Iulia Stamatescu

Analytical and experimental sensor node energy modeling in ambient monitoring

Wireless sensor networks are currently being deployed over a vast array of applications to collect, process and reliably transmit data from a source event to a data sink. This in turn requires reliable power supply, mainly in the form of primary or secondary batteries storing energy to supply the various functional discrete components along with accurate prediction of sensor node life time, based on estimated state of charge of the energy source. The paper presents two approaches for energy modeling of sensor node life time in ambient monitoring scenarios concerned with periodical sampling of measurable parameters from the indoor environment. The analytical approach involves modeling the individual components of a sensor node and carrying out simulations in different duty-cycling regimes to obtain an expected life time. Experimental modeling has also been carried out over short and medium term repeatable deployments in order to gather meaningful sets of data suitable for statistical interpretation. Battery discharge characteristics are determined in order to extract high level information used in system design for indoor ambient monitoring. final goal is to incorporate validated experimental results back into simulation approaches and enable a prediction framework for the Memsic IRIS XM2110 motes in order to approach the direct and inverse problem: accurately determine node life time for various sampling and communication regimes or choose optimal configuration parameters for an imposed node life time.

Embedded networked monitoring and control for renewable energy storage systems

Energy storage plays an important role in managing effectively the integration of distributed renewable energy generation within the electrical networks of the future. Pervasive monitoring and control of these systems makes growing use of information and communication technologies, which aim at secure and economic operation at various scales from microgrids to system-wide integration. Among these, low power wireless communication and computing embedded systems, in the main form of wireless sensor networks (WSN), have become a robust solution. Through hardware and software architectures, along with appropriate mechanisms e.g. for data collection and aggregation, wireless communication protocols and standards, they represent a valid solution in assuring continuous and reliable operation. The paper introduces a cyber-physical framework for renewable storage systems monitoring and control and discusses the application of wireless sensor networks to densely instrument such deployments. A hardware-in-the-loop type structure is designed which allows both testing various types of real storage systems, as well as more complex simulated models derived from large scale applications. We argue that the specific advantages brought forward by the advances in WSN technology can be put to efficient use for local distributed intelligence and control. Experimental data collected is analyzed in order to achieve an insight into the characteristics of the proposed solution.

Towards cloud integration for industrial wireless sensor network systems

The paper discusses the advantages and challenges for a cloud-based system architecture for process monitoring and statistical analysis of network performance. The focus is on integrating industrial wireless sensor networks (IWSN), composed of low-power wireless field devices for sensing and actuation, with cloud infrastructures, enabling remote access over secure real-time communication channels. The proposed cloud integration solution implements RESTful services at a coordinator node level of the WSN, allowing the implementation of a scalable and more performant communication infrastructure. The overall system allows functionality for visualization, data storage and processing and distributed algorithms that can run across heterogeneous multi-level monitoring and control systems. We present a modeling and simulation approach for scalable IPv6-based industrial wireless sensor networks, highlight effective mechanisms for cloud interoperability and argument the feasibility of the proposed system.

Fuzzy decision support system for solar tracking optimization

This paper presents the design and implementation of a fuzzy decision support system for control of photovoltaic panel movement in order to improve the availability of solar energy and the systems total efficiency. The designed algorithm is implemented on a solar tracking experimental platform using a fuzzy logic control strategy. It makes use of measured values for radiation from appropriate sensors and assures command of the platforms two positioning motors. The solution was developed as a virtual instrument, using a graphical programming environment. This allows for fast deployment, versatility and scalability.

Open and closed loop simulation for predictive control of buildings

The paper presents the system modeling, controller design and numerical simulation results for thermal energy management of a real office building. Focus is set on an efficient and unitary approach which leads from detailed civil engineering specifications of the building elements to compact and effective models which are suitable for control. A modular semi-automated approach in used in order to derive the discrete state-space representation of the system model. This combines the key thermal dynamics of the constructions with a modular list of thermal loads and losses, defined as external heat fluxes. A balanced trade-off is thus achieved between model accuracy and complexity through a compact and effective representation of the plant dynamics. The control strategy is based on a predictive controller which evolves an optimized system input vector, in a closed loop. Paths for occupant feedback integration into a single framework, by using human-in-the-loop models via disturbance channels are also discussed.

