Claudio M. de Farias

Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques

An IDS framework inspired in the Human Immune System to be applied in the wireless sensor network context is proposed. It uses an improved decentralized and customized version of the Dendritic Cell Algorithm, which allows nodes to monitor their neighborhood and collaborate to identify an intruder. The work was implemented and tested both in simulation and in real sensor platform scenarios, comparing them to each other and was also compared to a Negative Selection Theory implementation in order to demonstrate its efficiency in detecting a denial-of-sleep attack and in energy consumption. Results demonstrated the success of the proposal.

A Systematic Review of Shared Sensor Networks

While Wireless Sensor Networks (WSNs) have been traditionally tasked with single applications, in recent years we have witnessed the emergence of Shared Sensor Networks (SSNs) as integrated cyber-physical system infrastructures for a multitude of applications. Instead of assuming an application-specific network design, SSNs allow the underlying infrastructure to be shared among multiple applications that can potentially belong to different users. On one hand, a potential benefit of such a design approach is to increase the utilization of sensing and communication resources, whenever the underlying network infrastructure covers the same geographic area and the sensor nodes monitor the same physical variables of common interest for different applications. On the other hand, compared with the existing application-specific design, the SSNs approach poses several research challenges with regard to different aspects of WSNs. In this article, we present a systematic literature survey on SSNs. The main goal of the article is to provide the reader with the opportunity to understand what has been done and what remains as open issues in this field, as well as which are the pivotal factors of this evolutionary design and how this kind of design can be exploited by a wide range of WSN applications.

A scheduling algorithm for shared sensor and actuator networks

Shared Sensor and Actuator Networks (SSAN) represents a new design trend of Wireless Sensor Network (WSNs) that allows the system infrastructure to be shared among multiple applications submitted by different users instead of the original application-specific network design. In this context, we propose a task scheduling algorithm that well utilizes the characteristics of applications with common tasks to improve the system energy efficiency, so as to extend the most concerned performance metric system lifetime. Our proposal is validated by both computer simulations and real sensor nodes tests.

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.

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.

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.

Information fusion techniques applied to Shared Sensor and Actuator Networks

This work presents an adaptation (and enhancement) of a well-known fusion technique in order to deal with multiple applications simultaneously in a context of Shared Sensor and Actuator Networks (SSAN). SSAN allow the sensing infrastructure to be shared among multiple applications that can potentially belong to different users instead of assuming an application-specific network design. We also present simulation and tests conducted with the proposed solution on real nodes to validate our proposal.

A multisensor data fusion algorithm using the hidden correlations in Multiapplication Wireless Sensor data streams

While wireless sensor networks (WSNs) have been traditionally tasked with single applications, we have witnessed the emergence of multiapplication paradigms in the sensor network field such as Shared Sensor Networks and Virtual Sensor Networks. As the number of applications in a WSN increases, it also increases the WSN complexity and the amount of required transmitted messages. A major requirement in these networks is to save energy in order to extend their operational lifetime. Among the methods employed to extend network lifetime, Multisensor data fusion (MDF) is one of the most widely used. This technique can be very important when applied in a heterogeneous network (a common case for multiapplication WSNs). In a heterogeneous network data streams may come from very different contexts and may have distinct representations. Traditional MDFs are not able to identify these different contexts, since they are designed using an application-specific design for the network. In order to overcome this limitation, MDFs have to identify (hidden) correlations between sensors and to exploit such knowledge to monitor the behavior of sensors during their working life. Those correlations in a multiapplication environment could indicate that different applications may share similarities in terms of sensing that could be used in MDFs to achieve better results in terms of energy consumption and MDFs accuracy. In this paper, we propose a data fusion algorithm that identifies patterns in data streams generated by distinct applications in order to find the best correlations to apply MDFs. Our proposal is validated through simulations and tests on real nodes.

DISSN: A Dynamic Intrusion Detection System for Shared Sensor Networks

Recent years we have witnessed the emergence of Shared Sensor Networks (SSNs) as a core component of cyber-physical systems for diverse applications. As Wireless Sensor and Actuator Networks (WSANs) design starts shifting from application-specific platforms to shared system infrastructures, a new but pressing research challenge is security. In scenarios involving unprotected hostile outdoor areas, SSNs are prone to different types of attack which can compromise reliability, integrity and availability of the sensor data traffic and sensor lifetime as well. In this work we propose a dynamic resilient security framework to be applied in the shared sensor network context. Its basic feature is the nodes neighborhood monitoring and collaboration (through the use of the byzantine algorithm) to identify an attack and enhance security. The work was experimentally evaluated in order to demonstrate the efficiency of the proposed solution.