Outdoor operations of multiple quadrotors in windy environment

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system. Such continuous manipulation of the control effort for multiple sUAVs under uncertain environmental conditions is computationally taxing and can lead to reduced efficiency and safety concerns. Additionally, modern day sUAV systems are susceptible to cyberattacks due to their use of commercial wireless communication infrastructure. This dissertation aims to address these multi-faceted challenges related to the operation of outdoor rotor-based multi-sUAV systems. A comprehensive review of four representative techniques to measure and estimate wind speed and direction using rotor-based sUAVs is discussed. After developing a clear understanding of the role wind gusts play in quadrotor motion, two decentralized motion planners for a multi-quadrotor system are implemented and experimentally evaluated in the presence of wind disturbances. The first planner is rooted in the reinforcement learning (RL) technique of state-action-reward-state-action (SARSA) to provide generalized path plans in the presence of wind disturbances. While this planner provides feasible trajectories for the quadrotors, it does not provide guarantees of collision avoidance. The second planner implements a receding horizon (RH) mixed-integer nonlinear programming (MINLP) model that is integrated with control barrier functions (CBFs) to guarantee collision-free transit of the multiple quadrotors in the presence of wind disturbances. Finally, a novel communication protocol using Ethereum blockchain-based smart contracts is presented to address the challenge of secure wireless communication. The U.S. sUAV market is expected to be worth $92 Billion by 2030. The Association for Unmanned Vehicle Systems International (AUVSI) noted in its seminal economic report that UAVs would be responsible for creating 100,000 jobs by 2025 in the U.S. The rapid proliferation of drone technology in various applications has led to an increasing need for professionals skilled in sUAV piloting, designing, fabricating, repairing, and programming. Engineering educators have recognized this demand for certified sUAV professionals. This dissertation aims to address this growing sUAV-market need by evaluating two active learning-based instructional approaches designed for undergraduate sUAV education. The two approaches leverages the interactive-constructive-active-passive (ICAP) framework of engagement and explores the use of Competition based Learning (CBL) and Project based Learning (PBL). The CBL approach is implemented through a drone building and piloting competition that featured 97 students from undergraduate and graduate programs at NJIT. The competition focused on 1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, 2) simulation of drone flight missions, and 3) manual and semi-autonomous drone piloting were implemented. The effective student learning experience from this competition served as the basis of a new undergraduate course on drone science fundamentals at NJIT. This undergraduate course focused on the three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. Multiple assessment methods are applied to examine the students’ gains in sUAV skills and knowledge and student attitudes towards an active learning-based approach for sUAV education. The use of active learning techniques to address these challenges lead to meaningful student engagement and positive gains in the learning outcomes as indicated by quantitative and qualitative assessments

Using Virtual Laboratories to Teach Realistic Hands-On IoT Training in Remote Settings

With an excess of interconnected devices, Internet of things (IoT) technologies offer an exciting area for information systems researchers; however, the inherently physical nature of IoT makes it difficult to provide hands-on laboratory exercises to remote students. Research suggests that enactive mastery provides the greatest educational improvement to individual self-efficacy, yet not all enactive experiences are the same, certainly not when individuals have no means of accessing materials physically. Through the use of a virtual laboratory and home automation IoT technology, we develop a method to teach IoT in remote settings where students can experience hands-on IoT training in remote settings. We experimentally evaluate the virtual laboratory by comparing student outcomes in a traditional setting using physical materials to those using the virtual laboratory. Results indicate that while student perceptions were lower for students using the virtual laboratory, this virtual laboratory was successful in offering students the means to perform hands-on IoT automation, with these students achieving equivalent performance metrics to those utilizing a traditional physical laboratory

