Lab wins best award
Best paper awards for the research comes in IEEE
IIT Patna Research Lab
Pervasive &
Intelligent
Systems
Advancing the frontiers of intelligent systems through innovative research in emerging technologies and real-world applications.
Internet of Things
Network Economics
Mechanism Design
Game-Theoretic Applications
Blockchain
Machine Learning
5+
Publications
in Top Venues
8+
Research Areas
of Excellence
5+
Active Research Scholars
Current PhD Team
0+
Ongoing Research Projects
Funded Work
About the Laboratory
Pervasive & Intelligent Systems Lab (PRISM Lab) is committed to advancing cutting-edge research and innovation in next-generation communication, intelligent systems, and pervasive technologies. With a primary focus on “5G and Beyond,” the lab actively explores emerging domains such as advanced wireless communication, AI-driven networks, pervasive computing, Internet of Things (IoT), edge intelligence, and future digital infrastructures.
PRISM Lab serves as a dynamic research environment where students, researchers, and faculty collaborate to design, develop, and evaluate innovative solutions for real-world challenges in connectivity and intelligent systems. Through experimental research, advanced prototyping, and interdisciplinary collaboration, the lab aims to contribute to the evolution of smarter, faster, and more resilient communication ecosystems for the future.
News & Events
PRISM Lab Expands Research in 5G and Beyond
Strengthening research initiatives in next-generation wireless communication, intelligent systems, and pervasive technologies.
Advanced USRP-Based Wireless Experiments Initiated
PRISM Lab begins real-time experimentation in software-defined radio (SDR) for future communication systems.
Focus on AI-Driven Intelligent Networks
Ongoing research explores AI-powered communication systems for smarter and adaptive network solutions.
Advancing Innovation in Smart Communication Systems
Researchers at PRISM Lab continue exploring future-ready technologies for seamless connectivity.
PRISM Lab Strengthens Hands-On Research Environment
Modern research workstations and experimental setups enhance practical learning and innovation.
Core Research Domains
Explore the specialized theoretical and applied computer science fields we investigate.
Internet of Things (IoT)
The Internet of Things (IoT) represents a transformative paradigm in which interconnected devices, sensors, and intelligent systems communicate seamlessly to collect, exchange, and analyze data in real time. IoT enables smart environments by integrating physical systems with computational intelligence, facilitating automation, remote monitoring, predictive decision-making, and enhanced operational efficiency. Research in IoT focuses on the design and development of smart, scalable, and secure connected ecosystems for applications such as smart healthcare, intelligent transportation, industrial automation, agriculture, smart cities, environmental monitoring, and energy-efficient systems. The research also addresses challenges related to device interoperability, communication protocols, data management, low-power systems, security, privacy, and edge intelligence. Emerging research directions include IoT-enabled healthcare systems, edge computing for IoT, intelligent sensor networks, cyber-physical systems, AI-integrated IoT architectures, and privacy-preserving IoT frameworks for real-world deployments.
Network Economics
Network Economics is an interdisciplinary research area that integrates concepts from economics, optimization theory, communication networks, and computational intelligence to study the efficient allocation, pricing, and management of network resources. It focuses on developing economic models and incentive mechanisms to optimize the performance, sustainability, and fairness of modern communication systems. Research in network economics explores problems related to resource allocation, pricing strategies, spectrum sharing, congestion control, incentive design, auction-based mechanisms, and cost optimization in wireless and distributed networks. This area is particularly important in emerging communication systems such as 5G/6G networks, edge computing, cloud systems, and decentralized communication infrastructures. The field also investigates economic incentives for cooperative networking, market-driven communication systems, network neutrality, and game-theoretic optimization techniques for large-scale intelligent networks.
