Boat Detection and Tracking Using RSSI Signal in A Device-Free Environment

Abstract

This project presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So, it raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receiver sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method.

Introduction

I. INTRODUCTION

The Boat detection and tracking using radio signal strength in indoor wireless networks has attracted a great deal of interest from the research community because this technology can be applied in many applications including intrusion detection and tracking in buildings, monitoring and tracking in emergency situations and monitoring for controlling automated devices. In many scenarios, the boatss to be monitored cannot be expected to carry any radio device. Consequently, a device-free boat detection and tracking system, that works by monitoring and analyzing the changes in received signal strength patterns, is used to fulfill such a requirement.

The first function is developed for measuring and collecting RSSI signals affected by boat movement, while the second function is developed for detecting and tracking the boat using a predefined threshold and a zone selection method. The novelty of our proposed system is that the communication protocol can avoid signal interference and packet loss in the network, and the detection and tracking method can specify an actual zone that the human is present by taking an optimal predefined threshold and a level of RSSI variation in each zone into consideration.

To detect and track boat movements, RSSI information is widely used because most wireless devices have RSSI circuits built into them. Thus, no additional or extra hardware is required. This helps reduce the hardware cost and power consumption of the system. The major challenge in the use of RSSI is that the measured RSSI is time-varying and unreliable in general. It often fluctuates over time due to multi-path effects caused by reflection, diffraction, and scattering of radio signals in a physical environment. High variation of the RSSI can cause significantly high levels of detection and tracking errors, and inaccurate results can lead to poor decisions in the overall system. Due to the RSSI variation problem, an acceptance level for the detection and tracking accuracies is required (depending on the application). The issue of the balance between the detection and tracking accuracy and the complexity of the method should also be considered. In addition, from a wireless communication perspective, for RSSI measurement and collection, the signaling overhead generated by communication protocols, the power consumption of wireless devices, and communication reliability are also major concerns. Therefore, in the design and development of boat detection and tracking system using RSSI, the mentioned requirements are very challenging and need investigation.

A. Proposed Work

In the proposed system, the boat distance can be measured using the received signal strength received from the RSSI Transmitter (boat). By using this RSSI, we can find the location of the boat in the sea. Whenever the boat reaches the border, the APR voice, alert the concern person in the boat and at the same time boat will automatically turn OFF.

B. Block Diagram

C. Component Requirement

1. Hardware Requirements

a. Microcontroller

b. RSSI Module

c. Motor Driver

d. DC Motor

e. LCD Display

f. Speaker

g. Power Supply Unit

2. Software Requirements

a. PIC CC

b.MPLAB IDE

c. Embedded C

D. The Simulation

1. Step 1: Run the proteus 8 professional software application by clicking the icon on the desktop.
2. Step 2: Next, a workspace appears with interface buttons for designing circuits. Notice that there is a blue rectangular line in the workspace
3. Step 3: Select the library components. Select Library → Device/Icon from the menu bar. Then the window shown below will open, it is another way to select the components. There is a toolbar on the left side of the work area. Click the component mode button on this toolbar or choose from the library
4. Step 4: Select all components from the library, these components will be added to the device list. To change the angle of the component, click on the component with the Rotate button. Then click on the canvas and then the selected component will be placed on the canvas.
5. Step 5: Place all devices in the workspace and place the cursor on the pin end of the component, then draw the connections with the pen symbol. Connect all the components according to the circuit then the designed one.
6. Step 6: If any modifications needed in the component, place the cursor over the component and click the right button on the mouse, the option window will open.
7. Step 7: The components can be connected using the wires or it can be connect using the wire label by giving command on wire label.

