• Examples of Use Cases

      Students are invited to submit their project abstracts in areas related to the advancement of technology and applications related to the following focus areas: Use cases in AI, IoT and Multi-Cloud: (This sector will be in separate tab in the website)

      *Please note that you can present any solution not listed in the below use cases if it fits our areas of interest.

      The following is a non-exhaustive list of example project areas that fit within one or more of the focus areas of the Envision the Future Competition this year:

      Artificial Intelligence:

      Artificial Intelligence is one of the emerging technologies that strengthen the partnership between humans and machines, thus improving the ability of computers to improve human lives. The increase in available data, coupled with the significant improvements in computational power and the development of better algorithms, have made it feasible to use AI in breaking new frontiers.

      Healthcare, Wellness, medical research and drug discovery are all very interesting fields for applying different AI solutions. These solutions have the potential to improve the connected patient and physician experience, increase efficiencies throughout the healthcare ecosystem, accelerate pharmaceutical development, enhance the decision-making process, improve diagnosis and treatment results, and ensure better wellness through accurate early prediction and prescriptive analytics.

      However, fully leveraging these new technologies requires building innovative solutions and applications that target some of the ongoing challenges in the healthcare field. These include the ability to manage data without sacrificing safety and privacy, ensure transparency, produce unbiased explainable predictions, and address the significant engineering overheads that are encountered when putting these techniques into production. There have been significant demonstrations of the potential utility of Artificial Intelligence approaches such as deep learning techniques, which is used in medical diagnostics in the current developing applications. However, these solutions could lead to wrong predictions if the training data is not realistic or dynamic. This was shown in fields such as drug side-effects or treatment resistance where the inputs are dynamic.

      *To help you with the brainstorming, here are some general use cases covering different aspects of healthcare sector:

      1. HealthCare AI Use Cases
      • Medical Diagnosis
      AI systems can analyze data far faster than humans, which may lead to faster, efficient and, if trained correctly, much accurate identification of medical diagnoses than doctors. AI uses historical data from up to millions of diagnoses and then compares that with a patient's current condition to diagnose their health condition, to predict the level of severity, the progression of it and to recommend treatment. Most diagnoses depend on a pathology or radiology result, so a pathology report's accuracy can make the difference between diagnosis and misdiagnosis. AI can "see" pathology results at the pixel level which can indicate the progression of cancer.
      • Disease Treatment & Drug Creation
      AI has been used in different disease treatments. For example, in cardiology an example of AI use is an implantable defibrillator that monitors the heart rhythms of patients at risk of a sudden heart attack. The device also administers a shock if necessary. In Neurology also, AI can be used monitor patients with neurological disorders to see whether the patient's status is improving or declining. AI can also predict strokes and monitor seizure frequency. For Cancer treatment it is believed that AI is capable of recognizing patterns in genetics strings and correlating those against immunotherapy options and thus resulting in a personalized approach to cancer treatment. AI can help reduce the cost of medicine by reducing the cost of R&D where it can be used to identify genes that cause antibiotic resistance in bacteria.
      • Clinical Settings
      Different predictive models are being developed to assist in clinical settings this includes using electronic medical record data to predict hospital-acquired conditions like operational models that predict patient admission rates into the emergency department, or financial models that can be used to identify new bundled service offerings. Also, data from the electronic medical record to predict which patients are at highest risk for needing an intervention from a rapid response team and thus proactively position the needed help and decrease the response time. Also, some applications are being developed to assist with the execution of certain tasks so that physicians and nurses can concentrate on the more critical ones. Currently, the power of AI and analytics is also used to design proactive plans that result in the earlier and more efficient treatment of patients. Also, Natural Language Processing (NLP) can help doctors to filter out relevant information from lengthy reports.
      1. Wellness Use Cases
      • Wellness Tracking Applications
      More consumers are wearing health and fitness bands or smart watches. Such devices, depending on their design can provide insight into a person's heart rate, oxygen level, sugar level, sleep patterns, breathing and more, providing healthcare providers or doctors with information they wouldn't otherwise get between appointments. The main target is determining and detecting patterns in our daily routines that may cause certain diseases in the future.
      • Sleep tracking applications
      Developed to provide the means to monitor sleep information without the need to connect patients to wired sensors and thus be able to understand how sleep affects the overall health and productivity of an individual and define the main factors affecting the quality of sleep.
      • Workplace Stress-Control Application
      Developed to ensure a stress-less working environment, this is using emotion recognition technology, Natural Language Classifiers, Dialog, Emotion Analysis and Sentiment Analysis to register employee emotions in a way that can help companies properly reach out to them and provide them with what they need to ensure the highest rates of employee satisfaction with the workplace.
      • Life Coaching Application
      Follow-ups are an essential part of healthcare, especially if a patient is suffering from a chronic disease. In most hospitals and clinics, doctors offer regular follow-up or life coaching as a part of their treatment. However, such services can be extremely costly, and not every patient can afford it. With an AI-enabled mobile app the needed data is captured from the wearable device and accordingly the app suggests the required medication, exercises, activities, and even habits, which will help them live a healthier life.
      • Save the planet using AI (Sustainable Environment)
      AI is shaping up to be one of society’s most helpful tools. With it, we can start making significant amends with our land, air, and water and clean it up for a healthier planet.
      Through machine learning, robotics, drones, and the internet of things (IoT), society can achieve better monitoring, understanding, and prevention of damage and stressors on Earth’s land, air, and water. Even technology already available today could reduce energy usage in the U.S. by 12 to 22 percent, according to The Information Technology Industry Council (ITI),
      AI will make renewable energy technology like solar panels and wind turbines more efficient and cost effective, helping them to become ubiquitous and lower society’s dependence on fossil fuels.
      AI has the power to completely transform agricultural practices to make them safer for the Earth and people’s health. Machine learning combined with robotics can provide automated data collection and decision-making to optimize farming processes. These systems can interact directly with crops to detect and act on the best times to plant, spray, and harvest, decreasing the need for the fertilizers and pesticides polluting the soil. This will make farming not only more efficient, but it will also lead to more organic, earth-friendly crops. Similarly, AI monitoring can help societies protect areas of land larger than just farms; ecosystems and habitats all over the world can benefit as well. AI-enabled drones, for example, are providing new opportunities to observe and protect endangered areas. More effective plant disease detection, poacher route prediction, erosion monitoring, species identification, and animal migration tracking are all a reality with AI, according to a World Economic Forum report.
      Internet of Things:
      The power of IoT systems lies in their ability to sense, process, and communicate. On the other hand, with great powers comes a lot of challenges. For example, Information that is being delivered from sensors may appear to be correct but could be corrupted somehow at the origin or during transmission. Moreover, malware might deliberately alter information that would then be used to make life and death decisions. So, there is a call to develop a solution that allows us to trust the information delivered through an IoT healthcare system.
      Another challenge is that there are different IoT healthcare frameworks that are designed and implemented by various researches. So, there is a need to compare and improve such frameworks to enhance the safety, usability, performance, reliability, energy usage, longevity, and overall cost of such devices and systems.
      Healthcare organizations and hospitals often deal with a huge number of patients, with multiple support staff performing various duties. A solution which covers proper data management in these situations must be developed and this must ensure accurate identification of patients and staff while developing IoT healthcare solutions that adapt to the mobility of patients in various environments, keeping them connected anytime, anywhere.

      To help you with the brainstorming, here are some general Healthcare & Well-being IoT covering different aspects of healthcare sector:

      1. Healthcare Use Cases
      IoT devices have been huge contributors in the development of different solutions in the medical sector for years. They are used to monitor data remotely and thus help take instant decisions whenever needed. This takes into consideration any dynamic changes including any changes in the data itself, this instant decision is done through a myriad of solutions that also use the assistance of two other powerful fields together with the IoT which are: Artificial Intelligence and Cloud Architecture. Below are some examples of such applications:
      • IoT Applications Security
      Due to the small size and low computing power of IoT devices, there must be a focus on securing such devices. Any exposure of any healthcare related data to hackers, terrorists, or any untrusted entities may have deadly consequences. Therefore, one of the challenges that should be targeted are the current security solutions implemented in healthcare IoT enabled devices and how to improve whether it is on the device or network level, to avoid any preaches or data manipulation.
      • IoT and sleeping patterns, detection, methods and variables
      For years, scientists have been studying human sleeping behavior. This is a challenging task, as it requires taking a lot of measurements about the quality, length of sleep, brain and muscle activity, etc. from the patient. Nowadays, there are some mobile applications that can help track sleeping patterns and other behaviors during sleep. How can IoT devices aid researches in the task of studying sleep behavior, potentially understanding and increasing the quality human sleep?
      • IoT equipped offices

      Statistics based on data collected from companies have revealed that early detections of employee well-being. This can significantly reduce the cost of healthcare, due to the strong link between physical and mental health. In some cases, this may have catastrophic consequences, such as physician burnout. Companies need to focus on improving employee wellbeing to foster better results and enhance productivity. Luckily, IoT devices such as noise sensors, light sensors, microphones and cameras detecting emotions can aid in the detection and prevention of healthcare problems arousing from the workplace, along with analytics from the employee’s medical data.

