With the capability to replicate or surpass human-like cognitive functions, Artificial Intelligence’s (AI) impact can be felt across multiple industries, helping to drive innovations in healthcare with predictive diagnostics, enhance consumer experiences through personalized marketing, and even address complex transportation challenges with autonomous vehicles.

Another evolving area, the Internet of Behaviors (IoB), will be greatly impacted by AI developments. The integration of AI with IoB amplifies the potential to understand and predict human behaviors, ushering in enhanced personalization, rich insights, and transformative impacts on service delivery and societal challenges. Potential examples are utilizing a buyer’s history and location to offer customized promotions in real-time, monitoring health metrics through a wearable device and alerting the wearer to potential conditions with strong accuracy, or reducing car insurance premiums for drivers on safe acceleration and braking patterns.

This white paper examines IoB, including its benefits and risks, and explores AI's critical role in IoB, highlighting how a fusion of the two can drive positive outcomes.


Internet of Behaviors captures, transmits, and analyzes behavioral data from various sources, using these insights to understand and influence human behaviors. Simply, it is a data-driven attempt to understand how, when, and why humans use technology to make decisions. IoB encompasses the comprehensive aggregation of behavioral data, the integration of physical and digital data streams, and the ethical application of this information, ensuring privacy, transparency, and individual agency.


IoB has numerous components and stakeholders. Here, we'll explore both.


Data sources: IoB originates from numerous sources, including:

  • Personal devices like smartphones and smartwatches
  • Online platforms that include information about social media activities and online shopping behaviors
  • Smart home devices
  • Public records that include information about medical history or finances
  • Environmental sensors, including things like traffic cameras

Analytics and AI technologies: After the data is generated or captured, it is stored and formatted for analysis. Analytic and AI tools, which can include machine learning, take processed data and extract meaningful insights or patterns from it, including looking at what happened in the past (descriptive analytics), predicting future behaviors (predictive analysis), and recommending solutions based on the analyzed data (prescriptive analysis). For further learning, VT-MIT’s Business Data Analytics certificate program introduces students to the foundational knowledge and techniques for behavior analysis and recognition in IoB systems.

Decision-making and intervention: After analyzing the data, stakeholders—including businesses, government agencies, researchers, and individuals—can determine the best actions to influence specific human behaviors. This can include things like recommendation systems that suggest additional content or products the consumer would be interested in or behavioral nudges that remind users to take a particular action—e.g. a fitness wearable that buzzes to let the user know they've been sitting too long.


The Internet of Behaviors brings together data, technology, and human behavior in a meaningful way. And when it's implemented well, IoB can create numerous benefits, including:

Personalization: IoB allows experiences to be tailored to fit individual preferences and behaviors. For example, online retailers can suggest products based on a user's browsing history or past purchases, and educational platforms can curate personalized learning pathways based on a student's strengths and weaknesses.

Improved Decision-Making: Businesses, governments, and individuals can make better-informed decisions by gathering and analyzing behavioral data. Companies can adjust marketing strategies based on customers' real-time feedback, and individuals can make more informed life choices.

Public Safety and Efficiency: Governments and local agencies can leverage IoB to ensure safer public spaces and more efficient public services. For example, a city government can analyze traffic cameras and sensor data to optimize traffic flow, reducing congestion and accidents.

Health and Wellness Monitoring: IoB can be pivotal in monitoring an individual's health, providing timely feedback, and predicting potential health issues. Devices like smartwatches can track heart rate, sleep patterns, and physical activity, offering insights and suggestions for improving health. And telemedicine allows healthcare providers to remotely monitor patients, receiving data in real-time, allowing for more timely interventions.

While the benefits of IoB are promising, it's essential to balance them with considerations around data privacy, ethical implications, and the potential for misuse. VT-MIT’s graduate certificates in Cybersecurity Technologies, Management, or Policy teach students threat data collection, assessment, policy development, administration, and coordination of all activities and personnel to protect computers, networks, and organizational information from external intrusion. Some risk concerns to consider are:

Data Privacy Concerns: Because IoB revolves around collecting and analyzing behavioral data, concerns about who has access to it, how it is used, and whether individuals can control their information become paramount. Without proper safeguards, unscrupulous players could misuse personal or sensitive data for invasive advertising or unauthorized surveillance.

Security Risks: The extensive array of devices and platforms collecting data presents numerous points of vulnerability. Cyberattacks and data breaches can expose personal information, leading to potential identity theft, fraud, or unauthorized access to sensitive data, which can have serious personal and financial consequences for individuals.

