Machine learning based lightweight interference mitigation scheme for wireless sensor network

The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference

Energy detection based cooperative spectrum sensing system for emergency networks

During emergencies, a number of rescue teams come to the field and setup their own radio communication systems. If the deployed communication setup does not coordinate among themselves properly, they may interfere with each other when using the same RF channels known as co-channel interference. Spectrum sensing is the most important and complex job for cognitive radios. Cooperation among cognitive radio nodes is needed to enhance the sensing performance. In this paper, we present an experimental study of this solution. A Software Defined Radio comprising of GNU Radio and USRP were used to capture the signal samples to build a database profile of the spectrum condition. MATLAB communications toolbox was used to analyze the data and examine the spectrum pertaining to the condition in emergency networks. The benefits of cooperative spectrum sensing in avoiding co-channel interference during emergency situations are illustrated. Cooperation among cognitive spectrum sensing nodes operating at the same frequency improves the probability of detection, and the overall efficiency of the system. Results show that the cooperative sensing scheme outperforms the individual sensing approach. It can increases the probability of detection relative to the collected samples as the key performance indicator

Lightweight IoT middleware for rapid application development

Sensors connected to the cloud services equipped with data analytics has created a plethora of new type of applications ranging from personal to an industrial level forming to what is known today as Internet of Things (IoT). IoT-based system follows a pattern of data collection, data analytics, automation, and system improvement recommendations. However, most applications would have its own unique requirements in terms of the type of the smart devices, communication technologies as well as its application provisioning service. In order to enable an IoT-based system, various services are commercially available that provide services such as backend-as-a-service (BaaS) and software-as-a-service (SaaS) hosted in the cloud. This, in turn, raises the issues of security and privacy. However there is no plug-and-play IoT middleware framework that could be deployed out of the box for on-premise server. This paper aims at providing a lightweight IoT middleware that can be used to enable IoT applications owned by the individuals or organizations that effectively securing the data on-premise or in remote server. Specifically, the middleware with a standardized application programming interface (API) that could adapt to the application requirements through high level abstraction and interacts with the application service provider is proposed. Each API endpoint would be secured using Access Control List (ACL) and easily integratable with any other modules to ensure the scalability of the system as well as easing system deployment. In addition, this middleware could be deployed in a distributed manner and coordinate among themselves to fulfil the application requirements. A middleware is presented in this paper with GET and POST requests that are lightweight in size with a footprint of less than 1 KB and a round trip time of less than 1 second to facilitate rapid application development by individuals or organizations for securing IoT resources

Interference temperature measurements and spectrum occupancy evaluation in the context of cognitive radio

This paper presents a refined radio spectrum measurement platform specifically designed for spectrum occupancy surveys in the context of Cognitive radio. Cognitive radio permits the opportunistic usage of licensed bands by unlicensed users without causing harmful interference to the licensed user. In this work, a study based on the measurement of the 800 MHz to 2.4 GHz frequency band at two different locations inside Universiti Teknologi Malaysia (UTM), Johor Bahru campus, Malaysia is presented. Two Tektronix RSA306B spectrum analyzer are set up to conduct simultaneous measurements at different locations for a 24 hours period. The analysis conducted in this work is based on the real spectrum data acquired from environment in the experimental set up. Busy and idle channels were identified. The channels subject to adjacent-channel interference were also identified, and the impact of the detection threshold used to detect channel activities was also discussed. The consistency of the observed channel occupation over a range of thresholds and a sudden drop has good characteristics in determining an appropriate threshold needed in order to avoid interference

A Concept Paper on Smart River Monitoring System for Sustainability in River

River is a major source of water in Malaysia and one of the major threats to its sustainability is pollution. The existing methods for monitoring of water quality in rivers are manual monitoring and continuous monitoring. These methods are costly and less efficient. Hence, we propose a smart river monitoring system (SRMS) that uses unmanned aerial vehicles (UAVs) or drones and low power wide area (LPWA) communication technology. The Internet of Things (IoT) and data analytic are promising techniques which provide real-time monitoring and enhances efficiency. However, due to the span of river that needs to be monitored, conventional communication technology such as Wi-Fi, Zigbee, Bluetooth are not suitable. Hence, there is the need for LPWA communication technology. We discuss the application of LPWA and UAV for sustainability of rivers in Malaysia as a case study. Preliminary results show that the use of UAV will increase the efficiency of measuring the water quality parameters compared to manual monitoring method. Also, real-time monitoring enables us to study the changes in water quality. Finally, we provide future direction in the application of UAV and LPWA for sustainability in river

On achieving network throughput demand in cognitive radio-based home area networks

