AI-Powered Internet of Things

Courses included in the program:

Evolution of Smart City Technologies
Smart City Transportation System
Smart City Energy System
Smart City Healthcare System
Smart City Data Privacy, Safety, and Security

Project: Smart Cities: Digital Transformation of Cities

Duration: 01.20122 - 02.20223

Funding agency: IEEE

Project Coordinators (PIs): Dr. Madusudan Singh, WSU, Korea and Dr. Dhananjay Singh, HUFS, South Korea

Description: The project has successfully explored and addressed the challenges and opportunities presented by Industry 4.0 through the integration of Internet of Things (IoT) and Machine Learning (ML) technologies in the modeling of a smart factory. By combining sensor-based data collection with intelligent data-driven decision-making systems, the project developed a comprehensive framework for enhancing industrial automation, predictive maintenance, real-time monitoring, and production optimization.

Throughout the project, advanced digital tools, including microelectronics, AI algorithms, and embedded IoT devices, were applied to model key processes in a smart factory environment. Machine learning techniques were used to analyze large volumes of operational data, enabling improved forecasting, fault detection, and adaptive planning mechanisms.

The project outcomes demonstrated significant improvements in productivity, energy efficiency, and resource utilization. Additionally, new approaches for optimizing workflows, reducing downtime, and supporting data-informed decision-making were proposed and validated. By contributing novel methods and practical solutions, this project has laid the groundwork for future applications of Industry 4.0 technologies in industrial settings, helping pave the way toward more intelligent, adaptive, and sustainable manufacturing systems.

High-level abstraction of the processing flow when requesting a task in the Elastic-RAN model: (1) the user performs a request that is captured by an antenna; (2)this antenna passes this request to the Pool Orchestrator responsible for a particular geographic region; (3) later, this orchestrator dispatches the incoming task to one of its BBU Pools (4) for the appropriate processing; (5) in parallel, Elasticity Manager performs periodic monitoring of different metrics for resource reorganization.

Project: Using Internet of Things and Machine Learning in Modeling a Smart Factory in the Context of Industry 4.0

Duration: 04.2019 - 04.2022

Funding agency: CAPES , Brazil

Project Coordinators (PIs): Dr. Rodrigo Righi, UNISINOS, Brazil and Dr. Dhananjay Singh, HUFS, South Korea

Description: The project has successfully explored and addressed the challenges and opportunities presented by Industry 4.0 through the integration of Internet of Things (IoT) and Machine Learning (ML) technologies in the modeling of a smart factory. By combining sensor-based data collection with intelligent data-driven decision-making systems, the project developed a comprehensive framework for enhancing industrial automation, predictive maintenance, real-time monitoring, and production optimization.

Throughout the project, advanced digital tools, including microelectronics, AI algorithms, and embedded IoT devices, were applied to model key processes in a smart factory environment. Machine learning techniques were used to analyze large volumes of operational data, enabling improved forecasting, fault detection, and adaptive planning mechanisms.

The project outcomes demonstrated significant improvements in productivity, energy efficiency, and resource utilization. Additionally, new approaches for optimizing workflows, reducing downtime, and supporting data-informed decision-making were proposed and validated. By contributing novel methods and practical solutions, this project has laid the groundwork for future applications of Industry 4.0 technologies in industrial settings, helping pave the way toward more intelligent, adaptive, and sustainable manufacturing systems.

Project: Semantic edge computing and IoT architecture

Duration: 03.2012 - 02.2020

Funding agency: EU Horizon 2020.

Project Coordinators (PIs): Dr. Dhananjay Singh, HUFS, South Korea

Description: In this project, we have designed a Semantic Fusion Model (SFM) that involves the use of edge devices in Internet of Things (IoT) networks to perform local data processing and analysis, rather than relying on centralized servers or cloud systems. The architecture integrates SFM to process and integrate information from sensors in IoT networks. The smart embedded system in this architecture uses semantic logic and value-based information to enhance its intelligence. Additionally, this project discusses the various applications, services, and visual aspects of IoT using technologies such as Radio Frequency Identification (RFID), 6lowpan, and sensor networks, as well as the challenges that need to be addressed in the implementation of IoT.

