Physical Sciences and Mathematics Commons^™

Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das

Computer Science Faculty Research & Creative Works

UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data …

An Efficient Lightweight Provably Secure Authentication Protocol For Patient Monitoring Using Wireless Medical Sensor Networks, Garima Thakur, Sunil Prajapat, Pankaj Kumar, Ashok Kumar Das, Sachin Shetty

VMASC Publications

The refurbishing of conventional medical network with the wireless medical sensor network has not only amplified the efficiency of the network but concurrently posed different security threats. Previously, Servati and Safkhani had suggested an Internet of Things (IoT) based authentication scheme for the healthcare environment promulgating a secure protocol in resistance to several attacks. However, the analysis demonstrates that the protocol could not withstand user, server, and gateway node impersonation attacks. Further, the protocol fails to resist offline password guessing, ephemeral secret leakage, and gateway-by-passing attacks. To address the security weaknesses, we furnish a lightweight three-factor authentication framework employing the …

Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty

VMASC Publications

Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and …

Apt Adversarial Defence Mechanism For Industrial Iot Enabled Cyber-Physical System, Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

VMASC Publications

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods …

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

Flexible Global Aggregation And Dynamic Client Selection For Federated Learning In Internet Of Vehicles, Tariq Qayyum, Zouheir Trabelsi, Asadullah Tariq, Muhammad Ali, Kadhim Hayawi, Irfan Ud Din

All Works

Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality, …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)

Electrical & Computer Engineering Faculty Publications

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …

Overview Of Research And Application On Autonomous Vehicle Oriented Perception System Simulation, Ruoxuan Wang, Jianping Wu, Hui Xu

Journal of System Simulation

Abstract: Following the rapid progress of science and technology, vehicles with autonomous driving or auxiliary driving function enter into vehicle market. However, in the past decade, traffic accidents still occurred frequently, and the safety of these functions become the focus. Simulation technology provides a good platform to test the perception system of autonomous vehicle. Focus on the sensor simulation modeling of autonomous vehicle perception system, from the perspective of single sensor simulation, multi-sensor simulation and classic simulation platform including millimeter wave radar, lidar and camera, the existing research are reviewed, and the shortcomings and development trends of simulation modeling of …

The Feasibility And Utility Of Harnessing Digital Health To Understand Clinical Trajectories In Medication Treatment For Opioid Use Disorder: D-Tect Study Design And Methodological Considerations, Lisa A. Marsch, Ching-Hua Chen, Sara R. Adams, Asma Asyyed, Monique B. Does, Saeed Hassanpour, Emily Hichborn, Melanie Jackson-Morris, Nicholas C. Jacobson, Heather K. Jones, David Kotz, Chantal A. Lambert-Harris, Zhiguo Li, Bethany Mcleman, Varun Mishra, Catherine Stanger, Geetha Subramaniam, Weiyi Wu, Cynthia I. Campbell

Dartmouth Scholarship

Introduction: Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid …

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar, Beatrice Perez, Gregory Mazzaro, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

Data about users is collected constantly by phones, cameras, Internet websites, and others. The advent of so-called ‘Smart Things' now enable ever-more sensitive data to be collected inside that most private of spaces: the home. The first step in helping users regain control of their information (inside their home) is to alert them to the presence of potentially unwanted electronics. In this paper, we present a system that could help homeowners (or home dwellers) find electronic devices in their living space. Specifically, we demonstrate the use of harmonic radars (sometimes called nonlinear junction detectors), which have also been used in …

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

A Framework For And Design Of A Smart Academic Building Using Sensors, Citizen Participation, And Volunteered Geographic Information, Neelam Raigangar

CGU Theses & Dissertations

Population growth and migration patterns have shown an influx of residents from rural to urban environments. To deal with the problems caused by unprecedented urban influx, cities should plan to use technology in a smart and distinctive way. Tackling at the city scale is hard. But a set of smart buildings that are interconnected by technology will lead to smarter communities which are then interconnected to create a smart city. Smart lobby, building, community, or city is distinguished by its application of integrated software, hardware, and network technologies, along with access to real-time data enabling decision-making, facilitating tracing, tracking and …

Crowdsensing Application On Coalition Game Using Gps And Iot Parking In Smart Cities, Hasan Abu Hilal, Narmeen Abu Hilal, Ala’ Abu Hilal, Tariq Abu Hilal

All Works

This paper provides an overview of crowdsensing and some of its applications. Crowdsensing is a part of the collecting data situations also; it’s built on a data system on multiple customer interactions. Moreover, writing the general information of the smart cities can be used to boost to received number frequency to send messages. This work mentioned the Crowdsensing layers that describe Mobile crowdsensing. The article focuses on crowdsensing layers, developed an application in Coalition Game using crowdsensing in terms of GPS. In addition, this paper discussed the Mobile crowdsensing system and how important the cloud is in serving the wireless …

Comparison Of The Mental Burden On Nursing Care Providers With And Without Mat-Type Sleep State Sensors At A Nursing Home In Tokyo, Japan: Quasi-Experimental Study, Sakiko Itoh, Hwee-Pink Tan, Kenichi Kudo, Yasuko Ogata

Research Collection School Of Computing and Information Systems

Background: Increasing need for nursing care has led to the increased burden on formal caregivers, with those in nursing homes having to deal with exhausting labor. Although research activities on the use of internet of things devices to support nursing care for older adults exist, there is limited evidence on the effectiveness of these interventions among formal caregivers in nursing homes. Objective: This study aims to investigate whether mat-type sleep state sensors for supporting nursing care can reduce the mental burden of formal caregivers in a nursing home. Methods: This was a quasi-experimental study at a nursing home in Tokyo, …

