Development of an autonomous IoT-based drone for campus security

In recent years, drone technology has gained popularity across the world because of its numerous applications, particularly in security and surveillance. This technology can be further revolutionized with the deployment of Industrial Revolution 4.0 Technology. This paper discusses the development of an IoT-based autonomous drone for more comprehensive campus security and surveillance system. The drone is featured with the capability of conducting a fully autonomous aerial surveillance, being the first responder in emergencies, streaming video while flying, avoiding obstacles, following a target and communicating with the current IoT based UTM’s security patrolling system for data transfer and drone control. This has been accomplished by using the open source ArduPilot software, Pixhawk flight controller along with Dronekit python library installed on a Raspberry Pi 4. The findings show that the actual performance of the designed drone is fairly similar to the simulation results. The drone has successfully performed autonomous navigation to incident location with 1 to 2 meter accuracy as well as follow-me mode. The cellular technology utilized for drone communication also is more robust and provides promising solution to overcome short operation range and interference

Security system using biometric technology: Design and implementation of voice recognition system (VRS)

Biometric technology is fast gaining popularity as means of security measures to reduce cases of fraud and theft due to its use of physical characteristics and traits for the identification of individuals. The earliest methods of biometric identification included fingerprint and handwriting while more recent ones include iris/eye scan, face scan, voice print, and hand print. Biometric voice recognition and identification technology focuses on training the system to recognize an individual's unique voice characteristics (i.e., their voice print). The technology lends itself well to a variety of uses and applications, including security access control for cell phones (to eliminate cell phone fraud), ATM manufacturers (to eliminate pin # fraud) and automobile manufacturers (to dramatically reduce theft and carjacking). In this paper, we present an implementation of a security system based on voice identification as the access control key. Verification algorithm is developed using MATLAB (SIMULINK) function blocks which is capable of authenticating a person's identity by his or her voice pattern. A voice match will produce logic '1' while a mismatch, logic '0'. A microcontroller circuit controlling access to a door is built to test the reliability of this voice controlled security system. It is found out that the developed voice recognition software has successfully activated the door opening mechanism using a voice command that ONLY works for the authenticated individual. The system is proven to be able to provide medium-security access control and also has an adjustable security level setting to account for the variations in one's voice each time a voice identification occurs

IEEE802.11 interference management for wireless sensor node

Internet of Things (IoT) had undergone an increasing popularity due to its significant features that is able to connect devices to the internet. As a result, Wi-FI enabled Wireless Sensor Node gain popularity as an implementation of IoT. To achieve satisfactory quality of service (QoS) for a Wi-FI enabled WSN system, an interference management is one of the most crucial operation. In this paper, a simple yet effective method of IEEE 802.11 interference management method at the physical (PHY) layer is introduced, where it effectively associates the node with the best available Access Point (AP) in the vicinity, ensuring a seamless connectivity to the internet. The selection of AP includes evaluating three criteria: link margin (LM) of APs, channel distance (CD) between adjacent APs, and the probability of detection (Pd) of APsThe results show that the algorithm is a promising solution by ensuring that the system is automatically connected to the best available AP and thus, reducing interference to the minimum

Solar assisted system (SAS) for wireless sensor node

The deployment of Wireless Sensor Network (WSN) for autonomous long-term monitoring of the environment has enabled applications in many diverse areas. However, WSN motes are typically powered by small batteries, which are finite energy sources and normally need periodic maintenance. Therefore, energy harvesting for WSN is a very promising solution for perpetual operations. In this paper, a solar-assisted system (SAS) for WSN is proposed. It is equipped with energy storage system to ensure uncorrupted power supply as well as a battery protection circuit to prolong the lifespan. In conclusion, WSN with SAS will have continuous and maintenance-free more operating time compared to conventional WSN

Robust and fast algorithm design for efficient Wi-Fi fingerprinting based indoor positioning systems

Indoor positioning systems (IPS) based on Wi-Fi fingerprinting have gained significant attention due to their potential for providing location-based services. Large scale IPS deployments require implementation of robust, accurate, and fast algorithms. Data analytics assisted algorithms provide positioning accuracy improvements, however their integration within real-time and scalable solutions significantly depends on the computational complexity. Therefore, we propose robust and computationally efficient algorithms for performance enhancements in Wi-Fi fingerprinting-based IPS. A robust radio map algorithm based on enhanced statistical cluster initialization was designed for efficient indoor environment characterization. The proposed data filtering algorithm leveraged smart clustering to mitigate real-time data variations. The designed area classification algorithm was based on smart dual-band data aggregation. We evaluated the performance of proposed algorithms based on accuracy and computation time. The performance evaluations signified accuracy and computational enhancement, in comparison to related benchmark techniques. The data filtering and area classification algorithms required 40% less computation time. Simultaneously, 14.5% and 36% accuracy improvements were recorded for the area classification and radio map algorithms respectively. The proposed algorithms have the potential to significantly enhance IPS performance in a variety of real-time applications, including indoor navigation and asset tracking

UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms

Embedded digitalized weather inspection system using wireless sensor network

Roof sprinkler is one of the home cooling systems which involve the sprinkling of water over the roof of the house. The water will then absorb heat from the house and evaporate, achieving the purpose of cooling the home. However, existing roof sprinkler technology relies on water supply from water department, lacks weather monitoring capability and not fully automated. This resulted in unnecessary usage of water supply, thus increasing the cost of water bills. Therefore, in this project, several designs of Embedded Digitalized Weather INspection (EDWIN) system using sensor nodes and wireless sensor network are proposed. This roof sprinkler is designed to harvest rain as alternative water source, operate automatically based on inputs from equipped temperature, humidity and rain sensors, as well as to collect weather pattern for weather monitoring purpose. Two architectural designs of EDWIN are investigated and their efficiencies in resource and packet reception ratio (PRR) are compared. While multi-hop network design does not differ significantly than single-hop network design in term of efficiency in resource, its PRR is lower than single-hop network design. Both designs, however, are able to properly perform weather monitoring and execute its decision making algorithm in operating roof sprinkler automatically

Lightweight IoT based indoor positioning for guard touring system

An Indoor Positioning System (IPS) with Internet of Things (IoT) platform for a Guard Touring System (GTS) application is developed to track in real-time the guards’ whereabouts in an indoor setting when they perform their patrolling duties. The developed system comprises of Bluetooth Low Energy (BLE) beacons, mobile application and Node-RED IoT platform. BLE beacons are used to collect position data. The position information from the beacons captured by the developed mobile phone application is then transmitted using MQTT broker service to reach the Node-RED cloud platform for analysis of the information and generating a real-time end-user display dashboard. The real-time position data is also stored in MongoDB database platform for future reference. Two methods are used to estimate the indoor positioning of the guards which are machine learning using Linear Regression model and BLE Media Access Control (MAC) Identifier. The findings show the BLE MAC Identifier method provides a high accuracy of 98% and the least delay in decision time, which can be as fast as 0.5 s. The method is also more cost-effective as it uses lesser number of devices to achieve high accuracy indoor positioning estimation

TelG Mote: a green wireless sensor node platform for smart home and ambient assisted living

The wireless sensor network (WSN) consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living (SHAAL) to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM (industrial, scientific, and medical) band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate (PRR) above 80 for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market