The growing number of wireless devices for in-house use is causing a more intense use of the spectrum to satisfy the required quality-of-service such as throughput. This has contributed to spectrum scarcity and interference problems particularly in home area networks (HAN). Cognitive radio (CR) has been recognized as one of the most important technologies which could solve these problems and sustainably meeting the required communication demands by intelligently exploiting temporarily unused spectrum, including licensed spectrum. In this paper, we propose a throughput demand-based cognitive radio solution for home area networks (TD-CRHAN) which aims at effectively and efficiently meet the ever-increasing throughput demand in HAN communication. It is shown numerically and by simulations that a TD-CRHAN can satisfy the requested throughput from the network devices and has high utilization of the available throughput. The analysis further shows that, by setting the achievable throughput to be as close as possible to the total demanded throughput (instead of maximizing it), a TD-CRHAN is able to relax the tight cooperative spectrum sensing requirements which significantly improves cooperative spectrum sensing parameters, such as the local spectrum sensing time and the number of cooperative spectrum sensing devices. Finally, it is shown that these cooperative spectrum sensing parameters can be further improved when additional channels are available

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

Experimental study of sensing performance metrics for cognitive radio network using software defined radio platform

Cognitive Radio (CR) is a promising technology in wireless communication for an enhanced utilization of limited spectral resources. It allows unlicensed or cognitive users (CUs) to sense the spectral environment and access a channel exhibiting negligible activity of licensed or primary users (PUs). Hence, spectrum sensing is a crucial task for a CU to perform in an opportunistic spectrum access (OSA) based CR network to avoid harmful interference to PU. Two main performances metrics that are crucial in the design of spectrum sensing are the probability of false alarm ( P fa ) and the probability of detection ( P d ). These metrics are used to define the CR system quality of service (QoS). The threshold to decide on the presence of PU and the sensing time needed for the CR system are also determined based on these metrics. This paper presents the design of measurement methods to experimentally acquire the P fa and P d curves based on locally captured data to determine the value of the threshold and sensing time. The implementation, experimentation and measurement are done using GNU Radio and universal software defined radio peripheral (USRP) software defined radio (SDR) platform as the cognitive radio testbed. Spectrum sensing was done based on energy detection. Each of the energy based detection measurement is repeated 1000 times to obtain an accurate estimation of P fa and P d . The findings show that the target Quality of Sevice (QoS) of P fa of 5% and P d of 90% can be derived from the estimated sensing threshold of -39 dB and achieves a sensing time of 31.59 ms

SMART VEHICLE MONITORING AND ANALYSIS SYSTEM WITH IOT TECHNOLOGY

In order to reduce the increasing number of road accidents in recent years, the Transport Ministry of Malaysia is keen to study the flexible road tax payment system based on the motorists’ driving behavior which can be monitored using a vehicle monitoring system. However, existing vehicle monitoring systems in the market mostly rely only on Global Positioning System (GPS) to estimate the vehicle’s speed and location which results may be inaccurate once the satellite coverage is lost due to tall buildings or dense forest. Therefore, a Smart Vehicle Monitoring and Analysis System (VMAS) using Internet of Things (IoT) technology is proposed. The developed system comprises of an Android application on smartphone, an On-board Diagnostics II (OBD-II) device and a cloud database management system. OBD-II device will collect real-time data on engine parameters. The data will be automatically extracted using Bluetooth connection to the Android platform for display and to upload data to the cloud server.  The cloud server is used for data storage and can be accessed remotely by authorized or administrative personnel via a monitoring webpage. It will provide complete visibility of uploaded data in real time as well as instant activities reports and history logs. The performance investigation shows that the system latency is around 1 second. The data analysis includes driving attributes ranking and fuel efficiency rating which is foreseen to contribute significantly to flexible road tax scheme and diagnostic tests by vehicle manufacturers respectively

SMART VEHICLE MONITORING AND ANALYSIS SYSTEM WITH IOT TECHNOLOGY

In order to reduce the increasing number of road accidents in recent years, the Transport Ministry of Malaysia is keen to study the flexible road tax payment system based on the motorists’ driving behavior which can be monitored using a vehicle monitoring system. However, existing vehicle monitoring systems in the market mostly rely only on Global Positioning System (GPS) to estimate the vehicle’s speed and location which results may be inaccurate once the satellite coverage is lost due to tall buildings or dense forest. Therefore, a Smart Vehicle Monitoring and Analysis System (VMAS) using Internet of Things (IoT) technology is proposed. The developed system comprises of an Android application on smartphone, an On-board Diagnostics II (OBD-II) device and a cloud database management system. OBD-II device will collect real-time data on engine parameters. The data will be automatically extracted using Bluetooth connection to the Android platform for display and to upload data to the cloud server.  The cloud server is used for data storage and can be accessed remotely by authorized or administrative personnel via a monitoring webpage. It will provide complete visibility of uploaded data in real time as well as instant activities reports and history logs. The performance investigation shows that the system latency is around 1 second. The data analysis includes driving attributes ranking and fuel efficiency rating which is foreseen to contribute significantly to flexible road tax scheme and diagnostic tests by vehicle manufacturers respectively