Sayedul Aman

An Application-Driven Modular IoT Architecture

Building upon the advancements in the recent years, a new paradigm in technology has emerged in Internet of Things (IoT). IoT has allowed for communication with the surrounding environment through a multitude of sensors and actuators, yet operating on limited energy. Several researchers have presented IoT architectures for respective applications, often challenged by requiring major updates for adoption to a different application. Further, this comes with several uncertainties such as type of computational device required at the edge, mode of wireless connectivity required, methods to obtain power efficiency, and not ensuring rapid deployment. This paper starts with providing a horizontal overview of each layer in IoT architecture and options for different applications. Then it presents a broad application-driven modular architecture, which can be easily customized for rapid deployment. This paper presents the diverse hardware used in several IoT layers such as sensors, embedded processors, wireless transceivers, internet gateway, and application management cloud server. Later, this paper presents implementation results for diverse applications including healthcare, structural health monitoring, agriculture, and indoor tour guide systems. It is hoped that this research will assist the potential user to easily choose IoT hardware and software as it pertains to their respective needs.

Reliability evaluation of iBeacon for micro-localization

Bluetooth Low Energy (BLE) enabled iBeacon is a low-power device and is suitable for many proximity and location-based applications. Micro-localization refers to the phenomena of tracking a target with high accuracy, which is an integral part of location-aware applications, both indoor and outdoor. Although many approaches towards localization have already been proposed in many research works, Received Signal Strength Identification (RSSI) based localization using the latest BLE technology is comparatively a new concept and it reduces the energy requirements of such applications. But RSSI based distance calculation provide only a rough estimation of the proximity and does not give an accurate result for micro-localization. This paper presents and evaluates a computationally simple method for micro-localization of an object from the RSSI values from a collection of iBeacons so the method can be used in devices with very low processing power. Among the different scenarios the experiments were conducted, most of them resulted in localization error of less than 1m.

A low-power IoT framework: From sensors to the cloud

With todays technology humanity is continuously approaching a time where a vast majority of our electronic devices will be interconnected, or networked, commonly known as the Internet of Things (IoT). This project proposes an IoT-based wireless sensor network with configurable nodes with multiple sensors, also known as transducers. The nodes can collect data from the surrounding environment, and present it on the cloud for universal accessibility by the users. With this system, users can easily access and analyze various data, such as temperature, humidity, motion detection, illuminance, CO gas, air quality, etc. and take necessary actions upon that information. The feasibility of proposed IoT transducer framework has been verified through real-time hardware implementation.

Kalman filter based indoor mobile robot navigation

This paper focuses on navigation of a mobile robot in an indoor environment. Accuracy is an important issue in robot navigation. Thus, many approaches for mobile robot navigation have been proposed to improve the navigation accuracy. This paper presents work aimed to navigate a mobile robot, which uses wheeled encoder as a sensor. While using wheel encoder the robot is commanded to move along certain paths like an octagon, rectangle and its coordinates and travelled distances are monitored. The wheel encoder gives us Cartesian coordinate and orientation of the robot. The encoder accumulates error over time. Thus, a Kalman filter is proposed to minimize this error. The Kalman filter estimates the next position based on robots action, like velocity, acceleration etc and sensors output. The error made by both encoder and the Kalman filter is measured. Experimental results show that Kalman filter improves the accuracy.

Sensing and classifying indoor environments: An Iot based portable tour guide system

Tour guide system, as referred by the name, is a system used for assisting visitors during a tour of a certain place. Increasing tour efficiency while minimizing resources required has been studied by many researchers. The traditional multilingual human tour guides limit in the fact that, they cannot provide personalized information to every visitor because of time constraint. Also, having persons to assist tourists as a guide is expensive. Some tour guide robots and devices were presented in previous works to overcome certain limitations, but some challenges still exist. In this work, an indoor location-aware portable device is presented aiming at improving the users experience during a tour of an indoor facility. The proposed system can provide personalized audio-visual information based on the location of the visitor, making the visitors independent of following a guide. The core of the module is a Raspberry Pi 3 with Bluetooth Low Energy (BLE) and Wi-Fi transceivers. Localization is performed using iBeacons, and RFID technology is used to identify certain objects. The Thingworx platform has been used as the application cloud server while YouTube has been used to present visual feedback to the user.

