Fábio Januário

A distributed multi-agent approach for resilient supervision over a IPv6 WSAN infrastructure

Wireless Sensor and Actuator Networks has become an important area of research. They can provide flexibility, low operational and maintenance costs and they are inherently scalable. In the realm of Internet of Things the majority of devices is able to communicate with one another, and in some cases they can be deployed with an IP address. This feature is undoubtedly very beneficial in wireless sensor and actuator networks applications, such as monitoring and control systems. However, this kind of communication infrastructure is rather challenging as it can compromise the overall system performance due to several factors, namely outliers, intermittent communication breakdown or security issues. In order to improve the overall resilience of the system, this work proposes a distributed hierarchical multi-agent architecture implemented over a IPv6 communication infrastructure. The Contiki Operating System and RPL routing protocol were used together to provide a IPv6 based communication between nodes and an external network. Experimental results collected from a laboratory IPv6 based WSAN test-bed, show the relevance and benefits of the proposed methodology to cope with communication loss between nodes and the server.

A machine learning technique in a multi-agent framework for online outliers detection in Wireless Sensor Networks

Wireless Sensor Networks enable flexibility, low operational and maintenance costs, as well as scalability in a variety of scenarios. However, in the context of industrial monitoring scenarios the use of Wireless Sensor Networks can compromise the systems performance due to several factors, being one of them the presence of outliers in raw data. In order to improve the overall systems resilience, this paper proposes a distributed hierarchical multi-agent architecture where each agent is responsible for a specific task. This paper deals with online detection and accommodation of outliers in non-stationary time-series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine along with a sliding window-based learning algorithm. A modification to this method is considered to improve its performance in transient raw data collected from transmitters over a Wireless Sensor Networks (WSNs). An empirical study based on laboratory test-bed show the feasibility and relevance of incorporating the proposed methodology in the context of monitoring systems over Wireless Sensor Networks.

Detection and accommodation of outliers in Wireless Sensor Networks within a multi-agent framework

Graphical abstractDisplay Omitted HighlightsInvestigated three techniques for outliers detection in Wireless Sensor Networks.A Least Squares-Support Vector Machine-based technique with a sliding window-based learning.Principal Component Analysis method along with subspace tracking with rank-1 modification.Univariate statistics-based scheme within an oversampling environment.All methods are implemented within a hierarchical multi-agent framework. This paper studies three techniques for outliers detection in the context of Wireless Sensor Networks, including a machine learning technique, a Principal Component Analysis-based methodology and an univariate statistics-based approach. The first methodology is based on a Least Squares-Support Vector Machine technique, together with a sliding window learning. A modification to this approach is also considered in order to improve its performance in non-stationary time-series. The second methodology relies on Principal Component Analysis, along with the robust orthonormal projection approximation subspace tracking with rank-1 modification, while the last approach is based on univariate statistics within an oversampling mechanism. All methods are implemented under a hierarchical multi-agent framework and compared through experiments carried out on a test-bed.

Design of Robust CMOS Amplifiers Combining Advanced Low-Voltage and Feedback Techniques

This paper describes and tries to demystify the use of different low-voltage operation devices, such as dynamic threshold MOS transistors (DTMOS) with feedback techniques such as regulated-feedforward (RFF) and self-biasing (SB). Traditionally, DTMOS devices are only used when nominal supply voltages below 0.7 V are envisaged. Moreover, RFF and SB techniques are normally avoided since engineers designing high-performance amplifiers are afraid of additional stability concerns. This work demonstrates, through exhaustive simulation results over process, temperature and supply (PVT) corners using a standard 130 nm 1.2 V CMOS technology that, employing DTMOS in some specific devices can improve some performance parameters such as the open-loop low-frequency gain and, simultaneously, reduce significantly the variability over PVT corners. Moreover, it is also demonstrated that, there is no risk of operating at supply voltages higher than 1.2 V. Combining DTMOS with RFF and SB allows achieving reasonable gain-bandwidth products (GBW) even operating at low-voltage (down to 0.7 V), together averaged power savings of the order of 8% and highly simplifies the design of the circuit (since no biasing circuitry is required).

A Multi-Agent Framework for Resilient Enhancement in Networked Control Systems

Recent advances on the integration of control systems with state of the art information technologies have brought into play new uncertainties, not only associated with the physical world, but also from a cyber-spaces perspective. In cyber-physical environments, awareness and resilience are invaluable properties. The paper focuses on the development of an architecture relying on a hierarchical multi-agent framework for resilience enhancement. This framework was evaluated on a test-bed comprising several distributed computational devices and heterogeneous communications. Results from tests prove the relevance of the proposed approach.

Security challenges in SCADA systems over Wireless Sensor and Actuator Networks

Supervisory Control And Data Acquisition (SCADA) systems are currently an integrating part of most critical industrial infrastructures, for which security is paramount. Given to their increasing integration with corporate networks, which the industry 4.0 is the most recent driving force, security to cyber attacks can no longer rely on the principle of isolation. In this context, the present paper intends to shed some light on the subject by addressing the problem from each SCADA system components, including its interaction with Wireless Sensor and Actuator Networks (WSANs). The problem of resilience of WSANs is here discussed by appealing to a multi-agent based approach within a distributed middleware framework.

A General Distributed Architecture for Resilient Monitoring over Heterogeneous Networks

The growing developments on networked devices, with different communication structures and capabilities made possible the emergence of new architectures for monitoring systems. In the case of heterogeneous distributed environments, where knowledge, processing devices, sensors and actuators are distributed throughout the network, the design of such systems are challenging in terms of integration and, markedly, in security and state-awareness of the overall system. This work proposes a general distributed architecture, with supporting methods, for building a resilient monitoring system that can adaptively accommodate both cyber and physical anomalies. Its implementation relies on multi-agent systems within a distributed middleware.