Snapshots of the EYES project

The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It addresses the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This paper provides a broad overview of the EYES project and highlights some approaches and results of the architecture

Web-based platform for evaluation of RF-based indoor localization algorithms

The experimental efforts for optimizing the performance of RF-based indoor localization algorithms for specific environments and scenarios is time consuming and costly. In this work, we address this problem by providing a publicly accessible platform for streamlined experimental evaluation of RF-based indoor localization algorithms, without the need of a physical testbed infrastructure. We also offer an extensive set of raw measurements that can be used as input data for indoor localization algorithms. The datasets are collected in multiple testbed environments, with various densities of measurement points, using different measuring devices and in various scenarios with controlled RF interference. The platform encompasses two core services: one focused on storage and management of raw data, and one focused on automated calculation of metrics for performance characterization of localization algorithms. Tools for visualization of the raw data, as well as software libraries for convenient access to the platform from MATLAB and Python, are also offered. By contrasting its fidelity and usability with respect to remote experiments on dedicated physical testbed infrastructure, we show that the virtual platform produces comparative performance results while offering significant reduction in the complexity, time and labor overheads

OPCPP: An Online Plug-Configure-Play Experiment Platform for WSN

Wireless sensor network (WSN) experiment platforms are used for teaching, research, and development of WSN. However, existing WSN experiment platforms generally have the following disadvantages: tedious manual operations, invasive measurement method, poor sensor self-awareness, and low resource utilization rate. To address the above problems, this paper proposes OPCPP, an online Plug-Configure-Play experiment platform for WSN. It has four prominent strongpoints: in-application programming in batch, noninvasive measurement method, sensor self-awareness, and remote operation. OPCPP has been used in eight colleges in China for teaching course of “internet of things” till now. We also develop a sensor-aware ZigBee-based smart home system prototype based on OPCPP