ZigBee Motes Research Articles

Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring.

Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.

Experimental characterisation of the off-body wireless channel at 2.4 GHz for dairy cows in barns and pastures

Wireless Sensor Networks (WSNs) provide promising applications in healthcare monitoring of dairy cows. After sensors measure the data in or on the cow’s body (temperature, position, leg movement), this information needs to be transmitted to the farm manager, enabling the evaluation of the health state of the cow. In this work, the off-body wireless channel between a node placed on the cow’s body and an access point positioned in the surroundings of the cows is characterised at 2.4GHz. This characterisation is of critical importance in the design of reliable WSNs operating in the industrial, scientific and medical (ISM) band (e.g., Wi-Fi, ZigBee, and Bluetooth). Two propagation environments were investigated: indoor (inside three barns) and outdoor (pasture). Large-scale fading, cow body shadowing, and temporal fading measurements were determined using ZigBee motes and spectrum analysis measurement. The path loss was well fitted by a one-slope log-normal model, the cow body shadowing values increased when the height of the transmitter and/or the receiver decreased, with a maximum value of 7dB, and the temporal fading due to the cow movement was well described by a Rician distribution in the considered environments. As an application, a network planning tool was used to optimise the number of access points, their locations, and their power inside the investigated barns based on the obtained off-body wireless channel characteristics. Power consumption analysis of the on-cow node was performed to estimate its battery lifetime, which is a key factor for successful WSN deployment.

Development of Physical Intrusion Detection System Using Wi-Fi / ZigBee RF Signals

Wi-Fi is a wireless networking technology that enables computers and other devices to communicate using a RF signal enabling a lot of advanced applications to provide services. Nowadays, Wi-Fi signal coverage is almost a must in any office building or small office home office (SOHO) environment. Home and office networking concept involves the use of wireless sensors/Wi-Fi routers around the house to control and detect signals in the home-setting. In this project, however, RF signals from Wi-Fi NIC and ZigBee motes received signal strength indicators (RSSI) were used as a base feed to our system testbed to detect the human presence inside an indoor home/office environment. Then, a controller is used to switch ON/OFF appliances like air conditioning, lights …etc. The system testbed doubles as a reliable alarm for physical intrusion detection to secure an indoor perimeter as demonstrated in the paper. This simple, yet cheap, approach to home/office networking promises to save energy and cost in large numbers. It can also be used in many industrial settings including control and security applications.

Modeling of Current Consumption in 802.15.4/ZigBee Sensor Motes

Battery consumption is a key aspect in the performance of wireless sensor networks. One of the most promising technologies for this type of networks is 802.15.4/ZigBee. This paper presents an empirical characterization of battery consumption in commercial 802.15.4/ZigBee motes. This characterization is based on the measurement of the current that is drained from the power source under different 802.15.4 communication operations. The measurements permit the definition of an analytical model to predict the maximum, minimum and mean expected battery lifetime of a sensor networking application as a function of the sensor duty cycle and the size of the sensed data.

Testing ZigBee Motes for Monitoring Refrigerated Vegetable Transportation under Real Conditions

Quality control and monitoring of perishable goods during transportation and delivery services is an increasing concern for producers, suppliers, transport decision makers and consumers. The major challenge is to ensure a continuous ‘cold chain’ from producer to consumer in order to guaranty prime condition of goods. In this framework, the suitability of ZigBee protocol for monitoring refrigerated transportation has been proposed by several authors. However, up to date there was not any experimental work performed under real conditions. Thus, the main objective of our experiment was to test wireless sensor motes based in the ZigBee/IEEE 802.15.4 protocol during a real shipment. The experiment was conducted in a refrigerated truck traveling through two countries (Spain and France) which means a journey of 1,051 kilometers. The paper illustrates the great potential of this type of motes, providing information about several parameters such as temperature, relative humidity, door openings and truck stops. Psychrometric charts have also been developed for improving the knowledge about water loss and condensation on the product during shipments.

MODELING PSYCHROMETRIC DATA IN REAL-TIME FRUIT LOGISTICS MONITORING

Progress in fruit logistics requires an increasing number of measurements to be performed in refrigerated chambers and during transport. Fruits and vegetables are submitted to a variety of risks during transport and storage that are responsible for material quality losses. Among them water loss and condensation are causes of deterioration that reduces the marketability of fresh fruits and vegetables. Wireless sensor networks (WSN) are a promising solution in monitoring food logistics. Instrumented with sensors, such as temperature and humidity, this technology allows on-line supply chain monitoring of perishable food products. Psychrometry studies the thermodynamic properties of moist air and the use of these properties to analyze conditions and processes involving moist air. Using the information provided by the sensors, psychrometric equations can be used for quick assessment of changes in the absolute water content of air, allowing estimation of future water loss and detection of condensation. In this paper the psychrometric data from ASABE (American Society of Agricultural and Biological Engineers) has been applied, for modeling evaporation and condensation of water related with regard to product in a refrigerated chamber. The experiments were conducted in a commercial wholesaler store in the fruit and vegetables wholesalers market of Madrid. Two different types of IEEE 802.15.4/ ZigBee motes have been used.