Publications:

Misbehavior of nodes in IoT based vehicular delay tolerant networks VDTNs, Multimedia Tools and Applications, Aug. 24, 2022, https://doi.org/10.1007/s11042-022-13624-2 [SCIE: 2.6]
Internet of Things for Smart Community Solutions", Sensors 2022, 22 (2), 640.

https://doi.org/10.3390/s22020640 [SCIE: 3.57]

SOS: Socially Omitting Selfishness in IoT for Smart and Connected Communities, Int. J. Communication Systems, vol., July 2020, pp. DOI: 10.1002/dac.4455 [SCIE-2.01]
Game-Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks, Sensors, Switzerland, September 2019,19 (18), 3835. https://doi.org/10.3390/s19183835 [SCIE-3.57]
Cyber-Physical Surveillance System for the Internet of Vehicles", IEEE World Forum on Internet of Things WF-IoT 2018, 5-8 February 2018 — Singapore, pp. 551-556.
Semantic Edge Computing and IoT Architecture for Military Health Services in Battlefield, 14th IEEE Annual Consumer Communications& Networking Conference (CCNC), Las Vegas, January 9-12, 2017, pp. 185-190.
SiTASENSE: Hierarchical 6LoWPAN and Sensing Model for Women Safety Services, The 17th International Conference on Internet Computing and Internet of Things (ICOMP'16), Las Vegas, Nevada, USA, July 25-28, 2016.
Secure Layers Based Architecture for Internet of Things Services ", IEEE World Forum on Internet of Things (WF-IoT), Milan, Italy, Dec. 14-16, 2015.
Developing a NovaGenesis Architecture Model for Service Oriented Future Internet and IoT: An Advanced Transportation System Scenario, IEEE World Forum on Internet of Things 2014, Seoul Olympic Parktel Hotel in Seoul, Korea, 6-8 March 2014.
A Survey of Internet-of-Things: Future Vision, Architecture, Challenges and Services, IEEE World Forum on Internet of Things 2014, Seoul Olympic Parktel Hotel in Seoul, Korea, 6-8 March 2014. [600+ citations]
Internet of Things: Perspectives, Challenges and Opportunities", International Workshop on Telecommunications (IWT 2013), INATEL, Santa Rita do Sapucai, May 6-9, 2013, pp. 1-6.

Project: IP-Based Wireless Sensor Networks: 6LoWPAN Tealtime Testbed

Duration: 03.2007 - 02.2017

Funding Support: Brain Korea-21, DSU, NIMS, HUFS, MtoV Inc.

Project Coordinators (PIs): Dr. Dhananjay Singh, HUFS, South Korea

Description: In this project, we have developed a framework for IPv6-Low Power Wireless Personal Area Networks (6LoWPAN) that utilizes short adaptation identifiers (AIDs) in place of full IPv6 addresses to enable effective IPv6 header compression in communication between IEEE 802.15.4 nodes and the IPv6 domain. To facilitate this, we have implemented a mechanism for translating AIDs to IPv6 addresses and maintaining an AID-IPv6 translation table at the gateway and In-node. When a packet is transmitted, it carries an AID value in the adaptation header instead of the OUT-node's IPv6 address, which is then translated back to IPv6 at the gateway using the AID-IPv6 translation table. In addition, we have designed an effective frame format for the adaptation layer to support both global and local communication in this context. As a reminder, the primary goal of 6LoWPAN is to provide internet connectivity to low-power networks using the IEEE 802.15.4 standard. This allows IN-nodes within the network to communicate with OUT-nodes in the IPv6 domain.

