A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

Abstract

A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system.

Monte Carlo Optimization for Sliding Window Size in Dixon Quality Control of Environmental Monitoring Time Series Data

“…where rr is the rate of computation time increase, j is the number of windows (traverse calculation from 3 to 30), t j (n) is the run-time of FTSSSM, and t j (i) is the run-time of MCSM. Figure 3 shows the rate of computation time increase histogram calculated by Equation (2). It can be seen from Figure 3 and Table 4 that the time series of water quality factors with the highest rate of computation time increase was the Chl-a observation series.…”

“…The rapid development of the Internet of Things has promoted the application of smart sensors in the field of the environment, contributing to big data and the multi-dimension characteristics of environmental monitoring [1,2]. Outlier processing is critical in environmental data analysis owing to its significant effect on future analysis and modeling [3,4].…”

Synchronization of Separate Sensors’ Data Transferred through a Local Wi-Fi Network: A Use Case of Human-Gait Monitoring

“…There are three main challenges for environmental monitoring in buildings: optimizing the software for processing data from different types of sensors, data synchronization, and ensuring network stability when new sensors are connected or existing ones are removed. In [41], a platform and a software system for efficient data-acquisition collection, preprocessing, and packaging from various sensors were presented, simplifying the network's setup and ensuring its reliability. A gateway system developed for a wireless sensor network aimed at environmental monitoring is presented in [42].…”

Design and implementation of wireless sensor network for environmental monitoring

“…The sensors might also be used in a humid environment. Huang et al [10] proposed a userfriendly sophisticated software framework for monitoring real-time environmental data from buildings. A wireless node module and an application point module are the two major physical components of a WSN system.…”