Mechanism Design
Mechanism Design is a foundational area in economics and computer science that focuses on designing systems, protocols, and decision-making frameworks that encourage participants to behave strategically while ensuring desirable global outcomes. Often referred to as the “reverse engineering of economic systems,” mechanism design seeks to construct rules that align individual incentives with system-wide objectives. Research in this area involves the development of fair, efficient, and incentive-compatible mechanisms for applications including resource allocation, auctions, decentralized systems, blockchain governance, wireless communication, distributed computing, and market design. Mechanism design is widely used to address challenges in trust, cooperation, truthful information sharing, and strategic behavior among rational agents. Current research directions include auction mechanisms, incentive engineering, decentralized coordination, pricing frameworks, and secure protocol design for emerging digital ecosystems.
Game Theory
Game Theory is a mathematical and strategic framework for analyzing interactions among multiple decision-makers whose outcomes depend on the actions of others. It provides powerful tools for modeling competition, cooperation, and strategic decision-making in complex systems. Research in game theory focuses on solving problems related to resource sharing, strategic interactions, conflict resolution, incentive mechanisms, equilibrium analysis, and optimization in domains such as wireless communication, distributed networks, cybersecurity, economics, artificial intelligence, and blockchain ecosystems. The area encompasses concepts such as non-cooperative games, cooperative games, evolutionary game theory, Nash equilibrium, repeated games, bargaining strategies, and auction models, enabling intelligent decision-making in dynamic and uncertain environments.
Blockchain
Blockchain is a decentralized and distributed ledger technology that enables secure, transparent, immutable, and trustless management of digital transactions and information without requiring centralized control. It has emerged as a revolutionary technology for building secure digital ecosystems with enhanced transparency, accountability, and tamper resistance. Research in blockchain focuses on distributed consensus protocols, smart contracts, decentralized applications (DApps), cryptographic security, privacy-preserving mechanisms, scalability, interoperability, and trusted data sharing frameworks. Applications extend across multiple domains, including healthcare, supply chain management, digital identity, finance, governance, cybersecurity, and land registry systems. Emerging directions include blockchain-enabled federated learning, decentralized AI, secure multi-party collaboration, permissioned blockchain frameworks (e.g., Hyperledger Fabric), Web3 ecosystems, and scalable consensus mechanisms for next-generation secure infrastructures.
Machine Learning
Machine Learning (ML) is a branch of artificial intelligence that enables computational systems to learn patterns from data and make intelligent decisions with minimal human intervention. By leveraging statistical methods, optimization techniques, and computational intelligence, machine learning systems can automatically improve performance through experience. Research in machine learning spans areas such as supervised learning, unsupervised learning, reinforcement learning, deep learning, predictive analytics, pattern recognition, and intelligent automation. Applications include healthcare diagnostics, cybersecurity, natural language processing, computer vision, recommendation systems, predictive maintenance, and smart decision-making systems. Current research directions focus on privacy-preserving machine learning, federated learning, explainable AI (XAI), trustworthy machine learning, edge intelligence, and scalable intelligent systems for solving real-world interdisciplinary challenges.
Principal Investigator
Faculty Profile
Dr. Satendra Kumar
Assistant Professor
Department of Computer Science & Engineering
Indian Institute of Technology Patna
Leading interdisciplinary research in intelligent systems, blockchain-enabled frameworks, mechanism design, network economics, game theory, IoT systems, and machine learning with emphasis on scalable real-world impact.
Research Interests
BlockchainMechanism DesignIoTMachine LearningGame TheoryNetwork Economics
Featured Projects
Explore active and completed lab research projects sponsored by prominent organizations.
Development and Prototype Implementation of Energy-Efficient Cooperative Content Delivery for Beyond 5G Networks
completedThis project focuses on the development and prototype implementation of an energy-efficient cooperative multimedia content delivery framework for Beyond 5G networks. The work aims to optimize content delivery performance through intelligent server selection, reduced latency, improved energy efficiency, and enhanced Quality of Service (QoS) for Ultra-HD multimedia streaming.
Our Research Team
Meet our current Ph.D. research scholars actively contributing to cutting-edge research at PRISM Lab.
Supported by & Collaborating with
Lab Gallery & Activities
Snapshots of group discussions, research showcases, workshops, and lab milestones.
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