Publications:

Remark of Elliptic Curves Derived from Ant Colony Routing", American Institute of Physics, Greece, Vol.1389, pp. 317-320, 2011. DOI: 10.1063/1.3637756.
Needle in a cross-layer sensor stack", ICACT2011, Phoenix Park, Korea Feb. 13-16, 2011, pp. 61-66, ISBN 978-89-5519-154-7.
Performance Analysis of Gateway Discovery Techniques: IPv6 based Wireless Sensor Networks", INTERNET2010, Valencia, Spain, September 20~25,2010, pp.142-146.
Global Patient Monitoring system using IP-enable Ubiquitous Sensor Network, 2009 World Congress on Computer Science and Information Engineering (CSIE 2009) March 2009, Los Angeles/Anaheim, USA, pp. 524-528.
IP-based Ubiquitous Sensor Network for In-Home Healthcare Monitoring” IEEE-International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT-2009), Aligarh, India, March 2009 pp. 201-204.

Project: Ubiquitous IT: Wireless Sensor Networks with Simulation and Testbed

Duration: 03.2008 - 02.2018

Industry: Brain Korea-21, DSU, NIMS, HUFS, MtoV Inc.

Project Coordinators (PI): Dr. Dhananjay Singh, HUFS, South Korea

Description: In this project, we have developed a wireless ad-hoc sensor network that enables portable devices to establish communication without the need for a central infrastructure. However, the lack of a central infrastructure and the ability of devices to move randomly introduces challenges such as routing and security. To address these issues, we have examined several ad-hoc routing protocols, including AODV, DSR, DSDV, OLSR, and ZRP, which propose solutions for routing within a fixed ad-hoc sensor network. However, we are also interested in communication between a wireless device in the ad-hoc network and a fixed device in a fixed network, such as the Internet. Therefore, we have modified the ad-hoc routing protocol AODV to support this type of interconnection between the wireless ad-hoc sensor network and the fixed network.

Publications:

Selfishness in Vehicular Delay-Tolerant Networks: A Review, Sensors, 3000 vol. 20 (10), 25 May 2020. [SCIE-3.57]
Game-Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks", Sensors, Switzerland, September 2019,19 (18), 3835.

https://doi.org/10.3390/s19183835 [SCIE-3.57]

Energy‐balance node‐selection algorithm for heterogeneous wireless sensor networks, ETRI Journal Wiley, August 2018, https://doi.org/10.4218/etrij.2017-0349 [SCI-1.32]
Game-Theoretic Reward-Based Adaptive Data Communication in Wireless Sensor Networks, IEEE Access, Vol. 6 (1), pp. 28073-28084, June 2018, https://doi.org/10.3390/sym10080299 [SCIE-3.74]
Quality of Service of Routing Protocols in Wireless Sensor Networks: A Review, IEEE Access, Vol 5 (1), pp.1846-1871, 2017. [SCIE - 3.757]
Multi-Channel Distributed Coordinated Function over Single Radio in Wireless Sensor Networks, Sensors, 2011,11(1), pp. 964-991, https://doi.org/10.3390/s110100964 [SCIE-3.57]
Multi-Channel Multi-Radio Using 802.11 Based Media Access for Sink Nodes in Wireless Sensor Network, Sensors, 2011, 11(5), DOI:10.3390/s110504917, Pages 4917 - 4942, May 2011, https://doi.org/10.3390/s110504917 [SCIE-3.57]
MMSP: Design a Novel Micro-Mobility Sensor Protocol for Ubiquitous Communication, UBICOMM2010, Florence, Italy, October 25~30, 2010, pp.208~212.
Distributed Compressed Sensing of Sensor Data, in a Book: Sensor Fusion and its Applications, (edited by Dr. Ciza Thomas), SCIYO, Austria. ISBN: 978-953-307-101-5. In Tech open access publisher, August 2010, PP. 53-66.
Effects of channel SNR in Mobile Cognitive Radios and Coexisting Deployment of Cognitive Wireless Sensor Networks, 29th IEEE International Performance Computing and Communications Conference, Albuquerque, New Mexico, USA, December 9-11, 2010, pp. 294-301.
Connectivity of Ubiquitous Sensor Network with Fixed Network” to International Conference on Control, Automation and Systems 2007 (ICCAS 2007) Seoul, Korea, Oct. 2007, pp. 2378-2382.