E. Implementation     

The boat detection and tracking process begins after the computer receives the RSSI values from the transmitter nodes. The main concept of how to detect and track human movements is described here. Generally, in the communication area between the transmitter and the receiver nodes, RSSI values received by the receiver node often fluctuate around their mean. On the other hand, when the boat is in the communication area, blocking the radio signal path, the measured RSSI will significantly fluctuate. Thus, the variations in the RSSI values can represent the presence and movement of the boat. By this understanding, we use a different level between a mean RSSI value determined during no boat movement and a measured RSSI value collected during the test to compare with a predefined threshold (i.e., an appropriate RSSI variation level, which can indicate the boat movement) for detecting the boat. Detection results from all communication pairs are determined simultaneously. Here, the process of the boat detection and tracking is explained. During no movement in the communication area, the receiver node is assigned to collect the RSSI values from each transmitter node with a predefined number of samples. The RSSI is measured and collected using a simple and effective communication protocol. Indication intrusion and package defeat in the networks can be decreased by managing the sending and receiving sequences of packets that are transferred across nodes. In addition, the network's signaling overhead and the system's power consumption are reduced. We create an autonomous boat identification and tracking approach that uses low-complexity processing to accurately recognize and track boat movement.

 The individuality of this proposed method is that it determines the actual zone in which the boat is currently present by considering an acceptable threshold and the level of RSSI fluctuation measured in each zone. A simple and efficient communication protocol is developed for measuring and collecting the RSSI. By controlling the sending and receiving sequences of packets that are exchanged among nodes, the signal interference and the packet loss in the network can be reduced. Also, the signaling overhead generated in the network and the power consumption of the system are minimized. We develop an autonomous boat detection and tracking method that can accurately detect and track the boat and consume low complexity processing. 

II. RESULT AND DISCUSSION 

The results obtained are discussed below:

The proteus software is used for obtaining the simulation for the boat detection. The schematic representation is shown in simulation diagram which shows the connection of transformer, motor driver, LCD, potentiometer with the microcontroller.

A power supply unit (or PSU) converts mains AC to low voltage regulated DC power for the internal components of a controller.  A power supply is used to reduce the mains electricity at 240 volts AC down to something more useable, say 12 volts DC. There are two types of power supply, linear and switch mode

The microcontroller of the board has a circuit inside called an analog-to-digital converter or ADC that reads this changing voltage and converts it to a number between 0 and 1023. When the shaft is turned all the way in one direction, there are 0 volts going to the pin, and the input value is 0. When the shaft is turned all the way in the opposite direction, there are 5 volts going to the pin and the input value is 1023. In between, analog Read () returns a number between 0 and 1023 that is proportional to the amount of voltage being applied to the pin.

Conclusion

Boat Position estimation is an important goal for realizing services that offer safety and security, especially during emergency situations and greater energy efficiency, even in small areas. Our proposal combines a simple method with a new signal processing procedure that uses the received signal strength indicator (RSSI) in a wireless sensor network for estimating boat movement. This method is simple and has the benefit of running on existing devices and existing wireless sensor networks.

