A requirement for graduation, undergraduate student teams take on year-long capstone projects, putting their problem-solving skills to the test and collaborating to take an idea from concept to design to prototype. On the fourth Thursday in May, teams present their work to an audience of industry experts, faculty advisors, students, family, and friends of the School of Engineering at the annual Senior Design Conference—a highlight of our academic year.
With the ubiquity of wireless end-devices, more strain is placed on standard network deployment architectures. Mesh networks have started to rise in popularity in order to meet the needs of modern wireless networks. However, the exis- tent solutions for deploying and centrally configuring mesh networks leave much to be desired, as most are either too expensive or too cumbersome. This work showcases a solution to this problem, CentriFi—an open source platform, built to run on OpenWrt access points, providing a quick and easy way to set up and configure mesh networks in a central location using the 802.11s standard. CentriFi provides a web-based front-end for configuring the most crucial settings. Further, the system allows for greater expandability by providing a platform in which other configuration feature can be added by the open-source community in the future.
With the rise of the Internet of Things (IoT) leading to an explosion in the number of internet-connected devices, the current cloud computing paradigm is approaching its limits. Moving data back and forth between its origin and a far-away data center leads to issues regarding privacy, latency, and energy consumption. Edge computing, which instead processes data as close to its origin as possible, offers a promising solution to the pitfalls of cloud computing.
Our proof-of-concept edge computing platform, EdgeAP, is a programmable platform for the delivery of applica- tions on wireless access points. Use cases of the platform will be demonstrated via an example application. Additionally, the viability of edge computing on wireless access points will be thoroughly evaluated.
Roadways play an essential role in toady’s society by contributing to economic growth and development, providing social benefits and fast routes to travel around eciently. With more and more cars on roads, the quality of the streets is deteriorating faster than before. This decrease in road health contributes to hazards such as potholes and can cause significant damage to vehicles on the road. Currently, the process of improving and monitoring roads’ health is done infrequently and is time-consuming for the government. Therefore, many road quality issues are manually reported by the people who drive on them. This requires filling out forms or making phone calls while also remembering the pothole or road hazard location. In this work, we present Drive Health, an Internet of Things (IoT) system developed to monitor the health of roadways and to inform the transit authorities of poor road quality. This device also has the potential to inform the driver on how to be a safer and more ecient driver. Drive Health includes a smart sensor and performs machine learning on accelerometer data to process and analyze the device without using the cloud. If the system determines the data indicates the existence of a pothole, its location is recorded and sent to a web server.
Millions of Americans suffer every year from back problems, now imagine if their was a way to help track and prevent back problems. Our solution to this problem is PostureBot a device that will help its users to correct their back posture and maintain good back posture. In doing so this device can help elevate and prevent people from developing minor and serious back problems in their future.
Distributed denial of service (DDoS) is a highly discussed network attack in Software Defined Networks. Attacks such as the Mirai Botnet threaten to compromise portion of large networks, including home users. Today, corporations secure their network using enterprise level software to protest their network from DDoS attacks. But there solutions are meant for large networks and depend on expensive hardware. There are few security solutions for home users and most are expensive or require a subscription for full protection. We propose a new solution in the form of a plug and play device that will allow home users to easily take control of their network. We will be using the SDN controller Faucet and the protocol OpenFlow 1.3 to enable software defined functionalities. In addition to more basic network features such as blocking websites, the device will allow users to receive notifications about possible malicious activities on their network, generate device profiles for all devices on the network, and automatically detect and mitigate flooding attacks using a random forest classifier. We implement our network virtually using Graphic Network Simulator 3.
Nearly every person who uses WiFi on a daily basis has had trouble with a bad connection. Wireless connectivity issues are often dicult to diagnose and fix. Current solutions such as wired extenders, and Mesh WiFi commercial packages are expensive and do not provide the user with a system that suggests placement of mesh units to maximize coverage. Our solution is an inexpensive and open-source diagnostic tool that maps out Wifi quality and informs the user of interference. With a simple, meaningful display, users will find trouble spots in their house, diagnose why IoT devices are not working, effectively place WiFi extenders and mesh nodes, and more.
