Conversational Artificial Intelligence at Industrial Internet of Things

Siddharth, Dhirendra; Saini, DilipKumar Jang Bahadur; Ramchandra, Mummadi; Loganathan, Summathi

doi:10.1002/9781394200801.ch11

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IoT constitutes a network where various machines are interconnected, allowing them to interact and process data collected from numerous devices, typically sensor nodes. The emerging trend, the Internet of Everything (IoE) [ 3 ], expands the capabilities of the IoT, providing the opportunity not just for device and machine connectivity, but also for integrating people and overall processes into the data circulating within the existing network [ 4 ]. Despite the uncertainties of the future, the pivotal concepts remain the IoT and the Machine to Machine (M2M) communication protocols, alongside the growing utilization of data points from various nodes, whether referring to complex system infrastructures or embedded, low-cost, and low-power sensory devices [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

View full text Add to dashboard Cite

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

show abstract

Section: Introductionmentioning

confidence: 99%