References

[1]. K. Elango and S.R. Paranjothi, “Multi Area Economic Dispatch using Evolutionary Programming”, Proceedings of National Conference on Computational Intelligence in Power apparatus and Systems (CIPS2006), Organized by DEEE of SRM Engineering College, pp 55-61, April 2006. [2]. S. Paul and E. A. Wan, “RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers”, IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 5, pp. 860-873, Oct. 2009, DOI: 10.1109/JSTSP.2009.2032309. [3]. J. Wilson and N. Patwari, “Radio Tomographic Imaging with Wireless Networks”, IEEE Transactions on Mobile Computing, Vol. 9, No. 5, pp. 621-632, May 2010, DOI: 10.1109/TMC.2009.174. [4]. Elango K. and Paranjothi S. R. “Congestion Management using Generation Rescheduling and Load Shedding by Evolutionary Programming”, in Proceedings of the International Conference on Power, Control and Embedded System, pp. 21-26, July 2010. [5]. B. Mrazovac, M. Z. Bjelica, D. Kukolj, B. M. Todorovic and D. Samardzija, “A human detection method for residential smart energy systems based on Zigbee RSSI changes”, IEEE Transactions on Consumer Electronics, Vol. 58, No. 3, pp. 819-824, August 2012, DOI: 10.1109/TCE.2012.6311323. [6]. Y. Guo, K. Huang, N. Jiang, X. Guo, Y. Li and G. Wang, “An Exponential-Rayleigh Model for RSS-Based Device-Free Localization and Tracking”, IEEE Transactions on Mobile Computing, Vol. 14, No. 3, pp. 484-494, 1 March 2015, DOI: 10.1109/TMC.2014.2329007. [7]. H. Li and L. Chen, “RSSI-Aware Energy Saving for Large File Downloading on Smartphones”, IEEE Embedded Systems Letters, Vol. 7, No. 2, pp. 63-66, June 2015, DOI: 10.1109/LES.2015.2426195. [8]. S. Mahfouz, F. Mourad-Chehade, P. Honeine, J. Farah and H. Snoussi, “Non-Parametric and Semi-Parametric RSSI/Distance Modeling for Target Tracking in Wireless Sensor Networks”, IEEE Sensors Journal, Vol. 16, No. 7, pp. 2115-2126, April1, 2016, DOI: 10.1109/JSEN.2015.2510020. [9]. M. C. R. Talampas and K. S. Low, \"A Geometric Filter Algorithm for Robust Device-Free Localization in Wireless Networks”, IEEE Transactions on Industrial Informatics, Vol. 12, No. 5, pp. 1670-1678, Oct. 2016, DOI: 10.1109/TII.2015.2433211. [10]. L. Lin Shen and W. Wong Shung Hui, “Improved Pedestrian Dead-Reckoning-Based Indoor Positioning by RSSI-Based Heading Correction”, IEEE Sensors Journal, Vol. 16, No. 21, pp. 7762-7773, Nov.1, 2016, DOI: 10.1109/JSEN.2016.2600260. [11]. W. Xue, W. Qiu, X. Hua and K. Yu, “Improved Wi-Fi RSSI Measurement for Indoor Localization”, IEEE Sensors Journal, Vol. 17, No. 7, pp. 2224-2230, 1 April 1, 2017, DOI: 10.1109/JSEN.2017.2660522. [12]. S. Kianoush, S. Savazzi, F. Vicentini, V. Rampa and M. Giussani, “Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces”, IEEE Internet of Things Journal, Vol. 4, No. 2, pp. 351-362, April 2017, DOI: 10.1109/JIOT.2016.2624800. [13]. S. Subedi, E. Pauls and Y. D. Zhang, “Accurate Localization and Tracking of a Passive RFID Reader Based on RSSI Measurements”, IEEE Journal of Radio Frequency Identification, Vol. 1, No. 2, pp. 144-154, June 2017, DOI: 10.1109/JRFID.2017.2765618. [14]. Booranawong, N. Jindapetch and H. Saito, “A System for Detection and Tracking of Human Movements Using RSSI Signals”, IEEE Sensors Journal, Vol. 18, No. 6, pp. 2531-2544, 15 March15, 2018, DOI: 10.1109/JSEN.2018.2795747. [15]. A. Buffi, A. Michel, P. Nepa and B. Tellini, “RSSI Measurements for RFID Tag Classification in Smart Storage Systems”, IEEE Transactions