An integrating platform for environmental monitoring in museums based on wireless sensor networks

Monitoring the museum’s environment for preventive conservation of art purposes is one major concern to all museums. In order to properly conserve the artwork it is critical to continuously measure some parameters, such as temperature, relative humidity, light and, also, pollutants, either in storage or exhibition rooms. The deployment of a Wireless Sensor Network in a museum can help implementing these measurements in real-time, continuously, and in a much easier and cheap way. In this paper, we present the first testbed deployed in an Contemporary Art Museum, located in Madeira Island, Portugal, and the preliminary results of these experiments. On the other hand, we propose a new wireless sensor node that offers some advantages when compared with several commercially available solutions. Furthermore, we present a system that automatically controls the dehumidifying devices, maintaining the humidity at more constant levels.info:eu-repo/semantics/publishedVersio

Automatic monitoring and control of museums’ environment based on Wireless Sensor Networks

In museums, it is critical to properly conserve the existing artwork. For this purpose, it is fundamental to continuously monitor its environment, either in storage or exhibition rooms. Contrarily to traditional measuring equipments and procedures used in museums, the deployment of a Wireless Sensor Network (WSN) can help to implement these measurements continuously, in a real-time basis, and in a much easier and cheaper way. This is the main objective of the WISE-MUSE project, which proposes the use of WSNs for museums’ environmental and structural monitoring, and automatic environmental control. In this paper, the implementation and the main results of the WISE-MUSE project, which was carried out in a contemporary art museum, are described. Among other important contributions that will also be described in this paper, the development of a new wireless sensor node and a Web-based visualization tool, which bring some considerable advantages when compared with other commercially available solutions, are emphasized.info:eu-repo/semantics/publishedVersio

A flexible high-density sensor network

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 171-174).This thesis explores building and deploying a scalable electronic sensate skin that was designed as a dense sensor network. Our skin is built from small (1" x 1") rigid circuit boards attached to their neighbors with flexible interconnects. Each boardcontained an embedded processor together with a suite of thirteen sensors, providing dense, multimodal capture of proximate and contact phenomena. In addition to the design of the physical system, this thesis develops protocols for internode communication (both neighbor-neighbor and global), and power-efficient wake-on-phenomena operation. The system was rigorously tested with an array of up to 4x3 nodes subject to a variety of sensor stimuli. Although there were some robustness issues in the final design (particularly in the wired interconnects, which were not the focus of this thesis work), the skin that we developed showed good flexibility for a prototype, ran quickly and efficiently, and could detect and respond to a variety of stimuli.by Behram Farrokh Thomas Mistree.M.Eng

Localizing a Sensor Network via Collaborative Processing of Global Stimuli

Abstract — In order for nodes in a sensor network to meaningfully correlate their sensor readings, they must first determine their position in a globally shared coordinate system. Though there are many approaches which are suitable for achieving localization in the general case, sensor nodes are uniquely suited to use their sensing capabilities to aid them in this task. Global events which are detected in the environment surrounding the sensor network can serve as points of correspondence which, through collaborative processing on the network, provide nodes with sufficient information to compute their position. We have implemented an algorithm based on this approach in the Pushpin Computing sensor network: a dense, 55 node network which is spread over an area of 0.5 square meters. By queuing off of the minimum number of ultrasound pulses and light flashes needed to determine 2D coordinates using a simple lateration approach, we show that nodes in the Pushpin network can compute their position with an average error of 5-cm and a error standard deviation of 3-cm. In this paper we present this localization system and characterize its accuracy in our hardware testbed. I