Trajectory Design For Uav-Based Data Collection Using Clustering Model In Smart Farming, Tariq Qayyum, Zouheir Trabelsi, Asad Malik, Kadhim Hayawi

All Works

Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to …

Edge-Iiotset: A New Comprehensive Realistic Cyber Security Dataset Of Iot And Iiot Applications For Centralized And Federated Learning, Mohamed A. Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke

Research outputs 2022 to 2026

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame …

Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu

Electrical & Computer Engineering Faculty Publications

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …

Cognizant Composites: Seamless Integration Of Circuitry And Sensors Into Structural Composites, Reuben Fresquez

Computer Science ETDs

This thesis describes a set of novel techniques for embedding sensors, circuitry, and electronics into structural composites. I leverage recent developments in human computer interaction to create sensors and circuitry that are seamlessly incorporated into structural composites. I fabricate bend and compression sensors, along with circuitry, from textiles, which enables me to add electronic capabilities without impacting the composite’s structural integrity. I describe the construction of these “cognizant composites” and demonstrate their functionality. I also explore techniques for embedding standard electronic components, including microcontrollers, into structural composites. Potential applications of this technology include buildings that can warn occupants if load-bearing …

Energy Harvesting Techniques For Internet Of Things (Iot), Teodora Sanislav, George Dan Mois, Sherali Zeadally, Silviu Corneliu Folea

Information Science Faculty Publications

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the …

A Data-Driven Method For Online Monitoring Tube Wall Thinning Process In Dynamic Noisy Environment, Chen Zhang, Jun Long Lim, Ouyang Liu, Aayush Madan, Yongwei Zhu, Shili Xiang, Kai Wu, Rebecca Yen-Ni Wong, Jiliang Eugene Phua, Karan M. Sabnani, Keng Boon Siah, Wenyu Jiang, Yixin Wang, Emily Jianzhong Hao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many …

Performance-Based Iadl Evaluation Of Older Adults With Cognitive Impairment Within A Smart Home: A Feasibility Study, Iris Rawtaer, Khalid Abdul Jabbar, Xiao Liu, Thit Thit Htat Ying, Anh Thuy Giang, Philip Lin Kiat Yap, Rachel Chin Yee Cheong, Hwee-Pink Tan, Pius Lee Wei Qi, Shiou Liang Wee, Tze Pin Ng

Research Collection School Of Computing and Information Systems

Introduction Mild cognitive impairment (MCI) is characterized by subtle deficits that functional assessment via informant-report measures may not detect. Sensors can potentially detect deficits in everyday functioning in MCI. This study aims to establish feasibility and acceptability of using sensors in a smart home for performance-based assessments of two instrumental activities of daily living (IADLs). Methods Thirty-five older adults (>65 years) performed two IADL tasks in a smart home laboratory equipped with sensors and a web camera. Participants' cognitive states were determined using published criteria including measures of global cognition and comprehensive neuropsychological test batteries. Selected subtasks of the …

Prediction Of Nocturia In Live Alone Elderly Using Unobtrusive In-Home Sensors, Barry Nuqoba, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

Nocturia, or the need to void (or urinate) one or more times in the middle of night time sleeping, represents a significant economic burden for individuals and healthcare systems. Although it can be diagnosed in the hospital, most people tend to regard nocturia as a usual event, resulting in underreported diagnosis and treatment. Data from self-reporting via a voiding diary may be irregular and subjective especially among the elderly due to memory problems. This study aims to detect the presence of nocturia through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental …

A Novel Energy-Efficient Sensor Cloud Model Using Data Prediction And Forecasting Techniques, Kalyan Das, Satyabrata Das, Aurobindo Mohapatra

Karbala International Journal of Modern Science

An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the …

Addressing Rogue Vehicles By Integrating Computer Vision, Activity Monitoring, And Contextual Information, Brook Abegaz, David Chan-Tin, Neil Klingensmith, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

In this paper, we address the detection of rogue autonomous vehicles using an integrated approach involving computer vision, activity monitoring and contextual information. The proposed approach can be used to detect rogue autonomous vehicles using sensors installed on observer vehicles that are used to monitor and identify the behavior of other autonomous vehicles operating on the road. The safe braking distance and the safe following time are computed to identify if an autonomous vehicle is behaving properly. Our preliminary results show that there is a wide variation in both the safe following time and the safe braking distance recorded using …

Using Natural Language Processing And Sentiment Analysis To Augment Traditional User-Centered Design: Development And Usability Study, Curtis L. Petersen, Ryan Halter, David Kotz, Lorie Loeb, Summer B. Cook, Dawna M. Pidgeon, Brock Christensen, John A. Batsis

Dartmouth Scholarship

Background: Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process.

Objective: This study aims …

Arduino Microcontrollers In The Classroom: Teaching How To Phrase Effective Science Questions And How To Answer Them With Original Data, Tony Dinsmore

Science and Engineering Saturday Seminars

Arduino microcontrollers in the classroom: teaching how to phrase effective science questions and how to answer them with original data. Prof. Tony Dinsmore, UMass Physics This workshop will develop course modules that address a challenge in the science curriculum: how do we teach basic problem-solving and curiosity-based research skills in a classroom setting? The standard science curriculum teaches concepts and theory quite well but leaves rather little opportunity for students to take the lead in designing and implementing their own investigations. The workshop will use the Arduino, an inexpensive microcontroller that is simple to set up. A huge range of …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …