1.
Peesala Ilanna
– ACE Engineering College, Hyderabad, Telangana, India.
2.
Aruna Sree
– Malla Reddy Engineering College for Women, Hyderabad, Telangana, India.
3.
Venkata Sowjanya Peruri
– Malla Reddy Engineering College for Women, Hyderabad, Telangana, India.
4.
Phijik Battula
– Vignan’s Institute of Management and Technology for Women, Hyderabad, Telangana, India.
5.
Hoyala Jataboina
– Malla Reddy Engineering College for Women, Hyderabad, Telangana, India.
Abstract
When another device connects, older central hubs start to struggle - slowness follows. Information moving across long distances adds delay, clogging pathways bit by bit. Big piles of private details sitting in one spot invite risk and worry. By handling data nearer to its source, edge computing cuts down wait times, making everything react faster. When demand jumps suddenly, tiny local gadgets can falter. Still, today’s job-distribution setups mimic how liquids pass through cell walls, sliding tasks easily between phones, routers, edge hubs, and distant cloud sites. Work gets paired smartly with the right machines using clever models, spreading out jobs evenly so nothing runs too hot or sits idle. Rather than passing around private information, gadgets share what they’ve picked up by working together - keeping details safe while still learning from one another. Safety improves thanks to tools like ELET L-IDS that spot odd behavior fast, shutting down new IoT risks before they spread. Multiple models team up through ensemble methods, lifting accuracy without piling on processing demands. When the system grasps what a task truly needs and what each machine can handle, assignments shift into smoother alignment. Now here comes smart tracking that keeps checking how systems run, shifting loads between gadgets and servers without stopping. When one unit gets too busy or slows down, the setup reroutes jobs on its own, thanks to clever timing logic. That adaptability means steady speed holds up, even while hundreds of IoT units plug in every minute. Local smarts handle quick decisions nearby, while the cloud steps in for heavy math when needed. When machines work together, failures matter less. Because one part breaking won’t shut everything down. This kind of setup grows easily when needed. Security gets stronger along the way. Performance improves without extra strain. Handling tomorrow’s connected devices feels less like a stretch.
Keywords Edge-cloud computing, Ensemble transfer learning, Internet of Things (IoT), Intrusion Detection System (IDS), Osmotic computing