on Instrumentation and Measurement, Vol. 67, No. 4, pp. 894-904, April 2018, DOI: 10.1109/TIM.2018.2791238. [16]. V. Bianchi, P. Ciampolini and I. De Munari, “RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes”, IEEE Transactions on Instrumentation and Measurement, Vol. 68, No. 2, pp. 566-575, Feb. 2019, DOI: 10.1109/TIM.2018.2851675. [17]. D. Konings, F. Alam, F. Noble and E. M. K. Lai, “Device-Free Localization Systems Utilizing Wireless RSSI: A Comparative Practical Investigation”, IEEE Sensors Journal, Vol. 19, No. 7, pp. 2747-2757, 1 April 1, 2019, DOI: 10.1109/JSEN.2018.2888862. [18]. J. Yan, H. Zhao, X. Luo, C. Chen and X. Guan, “RSSI-Based Heading Control for Robust Long-Range Aerial Communication in UAV Networks”, IEEE Internet of Things Journal, Vol. 6, No. 2, pp. 1675-1689, April 2019, DOI: 10.1109/JIOT.2018.2875428. [19]. Booranawong, N. Jindapetch and H. Saito, “Adaptive Filtering Methods for RSSI Signals in a Device-Free Human Detection and Tracking System”, IEEE Systems Journal, Vol. 13, No. 3, pp. 2998-3009, Sept. 2019, DOI: 10.1109/JSYST.2019.2919642. [20]. R.Rajesh and Dr.K.Elango, \"Optimization Techniques for Congestion Management using Facts Devices\", International Journal of Innovative Technology and Exploring Engineering, Vol. 9, No. 2, pp. 2280-2285, December 2019. [21]. K. H. Lam, C. C. Cheung and W. C. Lee, “RSSI-Based LoRa Localization Systems for Large-Scale Indoor and Outdoor Environments”, IEEE Transactions on Vehicular Technology, Vol. 68, No. 12, pp. 11778-11791, Dec. 2019, DOI: 10.1109/TVT.2019.2940272. [22]. J. Yoo, “Change Detection of RSSI Fingerprint Pattern for Indoor Positioning System”, IEEE Sensors Journal, Vol. 20, No. 5, pp. 2608-2615, 1 March1, 2020, DOI: 10.1109/JSEN.2019.2951712. [23]. P. Ghosh, J. A. Tran and B. Krishnamachari, “ARREST: A RSSI Based Approach for Mobile Sensing and Tracking of a Moving Object”, IEEE Transactions on Mobile Computing, Vol. 19, No. 6, pp. 1260-1273, 1 June 2020, DOI: 10.1109/TMC.2019.2909020. [24]. C. Li et al., “ReLoc: Hybrid RSSI- and Phase-Based Relative UHF-RFID Tag Localization with COTS Devices”, IEEE Transactions on Instrumentation and Measurement, Vol. 69, No. 10, pp. 8613-8627, Oct. 2020, DOI: 10.1109/TIM.2020.2991564. [25]. N. Duda, R. Weigel and A. Koelpin, “Low-Weight Wireless Sensor Node with Sensor-Data-Enhanced Dual-Frequency RSSI-Based Distance Estimation”, IEEE Transactions on Microwave Theory and Techniques, Vol. 68, No. 10, pp. 4131-4137, Oct. 2020, DOI: 10.1109/TMTT.2020.2993245. [26]. Q. Zheng, L. Luo, H. Song, G. Sheng and X. Jiang, “A RSSI-AOA-Based UHF Partial Discharge Localization Method Using MUSIC Algorithm”, IEEE Transactions on Instrumentation and Measurement, Vol. 70, pp. 1-9, 2021, Art No. 9002309, DOI: 10.1109/TIM.2021.3070617. [27]. Y. You and C. Wu, “Hybrid Indoor Positioning System for Pedestrians with Swinging Arms Based on Smartphone IMU and RSSI of BLE”, IEEE Transactions on Instrumentation and Measurement, Vol. 70, pp. 1-15, 2021, Art No. 9510615, DOI: 10.1109/TIM.2021.3084286. [28]. Y. Zhang, N. Mirchandani, S. Abdelfattah, M. Onabajo and A. Shrivastava, “An Ultra-Low Power RSSI Amplifier for EEG Feature Extraction to Detect Seizures”, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69, No. 2, pp. 329-333, Feb. 2022, DOI: 10.1109/TCSII.2021.3099056.

Copyright

Copyright © 2022 Rajeshwari GP, Prathick Vasan G, Ramanarayanan C, Elango K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.