There is no place where safety is more important than in the home. Research has shown that home security systems are effective in deterring burglars; additionally, these security systems allow residents to monitor their property at all times, even while they are away. More and more of these home security devices rely on a stable Internet connection and cannot provide functionality without it. ACAS is a system that helps keep smart devices connected to the Internet, even in the event of a home internet outage.
ACAS includes a programmable router that can connect to multiple Internet sources, which sets it apart from other routers on the market. ACAS can connect to two or more Internet sources at a time and then broadcast a wireless Internet signal that one’s smart security devices (and any other device) can connect to. The router uses one Internet source at a time to provide a wireless signal for all devices to connect to it, but in the case that the Internet source goes down for any reason, ACAS automatically switches to one of the other Internet sources connected to it. This provides a reliable backup and keeps devices connected to the Internet as long as one of the multiple Internet sources connected to the router is up and running. Our system also includes a web application that provides the ability to configure some aspects of the router and obtain up-to-date statistics about the workings of the router itself. Users can check the network speed of the Internet connection and choose which of the multiple Internet sources is the main Internet source at any given time.
Minimal local resources, lack of consistency in low level protocols and market pressures contribute to IoT devices being more vulnerable than traditional computing devices. These devices not only have a wide variety of processors and implementations, but they often serve different purposes and generate unique network trac. Current IoT network security solutions fail to account for and handle both the scale at which IoT devices can be deployed and the heterogeneous nature of the trac they produce. In order to accommodate these differences and improve on current solutions, we propose the implementation of a microsegmented firewall for IoT networks. Unlike traditional microsegmented architectures, which use a virtual management layer and hypervisors to manage, route, and filter the trac from VMs, we propose the use of a cloud based management layer working in cooperation with fog node filters to manage end device trac. The fog nodes act as the first hop from the IoT devices, filtering trac according to the rules given to them by the management layer. This decreases packet filtering latency by distributing the computing load and limiting the number of hops packets make for processing. Meanwhile, having a singular management point gives network administrators the convenience of controlling all trac flows at a moments notice as would be the case in a traditional SDN. As a result, this architecture promotes both the adaptability and scalability needed in IoT networks, all while securing trac flows and minimizing latency.
The expansion of the Internet of Things (IoT) has led to numerous innovations in the industry, including improvements to existing systems. Disaster prevention and monitoring systems are a prime example of such systems. Every year, there are significant and preventable financial losses, not to mention the safety hazards caused by floods. To warn people ahead of time, it is possible to deploy low-power wireless sensor nodes to send readings across any terrain to a cloud platform, which can perform pattern analysis, prediction, and alert forwarding to anyone's cellular device. We propose Flomosys, a low-cost, low-power, secure, scalable, reliable, and extensible IoT system for monitoring creek and river water levels. Although there are multiple competing solutions to help mitigate this problem, Flomosys fills a niche not covered by existing solutions. Flomosys can be built inexpensively with off-the-shelf components and scales across vast territories at a low cost per sensor node. This work presents the design and implementation of Flomosys as well as real-world test results.
WASP’s goal is to augment and eventually replace the bulky, costly, and complex data acquisition systems used for vibrational reliability tests on satellites. As a mechanism to guarantee that a spacecraft is mechanically durable and strong enough to withstand the acceleration forces experienced on the vessel during launch, companies conduct vibrational experiments on their spacecrafts by subjecting them to high G-force events. Using wired accelerometers connected to obstructive cables, the mounting process and test setups required to perform such experiments are expensive, laborious, and have the potential to generate measurement inaccuracies. We developed a low-cost, battery-powered module, designed for engineers, to replace the current sensors and data acquisition systems with a wireless solution. This will enable precise testing of conditions on a smaller time frame and at a lower cost and help eliminate the disadvantages of a wired system. A custom circuit board has been fabricated containing the critical measurement and processing components required to realize this objective, as well as a complete software solution to facilitate data transmission to a wireless router over WiFi.
