Grigore Stamatescu

Evaluation of wireless sensor network monitoring for indoor spaces

Small-scale, medium-term deployments are an important tool when planning to install a wireless sensor network (WSN) monitoring system. It is vital to assess the software and hardware reliability of the desired solution in a well specified scenario. Also, it allows the system designer to evaluate beforehand the radio environment, a critical task in crowded environments such as buildings. The multitude of walls and large metal objects can produce unwanted effects such as fading and multi-path reflections while high powered radio devices produce interference when operating in the same band as the sensor nodes. This leads to a high packet loss rate and affect battery life through increased number of packet retransmissions. In this paper we report our results from a week long continues monitoring of an indoor space. Our focus is on sampling data for temperature, humidity, barometric pressure and ambient light. We analyze the results and provide experience regarding future real systems aimed at high spatial resolution measurements of environmental parameters indoors. Our study concerns sensor data, energy consumption of sensor nodes and radio channel characterization. A network sniffer is also employed to provide insight into the behaviour of the wireless mesh networking protocol. We consider that the relevance of our work can be highlighted in saved costs associated with maintenance operations and man-hours after a final, large-scale deployment of WSN instrumentation has already taken place.

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.

Sliding-mode real-time mobile platform control in the presence of uncertainties

In this paper, a new sliding-mode trajectory-tracking wheeled mobile robots (WMR) control is proposed. Dynamic model with uncertainties parameters (unknown or time varying mass and moment of inertia) of the WMR is taken into account. Robust stability depending on upper bound uncertainties, is guaranteed. A mobile platform, PatrolBot, with two driving wheels and two rear wheels is used in order to check proposed sliding-mode control. Closed-loop real-time control results are supplied. Good trajectory-tracking performances (small position and orientation errors) in the presence of external disturbances are obtained.

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.

Cognitive radio as solution for ground-aerial surveillance through WSN and UAV infrastructure

Intelligent collaborative environments, where heterogenous entities operate together in achieving common mission objectives have been increasingly adopted for monitoring and surveillance of interest areas and physical infrastructures. They can be assembled from multiple existing technologies ranging from wireless sensor networks (WSN), terrestrial remote operated vehicles (ROV) and unmanned aerial vehicles (UAV). In this context, we first introduce a multi-level system framework for multi-sensory robotic surveillance of critical infrastructure protection through communication, data acquisition and processing - MUROS. Leveraging a cognitive radio (CR) scheme is discussed as key point of the paper, arguing that by exploiting in an opportunistic fashion the time, frequency and spatial stream of the wireless environment, increased communication reliability can be achieved with positive impact on the availability and service level at each hierarchical level. The application of CR, given the heterogeneous nature of the application across multiple radio interfaces and protocols, stand outs as a novel and feasible research direction. We argument the advantages of this scheme within the constraints of a working scenario and define a simulation-based approach in order to validate our 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.

A mobile sensor network based road surface monitoring system

This paper presents a road surface defect identification system based on 3D accelerometers, GPS and video modules deployed on vehicles. The mobile platform architecture and the central data aggregation algorithm are also discussed. Because the mobile system is deployed over a large outdoor area, we also present a solution for the wireless communication coverage problem. Finally, we are highlighting the importance of the gathered information by making it available for the users using a GIS platform.

Wireless sensor network based on multilevel femtocells for home monitoring

An intelligent femtocell-based sensor network is proposed for home monitoring elderly or people with chronic diseases. The femtocell is considered as a small sensor network which is placed into patient house and consists on both mobile and fixed sensors disposed on three layers. The first layer contains body sensors attached to the patient that monitor different health parameters, patient location, position and possible falls. The second layer is dedicated for ambient sensors and routing inside the cell. The third layer contains emergency ambient sensors that cover burglary events or toxic gas concentration, distributed by necessities. Cell implementation is based on IRIS family of motes. In order to reduce energy consumption and radiation level, adaptive rates of acquisition and communication are used. Experimental results on body sensors and ambient ones are presented in the last section.

Real-Time Sliding-Mode Adaptive Control of a Mobile Platform Wheeled Mobile Robot

Abstract This paper presents parameter identification and discrete-time adaptive sliding-mode controller applied to control the mobile platform Pioneer 3-DX wheeled mobile robot (WMR) with 5-DOF manipulator. The dynamic model for mobile robot with two driving wheels, time–varying mass and moment of inertia have been used in sliding-mode control. Two closed-loop, on-line parameter estimators have been used in order to achieve robustness against parameter uncertainties (robot mass and moment of inertia). Two sliding-mode adaptive controllers have been designed corresponding to angular and linear motion. This paper presents closed-loop, circular trajectory tracking real-time control for the mobile robot Pioneer 3-DX.

Zone-level agreement by consensus for building thermal energy management

The paper addresses the problem of distributed agreement for control of building thermal energy through a class of consensus algorithms. The context of the work is defined by the emergence of dense networked measurement and control systems and the cyberphysical paradigm in real world building energy management applications. This has occurred more saliently by means of wireless sensor network systems, offering integration of sensing, computing and actuation capabilities. Key point is mitigation of current model- and occupancy-based strategies in order to increase building energy efficiency under a unifying framework. We consider both convergence and convergence speed conditions and relate our approach to the state-of-the-art. Simulation results are presented on a reference multiple thermal zone model.