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

IoT-MQTT based denial of service attack modelling and detection

Internet of Things (IoT) is poised to transform the quality of life and provide new business opportunities with its wide range of applications. However, the bene_ts of this emerging paradigm are coupled with serious cyber security issues. The lack of strong cyber security measures in protecting IoT systems can result in cyber attacks targeting all the layers of IoT architecture which includes the IoT devices, the IoT communication protocols and the services accessing the IoT data. Various IoT malware such as Mirai, BASHLITE and BrickBot show an already rising IoT device based attacks as well as the usage of infected IoT devices to launch other cyber attacks. However, as sustained IoT deployment and functionality are heavily reliant on the use of e_ective data communication protocols, the attacks on other layers of IoT architecture are anticipated to increase. In the IoT landscape, the publish/- subscribe based Message Queuing Telemetry Transport (MQTT) protocol is widely popular. Hence, cyber security threats against the MQTT protocol are projected to rise at par with its increasing use by IoT manufacturers. In particular, the Internet exposed MQTT brokers are vulnerable to protocolbased Application Layer Denial of Service (DoS) attacks, which have been known to cause wide spread service disruptions in legacy systems. In this thesis, we propose Application Layer based DoS attacks that target the authentication and authorisation mechanism of the the MQTT protocol. In addition, we also propose an MQTT protocol attack detection framework based on machine learning. Through extensive experiments, we demonstrate the impact of authentication and authorisation DoS attacks on three opensource MQTT brokers. Based on the proposed DoS attack scenarios, an IoT-MQTT attack dataset was generated to evaluate the e_ectiveness of the proposed framework to detect these malicious attacks. The DoS attack evaluation results obtained indicate that such attacks can overwhelm the MQTT brokers resources even when legitimate access to it was denied and resources were restricted. The evaluations also indicate that the proposed DoS attack scenarios can signi_cantly increase the MQTT message delay, especially in QoS2 messages causing heavy tail latencies. In addition, the proposed MQTT features showed high attack detection accuracy compared to simply using TCP based features to detect MQTT based attacks. It was also observed that the protocol _eld size and length based features drastically reduced the false positive rates and hence, are suitable for detecting IoT based attacks

Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest

Measuring trustworthiness of image data in the internet of things environment

Internet of Things (IoT) image sensors generate huge volumes of digital images every day. However, easy availability and usability of photo editing tools, the vulnerability in communication channels and malicious software have made forgery attacks on image sensor data effortless and thus expose IoT systems to cyberattacks. In IoT applications such as smart cities and surveillance systems, the smooth operation depends on sensors’ sharing data with other sensors of identical or different types. Therefore, a sensor must be able to rely on the data it receives from other sensors; in other words, data must be trustworthy. Sensors deployed in IoT applications are usually limited to low processing and battery power, which prohibits the use of complex cryptography and security mechanism and the adoption of universal security standards by IoT device manufacturers. Hence, estimating the trust of the image sensor data is a defensive solution as these data are used for critical decision-making processes. To our knowledge, only one published work has estimated the trustworthiness of digital images applied to forensic applications. However, that study’s method depends on machine learning prediction scores returned by existing forensic models, which limits its usage where underlying forensics models require different approaches (e.g., machine learning predictions, statistical methods, digital signature, perceptual image hash). Multi-type sensor data correlation and context awareness can improve the trust measurement, which is absent in that study’s model. To address these issues, novel techniques are introduced to accurately estimate the trustworthiness of IoT image sensor data with the aid of complementary non-imagery (numeric) data-generating sensors monitoring the same environment. The trust estimation models run in edge devices, relieving sensors from computationally intensive tasks. First, to detect local image forgery (splicing and copy-move attacks), an innovative image forgery detection method is proposed based on Discrete Cosine Transformation (DCT), Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. Using Support Vector Machine (SVM), the proposed method is extensively tested on four well-known publicly available greyscale and colour image forgery datasets and on an IoT-based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Second, a robust trust estimation framework for IoT image data is proposed, leveraging numeric data-generating sensors deployed in the same area of interest (AoI) in an indoor environment. As low-cost sensors allow many IoT applications to use multiple types of sensors to observe the same AoI, the complementary numeric data of one sensor can be exploited to measure the trust value of another image sensor’s data. A theoretical model is developed using Shannon’s entropy to derive the uncertainty associated with an observed event and Dempster-Shafer theory (DST) for decision fusion. The proposed model’s efficacy in estimating the trust score of image sensor data is analysed by observing a fire event using IoT image and temperature sensor data in an indoor residential setup under different scenarios. The proposed model produces highly accurate trust scores in all scenarios with authentic and forged image data. Finally, as the outdoor environment varies dynamically due to different natural factors (e.g., lighting condition variations in day and night, presence of different objects, smoke, fog, rain, shadow in the scene), a novel trust framework is proposed that is suitable for the outdoor environments with these contextual variations. A transfer learning approach is adopted to derive the decision about an observation from image sensor data, while also a statistical approach is used to derive the decision about the same observation from numeric data generated from other sensors deployed in the same AoI. These decisions are then fused using CertainLogic and compared with DST-based fusion. A testbed was set up using Raspberry Pi microprocessor, image sensor, temperature sensor, edge device, LoRa nodes, LoRaWAN gateway and servers to evaluate the proposed techniques. The results show that CertainLogic is more suitable for measuring the trustworthiness of image sensor data in an outdoor environment.Doctor of Philosoph

Cost-Effective and Energy-Efficient Techniques for Underwater Acoustic Communication Modems

Finally, the modem developed has been tested experimentally in laboratory (aquatic environment) showing that can communicates at different data rates (100..1200 bps) compared to state-of-the-art research modems. The software used include LabVIEW, MATLAB, Simulink, and Multisim (to test the electronic circuit built) has been employed.Underwater wireless sensor networks (UWSNs) are widely used in many applications related to ecosystem monitoring, and many more fields. Due to the absorption of electromagnetic waves in water and line-of-sight communication of optical waves, acoustic waves are the most suitable medium of communication in underwater environments. Underwater acoustic modem (UAM) is responsible for the transmission and reception of acoustic signals in an aquatic channel. Commercial modems may communicate at longer distances with reliability, but they are expensive and less power efficient. Research modems are designed by using a digital-signal-processor (DSP is expensive) and field-programmable-gate-array (FPGA is high power consuming device). In addition to, the use of a microcontroller is also a common practice (which is less expensive) but provides limited computational power. Hence, there is a need for a cost-effective and energy-efficient UAM to be used in budget limited applications. In this thesis different objectives are proposed. First, to identify the limitations of state-of-the-art commercial and research UAMs through a comprehensive survey. The second contribution has been the design of a low-cost acoustic modem for short-range underwater communications by using a single board computer (Raspberry-Pi), and a microcontroller (Atmega328P). The modulator, demodulator and amplifiers are designed with discrete components to reduce the overall cost. The third contribution is to design a web based underwater acoustic communication testbed along with a simulation platform (with underwater channel and sound propagation models), for testing modems. The fourth contribution is to integrate in a single module two important modules present in UAMs: the PSK modulator and the power amplifier

Design, Integration, and Evaluation of IoT-Based Electrochromic Building Envelopes for Visual Comfort and Energy Efficiency

Electrochromic glazing has been identified as the next-generation high-performance glazing material for building envelopes due to its dynamic properties, which allow the buildings to respond to various climate conditions. IoT technologies have improved the sensing, communication, and interactions of building environmental data. Few studies have been done to synthesize the advancements in EC materials and building IoT technologies for better building performance. The challenge remains in the lack of compatible design and simulation tools, limited understanding of integration, and a paucity of evaluation measures to support the convergence between the EC building envelopes and IoT technologies. This research first explores the existing challenges of using EC building envelopes using secondary data analysis and case studies. An IoT-based EC prototype system is developed to demonstrate the feasibility of IoT and EC integration. Functionalities, reliability, interoperability, and scalability are assessed with comparisons of four alternative building envelope systems. Nation-wide evaluations of EC building performance are conducted to show regional differences and trade-offs of visual comfort and energy efficiency. A machine learning approach is proposed to solve the predictive EC control problem under random weather conditions. The best prediction models achieve 91.08% mean accuracy with the 16-climate-zone data set. The importance of predictive variables is also measured in each climate zone to develop a better understanding of the effectiveness of climatic sensors. Additionally, a simulation study is conducted to investigate the relationships between design factors and EC building performance. An instantaneous daylight measure is developed to support active daylight control with IoT-based EC building envelopes

Detecting Impersonation Attacks in a Static WSN

The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks. A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device. The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system