Ethical Considerations: How data is used to influence behavior raises ethical questions. Manipulating user behavior — especially without transparent disclosure — can be seen as a breach of individual autonomy. If businesses or governments even give the appearance of using behavioral insights in manipulative or coercive ways, consumers may lose trust.

Regulatory Compliance: Different countries and regions have varying data collection, storage, and use regulations. And noncompliance can result in legal repercussions, hefty fines, and reputational damage.

Bias and Fairness: If the data used to train algorithms in the IoB ecosystem is biased, the insights and recommendations generated can perpetuate or even exacerbate those biases. Decisions based on biased data can lead to unfair or discriminatory outcomes — for example, if a health monitoring system is trained on data from one demographic, its recommendations may be less accurate for other groups.


AI technologies serve as the foundational pillars of IoB, enabling nuanced data interpretation and predictive insights. Machine learning provides the groundwork, allowing systems to learn and adapt from vast behavioral datasets. Deep learning dives deeper with neural networks that simulate human brain processes to derive intricate patterns. Further enriching IoB's capabilities, natural language processing interprets textual data while computer vision deciphers visual inputs.

Students are exposed to all three components in MIT’s course, ECE 5494 Innovation Pathways in Artificial Intelligence and Machine Learning. Dr. Kendall Giles, Collegiate Assistant Professor and MIT Program Coordinator and Area Chair for the Department of Electrical and Computer Engineering, states ECE 5494 “helps technical leaders not only understand artificial intelligence algorithms with hands-on experience developing neural network solutions but also do a due diligence assessment for sociotechnical risks such as ethics, bias, fairness, and privacy in AI systems—knowledge and skills directly relevant for developing successful IoB applications.”


Machine learning and deep learning algorithms are typically used for analyzing and interpreting human behaviors, especially within the context of IoB. A few examples of algorithm types are listed below. For a deeper dive into machine learning and big data, students should consider VT-MIT’s Big Data graduate certificate to enhance their technical skills and knowledge base.

Behavior identification recognizes and categorizes specific behaviors from a given data set — for example, using sensor data to determine whether a person is walking, running, or sitting. Machine learning uses classification algorithms to categorize behaviors based on input data, while deep learning convolutional neural networks can be trained to identify specific behaviors by processing and classifying images or video frames.

Anomaly Detection utilizes machine learning algorithms that detect anomalies in the dataset by understanding "normal" behavior and flagging deviations. Identification of unusual patterns that do not conform to expected behavior is crucial for applications like fraud or health alerts.

Behavior prediction forecasts future behaviors based on historical data — for example, by predicting a user's next action on a website. Machine learning regression algorithms can be employed to predict continuous outcomes, and deep learning long short-term memory (LSTM) networks are especially adept at learning sequences, making them excellent for predicting time-series data or behavior sequences.


The Internet of Behaviors merges physical and digital dimensions, with AI playing a pivotal role in gathering and interpreting data to deduce behavioral patterns. AI facilitates this complex process in numerous ways:

Integrating Multiple Data Streams: With the proliferation of IoT devices, wearables, and the omnipresence of social media platforms, AI algorithms play a crucial role in aggregating and synchronizing data streams from disparate sources (e.g., IoT devices, social media platforms, etc.). This integration draws insights from different facets of an individual's life, ensuring a holistic view of user behaviors.

Text and Visual Data Interpretation: Algorithms specialized in natural language processing (NLP) can parse and interpret text-based data from sources like social media, emails, or web searches — which allows stakeholders to extract sentiments, topics, or even behavioral cues. Additionally, computer vision techniques empower systems to decipher visual content, detect activities, identify objects, and even gauge emotions from facial expressions.

Data Preprocessing: AI algorithms require coherent, relevant data, meaning data must go through preprocessing steps, including data cleaning, filtering, normalization, and feature engineering. Proper preprocessing ensures that the subsequent analysis is based on high-quality inputs, improving the reliability and accuracy of insights.

Feature Extraction: Raw data, especially from diverse sources, can be overwhelming. AI algorithms' feature extraction techniques distill this data to extract meaningful attributes or components — retaining crucial information and reducing noise.

Real-Time Processing: AI technologies enable real-time or near-real-time processing of behavioral data — something that's invaluable in situations like emergency response or live customer interactions. Instantaneous data processing and analysis facilitates quick actions or interventions, helping capitalize on opportunities and mitigate risk.