Large scale heterogeneous monitoring system with decentralized sensor fusion

Monitoring large areas, critical infrastructure systems and surveillance operations require the design of advanced embedded systems which cooperate for achieving mission objectives within a decision support framework. As fundamental building blocks of this new paradigm, we focus on the integration at the communication and data processing levels of wireless sensor networks with unmanned aerial vehicles. WSNs are seen as collections of embedded computing and communication devices which are able to measure and report continuous and discrete values to a central server and allow dynamic interactions through dedicated middleware. At the same time, UAVs are a vital resource for rich field-level information such as picture and video stream capture along with great flexibility for deployment and operation. The paper discusses the general architecture of a large scale heterogeneous monitoring system and the application of decentralized sensor fusion mechanisms for efficient information extraction and data reduction. This promises significat impact on reducing the energy and communication constraints of the embedded sensing nodes. Experimental results are provided stemming from real-world deployment of the UAV platform and ground sensor network subsystems.

Sensor fusion method for altitude estimation in mini-UAV applications

The emergence of unmanned aerial vehicles for large scale monitoring of ground infrastructure has challenged new designs for electronic sensor systems capable of timely, accurate and compntationally effective measurements. A conventional design issue is balancing the complexity of the sensing subsystem with advanced data processing algorithms in order to obtain quality information regarding the airplanes posture and orientation. The paper presents the generic architecture of the navigation sensing system for a fixed-wing mini-UAV, at both the physical and algorithm levels. The focus is set on a sub-problem, namely altitude estimation by sensor fusion using barometric pressure and GPS data. Results are discussed based on actual flight log data and the approach is extended towards speed estimation: air, ground and wind influence. Conclusions are drawn regarding efficient implementation of the method in relation to the onboard embedded processing capabilities.

An analysis of security and communication constraints of IPv6-based Sensor Networks

Wireless Sensor Networks (WSN) offer a significant potential for ubiquitous monitoring and control in future internet-connected pervasive systems architectures. This can be achieved through seamless integration along existing networking devices and systems, while taking account the data-driven nature and security constraints imposed upon such low power, efficient embedded networked platforms. We argue that only through a combined security and communication approach can the relevant goals be achieved, by considering security issues as an integral part of high level layers of the protocol stack. This paper presents an analysis of security and communication constraints of IPv6-based wireless sensor networks at the network and transport layer. Leveraging the Contiki operating system for resource constrained devices along with the ContikiSec security layer and RPL, IPv6 Protocol for Low Power and Lossy Networks, helpful insight is achieved for evaluation and deployment. The results of the analysis are discussed and functions of its modules are explained. This opens up the path towards robust implementation in real world applications following comparative experimental work on simulated and emulated mote platforms.

Wireless system for occupancy modelling and prediction in smart buildings

The dense instrumentation of future smart buildings enables the implementation of advanced control techniques which are aimed at the dual objectives of energy efficiency/cost savings and occupant comfort. One of the essential functions and prerequisite consists of robust dynamic occupancy detection and prediction which allows improved estimation of active thermal zones and internal loads, as compared to static schedule-based approaches. The paper presents the design and preliminary evaluation of a wireless system for embedded monitoring of occupancy states across the thermal zones of the building. The challenges of calibration, detection and prediction algorithms are discussed along with experimental outcomes. The infrared array sensor within the system offers improved detection performance compared to conventional PIR sensors while preserving user privacy in comparison to image processing approaches using security cameras. Several advantages of the proposed solution are also highlighted such as the low cost, flexibility, scalability and integration towards the building-wide automation system.

Energy consumption forecasting using ARIMA and neural network models

Energy forecast is essential for a good planning of the electricity consumption as well as for the implementation of decision support systems which can lead the decision making process of energy system. Energy consumption time series prediction problems represent a difficult type of predictive modelling problem due to the existence of complex linear and non-linear patterns. This paper presents two approaches for energy consumption forecast: an autoregressive integrated moving average (ARIMA) model and a non-linear autoregressive neural network (NAR) model. The two models are deeply described and finally compared in order to evaluate their performance.