic Clustering in Wireless Sensor Networks

Wireless Sensor Networks have critical resource constraints and minimizing resources usage is crucial to extend the network lifetime. Energy saving in WSNs can be achieved through several techniques, such as topology control and clustering, to provide a longer lifetime and scalability to the network. In this paper we propose a semantic clustering model based on a fuzzy inference system to find out the semantic neighborhood relationships in the network. As a case study we describe the structural health monitoring domain application which has been used to illustrate and verify the proposed model.

COMFIT: A development environment for the Internet of Things

Abstract This paper presents COMFIT (Cloud and Model based IDE for the Internet of Things), a development environment for the Internet of Things that was built grounded on the paradigms of model driven development and cloud computing. COMFIT is composed of two different modules: (1) the App Development Module, a model-driven architecture (MDA) infrastructure, and (2) the App Management and Execution Module, a module that contains cloud-based web interface connected to a server hosted in the cloud with compilers and simulators for developing Internet of Things (IoT) applications. The App Development Module allows the developers to design IoT applications using high abstraction artifacts (models), which are tailored to either the application perspective or the network perspective, thus creating a separation between these two concerns. As models can be automatically transformed into code through the App Development Module, COMFIT creates an environment where there is no need of additional configurations to properly compile or simulate the generated code, integrating the development lifecycle of IoT applications into a single environment partially hosted in the client side and partially in the cloud. In its current version, COMFIT supports two operating systems, namely Contiki and TinyOS, which are widely used in IoT devices. COMFIT supports automatic code generation, execution of simulations, and code compilation of applications for these platforms with low development effort. Finally, COMFIT is able to interact with IoT-lab, an open testbed for IoT applications, which allows the developers to test their applications with different configurations without the need of using local IoT devices. Several evaluations were performed to assess COMFIT’s key features in terms of development effort, quality of generated code, and scalability.

Damage prediction for wind turbines using wireless sensor and actuator networks

The depletion of oil and gas reserves is bringing up economic, political and social issues which encourage the adoption of renewable, green energy sources. Wind energy is a major source of renewable energy because of the maturity and competitive costs of technological solutions to exploit this type of green energy. This kind of power generation is achieved through the use of wind turbines, which convert translational kinetic energy into rotational kinetic energy. The benefits already proven of this type of renewable energy source have motivated nations worldwide to adopt policies to improve the use of wind energy in order to minimize their dependence on oil and natural gas. However, the adoption of wind turbines poses several challenges. A key challenge is properly and timely identifying structural damages which affect the structural health of the wind turbine. In this context, we propose a damage prediction system for wind turbines based on wireless sensor and actuator network. The proposed system, called Delphos, is a decentralized system where all decision-making process is performed within the network, in a collaborative way by the nodes. The purpose of Delphos is to accurately predict when the turbine will reach a damage state, thus allowing timely actions on the turbine operation to prevent accidents, reducing maintenance costs and delays in the power generation. Delphos relies on a time series forecasting model, called ARIMA, and a fuzzy system to eliminate the influence of temperature in the process of damage prediction.

Ensuring energy efficiency of power quality applications in smart grids through a framework based on Wireless Sensor and Actuator Networks

This paper presents FraSEE, a framework whose goal is to increase the energy efficiency (EE) of smart grid applications, specifically for the Power Quality (PQ) application domain. The key idea is to use a Wireless Sensor and Actuator Network (WSAN) and decentralize the decision processes of such applications among WSAN nodes through information fusion. Experiments assessed FraSEE effectiveness in increasing the EE of a PQ application, its accuracy, i.e. correct PQ disturbance detections, considering the influence of environmental parameters in decision process, its overhead, i.e. WSAN resources consumption; and delay for performing control actions in response to decisions.

Web2Compile-CoT: A Web IDE for the Cloud of Things

This paper presents Web2Compile-CoT, a WebIDE for developing Cloud of Things CoT applications. The Web2Compile-CoT was built grounded on the paradigms of integrated development environments, based on web technology, and cloud computing. So it provides to the scientific community students and researchers an ubiquitous development environment that does not demand any configuration or download of applications to work properly, but requiring only updated Internet browsers. Web2compile-CoT works with Contiki and TinyOS sensor operating systems, and it is able to interact with IoT-lab, a sensor testbed for CoT applications. We evaluated Web2Compile-CoT in terms of System efficiency and effectiveness. With Web2Compile-CoT we can reduce the average time for development of an application in classrooms from four hours to 30i¾?min. In addition, due to IoT-lab integration, Web2Compile-CoT supports classrooms with more than 50 students executing experiments simultaneously.