Rapid deployment of IoT enabled system for automobile fuel range and gas price location

The automotive industry, in accordance to transportation methods represents a significant part of the general consumers daily life. With the limitations experienced by consumers in time and money, systems offering consumers the chance to save are becoming more and more prevalent. In this paper, an Internet of Things (IoT) system consisting of a vehicle, its On-Board Diagnostics (OBD) data, and an Android smartphone application monitors a vehicles present fuel range and outputs surrounding gas station suggestions based on proximity and gas prices. This network map of gas station locations allows the consumer to make decisions that could save them time and money. The proposed system has undergone experimental test to ensure efficient operation including data acquisition, transfer, and display. The proposed system was able to provide the consumer with a map of gas station suggestions based off of fuel range data obtained from the vehicles internal computer.

On the evolution of mobile computing software systems and C/C++ vulnerable code: Empirical investigation

A study is presented that examines the distribution and the usage of some unsafe functions that are known to cause security vulnerabilities in 15 software systems, written in C/C++. The systems are commonly used for mobile computing, and they comprise almost six million lines of code. A tool that uses a static analysis approach is applied to each system, and the number of calls to unsafe functions is determined and tabulated. The results show that vulnerable functions such as strcmp, strlen, and memcpy represent the vast majority of used unsafe functions that are banned by many companies (e.g., Microsoft) in the studied systems. This fact can help software trainers better design and plan training courses and materials on secure coding practices for software developers. Additionally, findings can help software engineers to conduct more effective refactoring processes that help to clean software systems from vulnerable code, and focus primarily on the removal of vulnerable code with higher usage for better outcomes. The historical data for a number of systems, subset, is presented over a five-year period. The data shows that few of the systems examined are increasing the number of unsafe function calls over time. This is somewhat contradictory to the literature, which claims that the use of vulnerable functions is decreasing in software systems. This fact demands that more attention and effort from software engineering and mobile computing communities be put towards addressing this phenomenon.

Parallelization in software systems used in wireless sensor networks and Internet of Things: Case study: Middleware systems

In the Internet of Things (IoT) architecture, middleware is the component that creates a bridge between the heterogeneous sensor nodes and the software applications. One aspect among many others that determine the efficiency, performance, and functionality of middleware is parallelizability. Parallelizability refers to how ready the software is to utilize multicore architecture. The problem in general has been studied recently. This paper presents a study that examines the middleware software systems that have been widely used in IoT and wireless sensor network (WSN) applications. The examination gives us an idea of how well the software takes advantage of the hardwares multicore technology. The study is conducted on the source code of three open source middleware systems containing 1.07 million lines of code and almost 5k files. Each system is analyzed and the inhibitors to parallelization are detected and presented. The most prevalent inhibitors are pointed out to show the developers of IoT middleware systems what they should focus on when refactoring and improving their products. A historical trend over the last five years concerning the presence of inhibitors in the source code is also presented. The study suggests some techniques for refactoring the source code to improve the parallelizability of the systems as well.

Vulnerable C/C++ code usage in IoT software systems

An empirical study that examines the usage of known vulnerable statements in software systems developed in C/C++ and used for IoT is presented. The study is conducted on 3 open source systems comprising more than one million lines of code and containing almost 5K files. Static analysis methods are applied to each system to determine the number of unsafe commands known among research communities to cause potential risks and security concerns, thereby decreasing a systems robustness and quality (i.e., strcpy, strcmp, and strlen). Some of those statements are banned by some companies (e.g., Microsoft). These commands are not supposed to be used in new code and should be removed from legacy code over time as recommended by new C/C++ language standards. Additionally, each system is analyzed and the distribution of the known unsafe commands is presented. Historical trends in the usage of the unsafe commands are presented to show how the studied systems evolved over time with respect to the vulnerable code. The results show that the most prevalent unsafe command used across all systems is memcpy, followed by strlen.

A sensor fusion methodology for obstacle avoidance robot

Obstacle detection and navigation of dynamic environments is a challenge in mobile robotics. To address this challenge, this paper presents an efficient sensor fusion methodology to detect the size and location of obstacles and navigate the mobile robot with high accuracy. This is done by leveraging upon the unique advantages of accuracy in both ultrasonic sensor and a Kinect sensor for near-field and far-fields respectively. Further, an efficient Kalman filter is implemented to reduce the systematic errors in encoder data to track robot pose of the robot in real-time and reach the destination with high accuracy. Implemented on differential drive-based mobile robot, the proposed system has been validated with a high efficiency of detecting obstacles and reaching the destination with an accuracy of 5cm.