The key to becoming a more sustainable society is first learning to take responsibility for the role we play in energy consumption. Real-time energy usage gives energy consumers a sense of responsibility over what they can do to accomplish a much larger goal for the planet, and practically speaking, what they can do to lower the cost to their wallets. Synergy is an energy monitoring and visualization system that enables users to gather information about the energy consumption in a building – small or large – and display that data for the user in real-time. The gathered energy usage data is processed on the edge before being stored in the cloud. The two main benefits of edge processing are issuing electricity hazard warnings immediately and preserving user privacy. In addition to being a scalable solution that intended for use in individual households, commercial offices and city power grids, Synergy is open-source so that it can be implemented more widely. This paper contains a system overview as well as initial finding based on the data collected by Synergy before assessing the impact the system can have on society.
We present an ecient multipurpose system capable of providing professors the ability to help students meet with them outside of class. Currently, should a student wish to schedule a meeting with a professor, they often have to initiate a long string of emails until a final date and time can be agreed upon. Additionally, professors have no way to simply broadcast messages to people visiting their oce nor a way to take messages from those individuals should a professor be absent. Our solution aims to fix these issues through the use of several low-power, budget-friendly devices. Our platform includes a touchscreen powered by a Raspberry Pi 3 which will function to display the professor’s public calendar and announcements. Additionally, a camera is used in order to perform face recognition to map students to accounts they create to interface with the system. Finally, we created a mobile app that will allow student and professor to communicate quickly in a way that abstracts their personal phone numbers.
The visually impaired rely heavily on hearing and touching (with their cane) to navigate through life. These senses cannot make up for the loss of vision when identifying objects in the user’s path. In this paper, we propose NavSense, an assistive device that supplements existing technology to improve navigation and peace of mind in day to day life. NavSense provides real-time object identification and context to the user through auditory feedback. The device reduces inference time by 50% without significant power consumption increases. We plan to continue testing on different platforms to identify lower power embedded systems computers to further improve the power consumption of the device.
Doorbell options for hearing impaired individuals is seriously limited. Affordable solutions are not scalable while other solutions are expensive. With this in mind, we designed a scalable and affordable system that will be beneficial to hearing impaired individuals in a small aspect of their life. Our solution takes advantage of affordable IoT devices and software to build a proof of concept. Due to the scope of the project, we only designed a proof of concept, in the hope that a company can design a viable product that will not only benefit hearing impaired individuals but bring a powerful IoT system to the homes of others.
This system allows network utilities to be used from a web-based interface to monitor and manage the transfer of data. The system runs primarily on Raspberry Pis using Raspbian Linux. Users can access the system through web browsers to both configure the system and interact with the data on the network. We discuss our motivation for the project, design decisions made, technologies used and more throughout this report. We conclude with some lessons learned and future work to be done.
Travelers often lose interest and joy when traveling in tourist-packed areas around the world. As more restaurants and attractions open up in popular cities, the wait and travel time from one location to another inevitably increases. Each attraction has certain hours throughout the day where visitors surge and the wait times increase. In addition, trac and travel time is an important factor to consider when looking to optimize ones trip. However, with large amounts of attractions, it is dicult to calculate and consider the most optimal routes and times an individual should use to visit each possible attraction. Travelers ultimately face an issue with maximizing productivity for their trips. Our goal is to create a mobile application that utilizes the data from the Google Directions API and Foursquare API to produce an optimal itinerary for travelers to use. Travelers will be able to input their place of stay, attractions they want to visit at their preferred times, and other time constraints to produce an itinerary that will allow the tourist to visit each attraction they please. The Optimal Itinerary Generator will eliminate blind spots in travel planning and as a result, make vacation trips more time ecient and enjoyable.