      1. IoT Wellness Use Cases

      With the increase in awareness of environmental issues related to pollution (Global warming, etc.), a need arises for the early detection of problems and emissions affecting air, water, and human-being’s surroundings. This provides an opportunity for IoT enabled eco-systems to help in the detection and alarming of the problems in the early stages. Below are some examples:

      • Predictive maintenance of vehicles components
      Vehicles can produce harmful emissions to the environment, if we were to detect engine failures prior to its occurrence using IoT sensors that fit inside the engine and the vehicle exhaust to notify the driver which helps in decreasing those emissions. This will lead to the wellbeing of the environment and the population.
      • Eco-friendly agricultural farms
      Farmers may use many harmful pesticides and chemicals affecting the health of the nearby ecosystems. IoT devices can help in reducing, detecting, and reporting any violation to the agricultural environmental rules, through sensors to detect the amount of chemicals used in the cultivated crops and the chemicals dumped in the nearby soil and water bodies.
      A multi-cloud approach leverages more than one cloud service provider. These cloud providers can be either private or public. Multi-cloud approach caters to several use-cases including disaster recovery, cost savings and performance.
      Healthcare multi-cloud is becoming an attractive way for organizations to deal with the explosion of digital health data in electronic health records, connected devices, Internet of Things, and healthcare apps. 93% of companies will use more than one cloud, thus offering flexibility, resiliency and consistency. In two years, it is predicted that most enterprises will use over five cloud platforms to run businesses operations.
      Managing a multi-cloud world will be the number 1 companywide issue by 2021. Take risk reduction, compliance or SLAs. These often require swapping workloads from public cloud to private cloud. While some developers want to code directly to public clouds. Dell Technologies makes managing workloads across environments easier.
      To help you with the brainstorming, here are some general use cases covering different aspects of the healthcare and wellness sector:
      The use cases of Multi-Cloud don’t impact the humans directly as it’s about the infrastructure itself that empowers other technologies like AI and Machine Learning, but it still has impact to the IT industry in the following use cases, that will accordingly affect the healthcare sector:
      1. Disaster recovery/high availability
        Distributing cloud workloads across different cloud providers allows companies to build resilient and robust applications. This ensures continued operations during outages and failures across cloud providers.
      2. Cost optimization
        Not all cloud providers are born equal. The same applies to the individual services they provide. Cloud services can be priced differently by several cloud providers. Companies looking to optimize cloud usage costs can choose services across cloud providers based on their individual price points by implementing a multi-cloud approach.
      3. Unique benefits
        In the same way that cloud providers have differing costs, individual services also differ in how well they suit a company’s needs. Deploying multi-cloud workloads enables companies to pick and choose services based on their unique requirements.
      4. Avoid vendor lock-in
        Multi-cloud architectures also allow companies to reduce their dependence on any one single cloud provider
      5. Scalability
        Multi-cloud approach also adds an additional layer of scalability on top of public cloud service providers and allows companies to provision resources as and when application and traffic requirements demand.
      6. Compliance
        Multi-cloud approach also enables companies to meet compliance requirements. Not all cloud providers have a presence in markets where companies are required to keep a local presence. Leveraging multiple datacenters from several cloud providers enable them to meet these requirements.
      7. More edge locations
        By leveraging regional presence of multiple cloud providers companies can push their applications nearer to end-users allowing them to provide a more reliable and optimized level of performance.
      8. Applications Integration.
        With the increasing amount of data, you might use from many different sources connecting these structured and unstructured data. You might feel overwhelmed with the amount of integrations between the applications which will consume these datasets. It’s a challenging part to be addressed in the next few years.
      9. Workload Migration
        In business, the change is always considered as a risky step Maintaining the data itself and the applications serviceability with the same quality, always have been the main concern during the migration plans, which tells, how the reliability of the migration platform and the new workload management platform, plays the most important role in this area.
      10. Operational Efficiency
        In a multi-cloud environment, the orchestration between different service providers is not easy, especially when you have different live data feeders and analytics services. This should immediately get a result to do a final action or give an accurate recommendation, that could be “saving someone’s life”.
        That’s why this is a challenging area. Having the distance between providers, data forms, applications severity and decision-making efficiency, will make the automation of the operations and its efficiency a very important area to develop in.
      Although our focus areas in the competition are IoT, AI and Multi-cloud, we encourage students to include more trending technologies in their project planning such as 5G and Edge Computing. We are adding below some concepts that can enrich your options to select a state-of-the-art project idea:
      Edge Computing:
      Edge computing is a distributed computing paradigm, which can distribute complex computations and run them on multiple edge devices, resulting in faster computations and more efficient connection bandwidth. It is used to run the computation where its data was originated on the edge device (for example: sensors and gateways) which will significantly reduce the communication costs, resulting also in taking decisions where the event happens which ensures that the best decision will be taken as fast as possible even if there is a network issue. It has multiple applications such as autonomous vehicles and IoT devices.
      The fourth industrial revolution, often known as 4IR or Industry 4.0 is the revolution of the current era of technology that we’re standing on the brink of. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before, fundamentally impacting how we live, work, and relate to one another. 5G plays an important role in the 4IR, bringing intelligent connectivity by promising ultra-low latency and massive speed.
      In its core, 5G technology provides wireless communication 10x faster than the previous generations, rivaling fiber-optic cable speeds. However, 5G is about more than just speed. The most impactful feature of 5G is its low latency. Latency refers to the delay between an instruction being given and executed. 4G has a latency of about 50ms; 5G will reduce this to less than 5ms in three years and less than 1ms in four years. For comparison, the human brain takes about 10ms to process an image. Another exciting innovation, which will come with the deployment of 5G, is network slicing. Put simply, this enables networks to be segmented to meet the requirements of specific services, such as guaranteed resilience, bandwidth or low latency.
      The race to 5G adoption is currently in full-speed, only held back by the COVID-19 pandemic crisis. To date, there are 50 commercial 5G network deployments in 27 countries. The approximate number of 5G wireless connections in 2019, according to IDC is 10 million. This number is expected to exceed 1 billion by 2023. As 5G rolls out, it will enable new applications that may not be viable today, particularly in urban areas and cities. This will probably take time as the 5G infrastructure is gradually deployed. Some example use-cases are given below:
      Unleashing AI
      Applying AI to an immense amount of data at scale will be accelerated with fast, efficient connectivity. For example, smart city AI could correlate traffic light data automatically and implement new patterns after an apartment complex nearby is opened. Smart security and machine vision can keep secure facilities safe with automatic recognition of potential security breaches or unauthorized visitors.
      While 5G will help enable AI inference at the edge, it will also play a role in delivering data from devices to the central cloud to train or refine AI models. For example, real-world data about road conditions collected by connected vehicles can improve cloud-based mapping services.
      Connectivity for Edge Computing
      With the move to cloud-native 5G networks, enterprises can take advantage of strategically distributed computational power, allowing more data to be processed and stored in the right place based on the needs of the application. Intelligent edge computing operates at the convergence of 5G’s ultra-low latency, IoT, and AI technologies. Devices and applications can tap into edge cloud computing resources without needing to access a centralized data center potentially thousands of miles away.
      As 5G edge computing becomes more pervasive, industries will be able to dramatically scale up their use of data and act on insights faster—often instantly and autonomously.
      IoT in Industry and Manufacturing
      Industrial automation is in use today, and most likely you have seen videos showing synchronized robotics at work in factories and supply chain applications. Today these applications require cables, as Wi-Fi does not provide the range, mobility and quality of service required for industrial control, and the latency of today’s cellular technology is too high. With 5G, industrial automation applications can cut the cord and go fully wireless, enabling more efficient smart factories.
      Augmented Reality (AR) and Virtual Reality (VR)


      The low latency of 5G will make AR and VR applications both immersive and far more interactive. In industrial applications, for example, a technician wearing 5G AR goggles could see an overlay of a machine that would identify parts, provide repair instructions, or show parts that are not safe to touch. The opportunities for highly responsive industrial applications that support complex tasks will be extensive.
      Guidance, resources, and technical support
      The Dell Technologies competition website will be the source and platform for all project related queries and FAQs. The participating groups are required to use only Open Source tools.

      Dell Technologies is the sole sponsor of this competition, but it is not providing any funding or technical sponsorship to any project team that choses to participate in the competition.
      Helpful reference links for students:
      For any inquiry please contact the competition helpdesk EnvisiontheFuture@emc.com