XBee S2C Research Articles

RSSI Measurement Analysis of Zigbee-Based Wireless Sensor Networks in Various Topologies for Solar Panel Monitoring

Abstract— Solar panels require performance monitoring when energy shortages occur in the area. Monitoring solar panels requires a detection device to detect solar panels, including the ACS712 current sensor for monitoring the output current of the solar panel, a DC voltage sensor for monitoring battery performance, and a DHT11 temperature sensor for monitoring the temperature of the solar panel storage battery. The WSN technology that can work is ZigBee communication. This ZigBee communication research uses various topology methods, including star, tree, and mesh topologies. Solar panel monitoring tests are carried out indoors and outdoors. The ZigBee communication device used is the XBee S2C device, which operates at a frequency of 2.4 GHz. From a comparison of various topologies, the mesh topology is a topology with good performance because it has the characteristic of choosing a router with the fastest path to reach the coordinator. It is proven by monitoring solar panels with an optimal distance of 80 m, which is carried out from outdoors with an RSSI value of -84 dBm, a throughput of 0.55 kbps, and a delay of 2.77 ms, producing power for 150 minutes with an average current of 0.14 A, voltage of 9.86 V, and temperature of 31.48°C. The number of disturbances and weather conditions greatly affect signal propagation, which can hamper the data transmission process. Throughput fluctuates due to uncertain weather conditions, and delay fluctuates due to a lot of interference from other radio signals.
 Keyworrds— Wireless Sensor Nirbael (WSN), RSSI, ZigBee, Panel Surya, Thinger.IO

Optimalisasi Labview Sebagai Kendali dan Monitoring Arus Tegangan pada Modul Solar Cell Menggunakan Jaringan Lokal

Renewable energy sources in Indonesia have been widely used, one of which is solar energy, for various needs, one of which is for solar power generation by using solar cells as the medium. In generating electricity, solar cells are very dependent on the intensity of the sun so as to produce currents and voltages that are not constant. Because of this, a monitoring system was created to monitor the current and voltage in a solar cell module to determine current and voltage conditions. In this study, the monitoring system used wireless data transmission via the XBee S2C module installed on the Arduino Mega 2560 and Arduino UNO. The INA 219 sensor is mounted on a solar cell module along with a relay module connected to the Arduino Mega 2560 and the XBee S2C module. The test results show that wireless data transmission cannot be used indoors at a distance of more than 20 meters. The INA 219 sensor as a reader for DC current and voltage parameters has an error rate of 0.49% to 0.75% for voltage and for DC current readings it has an error rate of 1.85%. The results of current and voltage readings are displayed on a LabVIEW interface in real-time and can be accessed via a web browser using a local network. Relay control carried out through LabVIEW has a delay time of 6.08 seconds. Logging data can be stored in .xls files and data displayed on a web browser has a delay of one to two seconds.

Children Tracking System Based on ZigBee Wireless Network and Neural Network

The safety of children is one of the fundamental concerns of parents. Recently, child kidnapping has increased by a large percentage, some children have been found, and some children have not found yet. This paper proposes an indoor localization system based on ZigBee wireless sensor network (WSN) and Backpropagation Artificial Neural Network (BP-ANN) to locate the child in an indoor environment. Several ANN topologies were investigated to select the best one with minimum tracking or localization error. The Received Signal Strength Indicator (RSSI) was collected from four ZigBee XBee S2C anchor nodes by the mobile node carried by the child in an indoor area of 32m × 32m. The RSSI was collected from 127 test points inside the tested area. The measured RSSI was used to train, test, and validate the performance of BP-ANN to determine the two dimensions (2D) of the target child’s location. Different topologies of ANN have been examined for training, testing, and validation which are 5-5, 10-10, 15-15, and 20-20 neurons in the hidden layer. The findings indicate that the 20-20 ANN topology can achieve higher accuracy than other topologies. Additionally, 20-20 topology localization errors were 1.0, 1.157, and 1.356 m for training, testing, and validating ANN performance.

