Secure and Resilient IoT-based Monitoring for Greener Environments

I provide a three-tier IoT solution with UAV-assisted robotic monitoring IoT system for scalable and verifiable (secure, transparent and immutable by blockchain with AI) environmental monitoring and data collection to track environmental conditions. 

It will help especially the people in poor and developing countries have resilient ecosystems. Therefore, my three-tier solution can affect the lives of hundreds of millions or perhaps billions of people on a global scale especially when you consider African countries and southeast Asia countries like China and India.

For resilient ecosystems, we need to monitor the environments fast&securely and show the required real-time reactions against anomalies in case anomalies are detected.

By proposing a UAV-Assisted clustered-robotic monitoring system, we aim to collect data from the environment in a secure, time-aware, energy-aware and efficiency-aware way. For this purpose, we should solve the following three subproblems.

In the first tier, each cluster member (CM) robot needs to collect data from surrounding sensors in a time-aware and energy-aware way. For this purpose, it needs scalable scheduling (data collection) algorithms.

In the second tier, each cluster head (CH) robot needs to collect data from  CM robots in a secure, transparent and time-aware way. For this purpose, it needs blockchain protocols whereby the data can be stored securely and shared transparently among all CM robots in the cluster, which increases the resilience of the system against malicious attacks in the IoT era. It is also required for CH election among robots.

In the third tier, a UAV with a limited-capacity battery needs to collect data from the CH robots in an efficiency-aware and energy-aware way. For this purpose, it needs not only energy-aware but also efficiency-aware routing algorithms coupled with trajectory optimization algorithms.

We propose a 3-tier solution via a UAV-assisted robotic monitoring IoT system that consists of a UAV, robot clusters, and sensor clusters.

In the first tier, each cluster member (CM) robot collects environmental data from sensors by using Uniforming Random Ordered Policy (UROP). Here, UROP uses no feedback to learn battery and buffer states of sensors. Thus, it is a time-aware&energy-aware data collection policy by reducing communications overheads.

In the second tier, a cluster head (CH) robot collects data from cluster member (CM) robots by blockchain supported with AI. Here, blockchain provides data transparency and security. Thus, the collected and evaluated data can be shared among the robots, which helps robots elect new cluster head easier unless the current CH robot performs well and has sufficient energy.

In the third tier, a UAV with a limited-capacity battery collects data from CH robots by visiting an optimal portion of them depending on its battery capacity. Here, we aim to minimize total joint cost of energy consumption and data efficiencies of CH robots by visiting the optimal set of CH robots via Quality and Energy-Aware Data Collection Strategy (QEADCS).

Hence, the proposed robotic monitoring system collects data from the environment in a secure, time-aware, energy-aware and quality-aware way.

Our target population is people especially in rural areas of low-income or developing countries. Especially because of bad economies in these countries, companies may look for ways of not using costly filters that can industrial wastes from mixing air, soil or water, which will affect housing. Not using these filters sometimes causes severe environmental problems, which causes economic and psychological problems. 

Proper data collection so important in two main purposes: 

1) Anomaly detection: If there is an anomaly or attack (cyber or physical) which may cause serious consequences like fire, then our environmental monitoring system gives a warning about that. Thus, precautions can be taken to tackle the anomalies and other problems.

2) Fast adaptation against sudden changes. If there is a sudden change in temperature, moisture, humidity, wind, etc, then the environmental system gives a warning about that. Thus, precautions can be taken to tackle these changes. For example, moisture and humidity may cause dampness in houses, which should be taken seriously.

As a further example from Turkey in the two months last year, the Marmara Sea suffers from mucilage which is caused by industrial wastes mixing directly into the sea for the last two to three decades. This mucilage in fact killed the sea by harming biodiversity and biological life in the Marmara Sea. The negative consequences are not only biological and economic but also psychological and cultural. While people watch the blue Marmara Sea to become more relaxed for decades, they watch its illness appearing with mucilage to go towards its death for two months last year. This was a striking observation for me about sea pollution.

As a person living and loving the Marmara Region in Turkey for three decades, I was unfortunately among such underserved people for sea pollution and especially air pollution because of the unfiltered industrial companies.  Therefore, I can understand the needs of such underserved people, and I benefit from this unfortunate experience as I develop my solution.

To overcome such problems, we need scalable and verifiable monitoring and data collection to track ecosystem conditions, such as biodiversity, carbon stocks, or productivity. If we use such a monitoring and data collection system, then we can detect such environmental problems at their initial phase and take necessary precautions to avoid the later stages of environmental problems like mucilage.

To solve this problem of resilient ecosystems, I provide a UAV-assisted robotic monitoring IoT system for scalable and verifiable (secure, transparent and immutable by blockchain with AI) monitoring and data collection to track ecosystem conditions, such as biodiversity, carbon stocks, or productivity.

I have already published, submitted, and prepared 40+ papers and 4 book chapters on computer&communicatio networks, IoT, UAV, robotics, routing, scheduling algorithms, blockchain and artificial intelligence. I have worked on this solution for ten years (since 2013 summer when I started work on the first tier of this solution). 

One of my journal paper "Asymptotically Throughput Optimal Scheduling Policy for Energy Harvesting Wireless Sensor Networks" was awarded 3rd place in 2019 Lance Stafford Larson Outstanding Student Paper Award by the IEEE Computer Society (the algorithm in the 1st tier of this solution).

My another work "Efficiency-Aware and Energy-Aware Data Collection via UAV with Limited-Capacity Battery in Robotic Wireless Sensor Networks in Post Covid-19 Era" was awarded 3rd place in the Poster Competition at the 2021 IEEE Rising Stars Global Conference (the algorithm in the 3rd tier of this solution).