Harnessing both AI and IoB promises transformative applications across industries. By melding data-driven insights with behavior modification, sectors like the medical, financial, and transportation industries are experiencing revolutions, paving the way for holistic advancements and better outcomes.

Within the healthcare realm, certificate programs like VT-MIT’s Health Information Technologies are instrumental, equipping students with the knowledge to parse medical images and derive consequential health insights for refined IoB monitoring.


Remote patient monitoring: When combined with IoB technologies like wearables and smart health devices, AI algorithms can remotely monitor patients' vital signs, detect anomalies, and provide real-time feedback. Patients with chronic diseases can receive continuous monitoring, ensuring timely interventions and reducing hospital readmissions.

Personalized healthcare: AI can analyze vast amounts of health data and behavioral patterns to provide personalized healthcare recommendations, interventions, or medication regimens. Data from sleep trackers, dietary apps, and fitness wearables can be integrated to provide a holistic view of an individual's health, leading to more tailored healthcare solutions.

Disease outbreak: AI algorithms can analyze data from various sources to track the spread of diseases, predict potential outbreaks, and suggest containment strategies. AI can provide early warnings and actionable insights during disease outbreaks by collecting data from hospital records, wearable health devices, and social media.


Crime prevention: AI and IoB analyze patterns of criminal activities, detect anomalies, and identify potential threats. Cities or businesses can deploy IoT devices, like cameras or sensors, and use AI algorithms to identify areas of potential criminal activity or threats proactively.

Emergency response optimization: AI can analyze real-time data during emergencies to optimize the deployment of resources, route emergency vehicles, or inform first responders. Wearables and smart devices can feed real-time data during emergencies, helping AI algorithms strategize effective response mechanisms.

Fraud detection: AI analyzes patterns, user interactions, and transaction data to detect anomalous behavior that might indicate fraudulent activity. By integrating data from banking apps, online shopping platforms, and even physical card swipes, IoB provides a comprehensive transaction dataset for AI to monitor and safeguard against fraud.


Customer segmentation: AI algorithms analyze customer behavior, preferences, and past purchasing histories, then segment customers into distinct groups based on similarities. IoT devices and online platforms can provide continuous streams of user interaction data, allowing businesses to dynamically adjust and refine their customer segments based on real-time behavior.

Product recommendations: AI systems can predict products a consumer is likely to purchase based on their past behavior, preferences, and even the behavior of similar consumers. Data from wearables, IoT devices, and social media can be used to provide more personalized and context-aware product recommendations.

Sentiment analysis: Using natural language processing, AI can analyze text from product reviews, social media, and other platforms to gauge public sentiment about a product, brand, or service. By integrating data from various digital interactions, businesses can get a more holistic view of customer sentiments across different platforms and touchpoints.


Traffic optimization: AI-driven IoB systems can analyze real-time traffic data to optimize traffic flow, reduce congestion, and suggest alternative routes. With data from road sensors, traffic cameras, and even drivers' smartphones, cities can dynamically manage traffic to reduce bottlenecks and improve overall transportation efficiency.

Infrastructure planning: AI can aid urban planners by analyzing behavioral data related to urban activities. Insights from commuter patterns, energy usage, and waste management can guide infrastructure investments and city planning initiatives.

Smart city initiatives: IoB and AI technologies can integrate data from various city systems to improve city-wide monitoring, resource utilization, and overall resident well-being. Data from environmental sensors, public transportation systems, and citizen feedback can be collated and analyzed to implement and refine smart city initiatives.


The fusion of AI and the Internet of Behaviors is poised to reshape our societal and technological landscapes. As emerging trends spotlight deeper personalization, real-time behavioral interventions, and expansive IoT ecosystems, the next frontier beckons researchers to address challenges in data ethics, privacy, and the balance of human-machine interactions.

Marrying behavioral data with AI's evolving prowess offers many transformative possibilities — from creating truly smart cities that respond to their inhabitants' needs to revolutionizing healthcare with predictive, personalized interventions. As we progress, this convergence promises advancement and a profound reimagining of our interconnected world.

Are you ready to take your career to the next level and prepare for the future of AI and IoB? Learn more about Virginia Tech's online Master of Information Technology degree program today.


The #3 nationally ranked Virginia Tech's Master of Information Technology degree program and 10 distinct graduate certificate offerings equip students with cutting-edge skills and knowledge in IT, preparing them for leadership roles in technology-driven industries. With a diverse and dynamic curriculum, distinguished faculty, and a strong industry network, our program offers a pathway to career success in the digital age.