Parkinson’s Disease (PD) is a progressive neurological disease that affects 6.2 million people worldwide. The most popular clinical method to measure PD tremor severity is a standardized test called the Unified Parkinson’s Disease Rating Scale (UPDRS), which is performed subjectively by a medical professional. Due to infrequent checkups and human error introduced into the process, treatment is not optimally adjusted for PD patients. According to a recent review there are two devices recommended to objectively quantify PD symptom severity. Both devices record a patient’s tremors using inertial measurement units (IMUs). One is not currently available for over the counter purchases, as they are currently undergoing clinical trials. It has also been used in studies to evaluate to UPDRS scoring in home environments using an Android application to drive the tests. The other is an accessible product used by researchers to design home monitoring systems for PD tremors at home. Unfortunately, this product includes only the sensor and requires technical expertise and resources to set up the system. In this paper, we propose a low-cost and energy-efficient hybrid system that monitors a patient’s daily actions to quantify hand and finger tremors based on relevant UPDRS tests using IMUs and surface Electromyography (sEMG). This device can operate in a home or hospital environment and reduces the cost of evaluating UPDRS scores from both patient and the clinician’s perspectives. The system consists of a wearable device that collects data and wirelessly communicates with a local server that performs data analysis. The system does not require any choreographed actions so that there is no need for the user to follow any unwieldy peripheral. In order to avoid frequent battery replacement, we employ a very low-power wireless technology and optimize the software for energy efficiency. Each collected signal is filtered for motion classification, where the system determines what analysis methods best fit with each period of signals. The corresponding UPDRS algorithms are then used to analyze the signals and give a score to the patient. We explore six different machine learning algorithms to classify a patient’s actions into appropriate UPDRS tests. To verify the platform’s usability, we conducted several tests. We measured the accuracy of our main sensors by comparing them with a medically approved industry device. The our device and the industry device show similarities in measurements with errors acceptable for the large difference in cost. We tested the lifetime of the device to be 15.16 hours minimum assuming the device is constantly on. Our filters work reliably, demonstrating a high level of similarity to the expected data. Finally, the device is run through and end-to-end sequence, where we demonstrate that the platform can collect data and produce a score estimate for the medical professionals.
Urban air pollution leads to widespread respiratory illness and millions of deaths annually. PM2.5, particulate matter with a diameter less than 2.5 micrometers, is the product of many common combustion reactions and poses a particularly serious health risk. Its small size allows it to penetrate deep into the lungs and enter the bloodstream. Existing air quality monitors are aimed at scientific research, differentiating between pollutants and providing high accuracy in measurement. These devices are prohibitively expensive and cannot easily be carried around. Due to the highly localized nature of air pollution, and in order to allow individuals and institutions to easily monitor their real-time exposure to PM2.5, we propose Halo, an air quality monitor costing less than $100. Halo is powered by a 500 mW solar panel and equipped with a 1500 mAh Lithium-Ion battery in order to handle 150 mW peak power consumption and operate continuously for over 24 hours without power input. The device is small enough to be clipped to a backpack or bag for easy portability, and it can be used in personal or public settings. Using an IR emitter and detector, Halo measures reflected IR light to determine the particulate concentration in the air with an error less than 10%. It uses Bluetooth Low Energy (BLE) to communicate these values to a user’s phone. From the phone, air data can be time-stamped, stored in a cloud database, and visualized in an app for easy monitoring of pollution trends and pollution exposure. Additionally, the cloud database allows for the aggregation of data from multiple devices to create crowdsourced pollution maps. These maps can be used to pinpoint areas with particularly bad air quality in order to try to make changes to these areas or to help users to know to avoid these areas in possible.
The majority of the energy within the United States is wasted. In addition, buildings such as apartment complexes and high-rises consume large amounts of energy, with commercial buildings wasting, on average, 30% of the energy that they consume. This issue leads to drastic consequences such as an increase in carbon footprint and high energy costs. With this in mind, our team was inspired to create a solution that decreases energy consumption and cost. Our project achieves this goal with a scalable and personalized smart home system that caters to individual users’ needs while conserving energy on a large scale. Our solution, SmartSys, cuts energy consumption as well as energy costs through interaction with IoT devices, an architecture that includes a combination of database-centric and event-driven data flows, and various technologies including sensors and machine learning. As a result of single room testing, we estimate that SmartSys will help indivdual users save over $1000 over a time period of 20 years in addition to saving a city with 20 apartment complexes over 150 million KWh after 20 years. For future work, we hope to decrease the fixed cost of SmartSys to make our solution have an even greater impact on energy cost savings while maintaining its energy saving performance. In addition, we hope to engage in multiple room testing as well as scale SmartSys to function throughout a large building.