I was a member of the IEEE Identity of Things Working Group (P2958). (the algorithm in the 2nd tier of this proposed solution). Moreover, my journal paper on AI-driven blockchain-enabled mobile crowdsensing was published in ACM Distributed Ledger Technologies. In addition, my book chapter entitled "Blockchain-enabled Internet of Things (IoTs) Platforms for Vehicle Sensing and Transportation Monitoring" for contributing a book entitled "Machine Learning-Based Blockchain Technologies for IoTs and Big Data: Fundamentals, methods, and applications" was published by IET. Furthermore, I am preparing a new paper on lightweight blockchain protocols (the algorithm in the 2nd tier of this solution). 

In 2022 calendar year, I worked on two industrial projects on applied machine learning and wireless radio access networks at the University of Ottawa Campus at Kanata North, the largest tech hub in Canada. 

Since January 2023, I have worked as an assistant professor in the Department of Computer Engineering at Bahcesehir University in Turkiye where I have supervised 4 MSc students (1 in Computer Engineering, 2 in Artificial Intelligence, and 1 in Big Data Analytics) and I have given courses on advanced computer networks and mobile wireless networks. I have just started to prepare an early-career project based on UAV-aided IoT monitoring systems. I am also an editor of three Springer journals, namely, Telecommunication Systems, Wireless Networks, and Cluster Computing.

Finally, I will organize a special issue on "Robotics and Wireless Communications Networks in Smart Agriculture" in our national conference and national journals to increase public awareness about the importance of 'smart agriculture and climate change' in my country, Turkiye.

My solution is about a functional stage of testing its feasibility, and performance. Therefore, it is a prototype rather than a concept. 

By considering the feasibility and real-life conditions, I have been developing my solution.

I am applying to MIT Solve which can provide us very good opportunities. especially because of the following reasons.

I can benefit from the funding and marketing opportunities.

I can benefit from access to mentorship, coaching, and strategic advice from experts, as well as the Solve and MIT networks. 

We can receive monitoring and evaluation support to build an impact measurement practice. This is very important in marketing aspects.

In the first tier and the third tier of my proposed 3-tier solution, I use completely novel and robust algorithms which have already been awarded by the Institute of Electrical and Electronics Engineers (IEEE) and IEEE Computer Society.

In the first tier, I apply my novel data collection algorithm, Uniforming Random Ordered Policy (UROP), which I proposed in my IEEE journal paper published in 2018. (This paper was awarded third place in 2019 Lance Stafford Larson Outstanding Student Paper Award by IEEE Computer Society). INNOVATION 1: Via UROP, a robot/node can achieve asymptotically optimal throughput (data) from the surrounding sensor nodes by reducing the communications overhead because UROP does not need feedback to learn battery&buffer states of the nodes from which the robot collects data.

In the third tier, I apply my another novel algorithm Efficiency&Energy-Aware Data Collection Strategy (EEADCS) which I proposed in my journal paper published in 2020. (This work was awarded third place in the Poster Competition at 2021 IEEE Rising Stars Global Conference in January 2021). INNOVATION 2: EEADCS passes beyond existing approaches since we consider keeping the total energy consumption of UAV below its limited battery capacity coupled to minimum total cost data collection from CH robots by visiting an optimal portion of CH robots.

In the second tier, I apply a lightweight scalable blockchain protocol on which I am still working to improve by using artificial intelligence (AI) techniques.  INNOVATION 3: I aim to derive a more lightweight and scalable blockchain protocol.

My solution has an impact of theory of change.

My proposed environmental monitoring system will provide a fast, scalable, and secure solution to anomalies or sudden changes in the environment. 

By reducing latency and energy consumption of the components in the system, we will make the environmental monitoring system more feasible and sustainable.

As said in the previous part, my environment-friendly solution may affect millions of people.

I plan the solution to serve millions of people in one year. It can be easily observed that many developing countries faces with air pollution with high carbon emission, especially in industrial developing cities.

In five years, I aim my solution to be globally used in poor and developing countries to have resilient ecosystems. Thus, it may affect hundreds of millions or even billions of people.

We propose a UAV-assisted robotic monitoring system where we use UAV (called DRONE if we consider commercial use) and a robotic&wireless sensor network that consists of clusters of robots and sensor nodes.

In the first tier of our solution, we use a time-aware and energy-aware scheduling algorithm UROP for each robot to collect data from surrounding sensors. It reduces the delay and energy consumption of the IoT network edges each of which consists of sensors and robots.

In the second tier of our solution, we use lightweight BLOCKCHAIN protocols whereby the cluster head robots collect data from other robots in a secure, transparent, and immutable way. Here, we can use AI to achieve an efficient lightweight blockchain protocol. Thus, the robotic system becomes MORE RESILIENT to malicious attacks.

In the third tier of our solution, we use a UAV (called DRONE if we consider commercial use) with a limited-capacity battery to collect data from the cluster head robots.

My proposed three-tier solution consists of three subsystems.

I have developed the solutions for each of these subsystems by publishing many papers some of which received best paper awards on the global scale.

In these papers, I have already analyzed mathematically and tested the proposed solutions via simulation and implementation.

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Diversity and Inclusion are central to my goals and all my activities. Equity at its heart is about removing barriers, biases, and obstacles that impede equal access and opportunity to succeed. Diversity is fundamentally about valuing human differences and recognizing diverse talents. Inclusion is the active engagement of Diversity and Equity. I aim to foster an environment in which all individuals are entitled to participate in my initiative free of discrimination. In this way, more and more people can contribute the advance of the solution, which will increase its scalability and applicability  to various regions in the world.  Hence, we display Equity, Diversity, and Inclusion in our team.