Low Budget Respirometer Chamber Design Based on Wireless Sensor Network

Fresh fruit respiration information is essential optimizing food storage systems. Meanwhile, respiration is defined as the process of oxygen production and carbon dioxide release during storage in a closed respiratory chamber. Therefore, this study aims to design a low-budget computerized respiratory chamber for enhancing fruit packaging and storage system. Real-time fruit respiration can be measured by applying wireless gas sensors network. The respirometer consisted of 3,600 mL glass jar with a screw stainless lid, while the electrochemical and non-dispersive infrared sensors were mounted on the cover of the glass jar for collecting data on the oxygen, carbon dioxide, and temperature during mangoes’ respiration. Arduino USB port was used to record all measured parameters consisting of oxygen (%) and carbon dioxide concentrations (ppm, as well as temperature in the respiration chamber. Additionally, a controlled cooling chamber was applied to maintain the temperature during storage, while data communication was supported by Xbee S2C based on radio frequency. According to the respirometer real-time reading, there was a decrease in oxygen concentration caused by increasing carbon dioxide release with temperature. The low-budget respirometer was used to measure the respiration rate and record the data through a wireless sensor network system. The data plot shows that the respiration rate increased as the storage temperature and the respiratory quotient ranged from 0.32-0.44.

Statistical Analysis Based Feature Selection Enhanced RF-PUF With >99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security

Due to the diverse and mobile nature of the deployment environment, smart commodity devices are vulnerable to various spoofing attacks which can allow a rogue device to get access to a large network. The vulnerability of the traditional digital signature-based authentication system lies in the fact that it uses only a key/pin, ignoring the device fingerprint. To circumvent the inherent weakness of the traditional system, various physical signature-based RF fingerprinting methods have been proposed in literature and RF-PUF is a promising choice among them. RF-PUF utilizes the inherent nonidealities of the traditional RF communication system as features at the receiver to uniquely identify a transmitter. It is resilient to key-hacking methods due to the absence of secret key requirements and does not require any additional circuitry on the transmitter end (no additional power, area, and computational burden). However, the concept of RF-PUF was proposed using MATLAB-generated data, which cannot ensure the presence of device entropy mapped to the system-level nonidealities. Hence, an experimental validation using commercial devices is necessary to prove its efficacy. In this work, for the first time, we analyze the effectiveness of RF-PUF on commodity devices, purchased off-the-shelf, without any modifications whatsoever. We have collected data from 30 Xbee S2C modules used as transmitters and released as a public dataset. A new feature has been engineered through PCA and statistical property analysis. With a new and robust feature set, it has been shown that 95% accuracy can be achieved using only ∼1.8 ms of test data fed into a neural network of 10 neurons in 1 layer, reaching >99.8% accuracy with a network of higher model capacity, for the first time in literature without any assisting digital preamble. The design space has been explored in detail and the effect of the wireless channel has been investigated. The performance of some popular machine learning algorithms has been tested and compared with the neural network approach. A thorough investigation of various PUF properties has been done. With extensive testing of 41238000 cases, the detection probability for RF-PUF for our data is found to be 0.9987, which, for the first time, experimentally establishes RF-PUF as a strong authentication method. Finally, the potential attack models and the robustness of RF-PUF against them have been discussed.

Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module.

The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends on networking parameters, such as baud rates, transmission power, data encryption, hopping, deployment environment, and transmission distances. For optimized network deployment, an extensive performance analysis is necessary. This would facilitate a clear understanding of the trade-offs of the network performance metrics, such as the packet delivery ratio (PDR), power consumption, network life, link quality, latency, and throughput. This work presents an extensive performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contributions of this work include (i) evaluating the most optimized transmission power level of Zigbee, considering packet delivery ratio and network lifetime; (ii) formulating an algorithm to find the network lifetime from the measured current consumption of packet transmission; and (iii) identifying and quantizing the trade-offs of the multi-hop communication and data encryption with latency, transmission range, and throughput.

Modelling on Impact of Building Obstruction for V2I Communication Link in Micro Cellular Environment

In vehicular communication, signal transmission in vehicle-to-infrastructure (V2I) mode typically takes place on highways, urban, suburban and rural environments. The presence of buildings in these environments poses a challenge to model path loss (PL) due to multiple propagation mechanisms such as diffractions and reflections. However, very little attention has been made to address building effects on the performance of V2I communication links in microcell environment. This paper investigates signal propagation characteristics caused by the impact of building under micro-cellular environment whereby the base station or road-side-unit (RSU) is usually located under the rooftop of building to allow communication between RSU and mobile station or on-board-unit (OBU) on the road. The goal of this paper is to validate and discuss available path loss models based on effect of building obstruction towards RSU-OBU links specifically in residential housing area. The channel measurements are conducted based on static line-of-sight (LOS) settings of a real-world environment at 2.4 GHz frequency band using IEEE 802.15.4 XBee S2C compliant device to measure its receive power. The results are demonstrated based on received signal strength indicator (RSSI) and root mean square error (RMSE). The attenuation profile is validated and compared with suitable path loss models to evaluate best fit and most compatible model based on our measurements data and environment. The analysis shows that several V2I path loss models and V2V channel models are applicable to be used as a reference to model in LOS microcell environment with building obstruction. The finding shows that PL Urban yields the best fit V2I path loss model in terms of RMSE when compared to our measurement campaign at 2.4 GHz.

Humanoid Dancer Robot Movement Synchronization System with Zigbee

This research proposes a wireless communication between two dancer robots using zigbee to overcome the differences in movement between the robots in order to dance in harmony. The robotic music processor uses Audio Analyzer V2.0 which functions to get 7 frequency values for the music being played. The frequency will be processed by Arduino to determine the condition of the music on and off which will be sent to each robot via the XBee S2C module using the Zigbee method, namely with 1 master and 2 routers. The result is that the music processing system becomes more robust because it uses the Zigbee method. By using the Zigbee method, robot 1 and robot 2 can also communicate with each other because with this method, fellow routers can exchange data. The robot produced by this study can already distinguish the conditions when music is playing or muted, and between robot 1 and robot 2 can communicate with each other. Communication between robots functions so that when one of the robots is late in getting data, the other robots will wait until the late robot completes its movements so that robot 1 and robot 2 are back in sync.

A Prototype of Wireless Networked IoT Based Lighting Control in Open Platform

Background: The lighting researchers are keenly looking for the huge benefits of the internet of things on an open platform which provides the cost gains in addition to other environmental benefits. Connected systems interact with the software and analyse real-time building conditions, and feed information into the building controls network. Methods: This paper presents a wireless networked system for lighting control in buildings which connect the power of the Internet of Things. After analysing the ZigBee network on QualNet v7.4, a Digi Mesh network was set up using XBee modules using the XBee Configuration and Test Utility [XCTU] Software v6.3.11. The ThingSpeak cloud platform along with MATLAB 2017b provides the necessary cloud support to enable this network to communicate over the internet. The results indicate that the XBee S2C module functioning in the API mode when flashed with the DigiMesh firmware offers the best option for forming a self-healing mesh network. An aggregator node acts as an information sink and collects the sensor data from all the sensor nodes and passes it on to the cloud via the Raspberry gateway. Results: The algorithm on the cloud can read this sensor data and compute the necessary Pulse Width Modulation [PWM] signals required to control the brightness of a dimmable LED luminaire. The system also takes into consideration the zone-wise occupancy in the room while computing the PWM values to be sent to the luminaires. Conclusion: The use of the concept of open platform sensors and actuators is the significance of the work.

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

The number of older adults with Alzheimer's disease is increasing every year. The associated memory problems cause many difficulties for Alzheimer's patients and their caretakers; patients may even become lost in familiar surroundings. In this article, a proposed localization system based on a wireless sensor network (WSN) and backpropagation based artificial neural network (BP-ANN) was practically implemented to detect and determine the position of an Alzheimer's patient in an indoor environment. The proposed system consisted of four ZigBee-based XBee S2C anchor nodes and one mobile node carried by the Alzheimer's patient. The received signal strength indicator (RSSI) of the anchor nodes was collected by the mobile node using a laptop supported by X-CTU software. The obtained RSSI values were used as input for training, testing, and validation processes of the BP-ANN, while two-dimension (2D) locations (x and y) were used as the output of the ANN. The results showed that the obtained mean localization errors were 0.964 and 0.921m for validation and testing phases, respectively, after applying the ANN. Based on a comparison with state-of-the-art technology, we deduced that the proposed ANN method outperformed other techniques in previous studies in terms of mean localization error.

Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation.

Wireless sensor networks proliferate more and more in all social scopes and sectors. Such networks are implemented in smart homes, smart cities, security systems, medical resources, agriculture, automotive industry, etc. Communication devices and sensors of such networks are powered with batteries: the enlarging of battery life is a hot research topic. We focus on wireless sensor networks based on ZigBee technology. While sleep standard operation mode is defined for end devices, it is not the case for the rest of devices (routers and Coordinator), which usually always remain in active mode. We designed a formal optimization model for maximizing the enlarging of the battery life of routers and Coordinator, allowing us to delimit practical successful conditions. It was successfully tested with a standard ZigBee datasheet comprising technical data for sensors, routers, and coordinators. It was tested in a practical wireless sensor network assembly with XBee S2C devices. We derived, from the previous model, a novel but simple protocol of communication among routers and coordinators. It was tested in different use cases. We showed that when end devices generate traffic at regular intervals, the enlarging of the battery life of routers and Coordinator was possible only under certain use cases.

Integrated Earthquake and Landslide Monitoring Over Wireless Sensor Network

Natural disasters have mercilessly devastated our lives in so many different terms. The impact of Earthquake and landslides are very severe because of their unpredictability. To alleviate the problem early warning system plays an important role. In this paper, we proposed an integrated earthquake and landslide monitoring system overthewireless sensor network. The Wireless Sensor Network is programmed to acquire the data, which is monitored and controlled centrally or independently and can be distributed widely in a random or planned deployment. By detecting suspicious indications such as tremor or landslides through sensor nodes, the system provides information to the monitoring and warning station. The processing unit is composed of arduino and Sensors such as 4.5 Flex Sensor, Capacitive Soil Moisture Sensor and a3-Dimensional Accelerometer are used in the proposed system to monitor landmass displacement/movement, the moisture level of the soil and the vibrations for earthquakes respectively. An RF module, XBee S2C is used which provides wireless communication with a range of about a 1 km line of sight and 60 meters indoor. The real-time data can be accessed via the internet. It will be powered from a 12V 20W solar panel with rechargeable Li-Ion Battery and fitted with necessary protection and charging circuit boards. The proposed system can be used in earthquake and landslide prone area to avoid damages to life and property in the area of operation by providing crucial information and further warning for any disastrous development. The proposed system is implemented through experiments and proved to be effective.

Implementing a Wormhole Attack on Wireless Sensor Networks with XBee S2C Devices

One of the most dangerous threats to Wireless Sensor Networks (WSN) are wormhole attacks, due to their capacity to manipulate routing and application data in real time and cause important damages to the integrity, availability, and confidentiality of network data. An empirical method to launch a successful attack on IEEE 802.15.4/Zigbee devices with source routing enabled is adopted in this work to find signatures for detecting wormhole attacks in real environments. It uses the KillerBee framework with algorithms for packet manipulation through a malicious node to capture and inject malicious packets in victim nodes. Besides, a reverse variant of wormhole attack is presented and executed. To evidence the realization of this threat by the attacking software, the experimental framework includes XBee S2C nodes. The results include recommendations, detection signatures and future work to face wormhole attacks involving source routing protocols like DSR.

Implementation of ZigBEE (IEEE 802.15.4) Based Wireless ECG Measurement System

This study presents a realization of a ZigBEE based wireless ECG system for remotely monitoring the patient's electrocardiogram (ECG) via wireless heart sensor (AD8232). The presented wireless ECG system is composed of hardware such as Arduino micro-controller connected to the electrodes on the patient, personal laptop, tablet PC, smart phone etc. for ECG signal monitoring, AD8232 sensor for measuring heart rate, ZigBEE transceiver (XBee S2C) for wireless transmission of ECG signal, and software such as Serial Plot or Arduino Serial Plotter. The results from the tests show that the system designed over a single node can work seamlessly in an open area of approximately 20 to 100 meters and take ECG measurements wirelessly. Additionally, the obtained results from the tests show that the ECG measurements can be taken wirelessly within a range of 20 to 80 meters when the obstacles are also placed between ZigBEE transmitter and receiver.