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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63912
Volume:
10 |
5 |
May 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
Ashokrao Mane Group of institution, Vathar tarf Vadgoan
Computer Science and Engineering
Ashokrao Mane Group of institution, Vathar tarf Vadgoan
Computer Science and Engineering
Ashokrao Mane Group of institution, Vathar tarf Vadgoan
Computer Science and Engineering
Ashokrao Mane Group of institution, Vathar tarf Vadgoan
Computer Science and Engineering
Ashokrao Mane Group of institution, Vathar tarf Vadgoan
Computer Science and Engineering
Ashokrao Mane Group of institution, Vathar tarf Vadgoan
The local skilled service sector is an essential driver of daily urban operations and informal employment, but both customers and gig workers face obstacles such as unreliable service fulfilment, lack of background verification, and poor digital visibility in real-time. The purpose of this paper is to introduce WorkEase: an intelligent web and mobile-based on-demand platform built using location-based services and algorithmic allocation into a cohesive solution. The proposed solution uses a multi-layer architecture including interactive user client interfaces, an asynchronous API gateway, a distributed caching layer, and a cloud document database. WorkEase incorporates an automated, hyper-local worker allocation system using the Haversine Formula alongside a multi-criteria Weighted Ranking Algorithm that maps professionals based on real-time spatial proximity, average reviews, and historical job acceptance metrics. To ensure seamless operations under high concurrency, the system integrates third-party services like Google Maps API for live route tracking, Firebase Cloud Messaging for instant alerts, and automated state handlers to prevent data inconsistencies over wireless connections. Experimental evaluations indicate that the WorkEase platform successfully minimizes matching response latency to under 500 milliseconds for heavy concurrent request loads, ensures strict data consistency across distributed database boundaries, and provides an efficient digital ecosystem that improves consumer convenience while promoting professional growth and digital inclusion for skilled workers across urban and semi-urban environments.
KEYWORDS
WorkEase is an on-demand service platform designed to make it easy for customers to find trusted and verified skilled workers for daily repair and maintenance tasks. The project successfully connects customers and workers through a simple mobile and web application, offering features like real-time booking, secure payments, location-based matching, and rating systems. It helps customers get fast, reliable services and gives workers more job opportunities, fair payments, and digital visibility. While the platform has some limitations like network dependency and worker availability, it still provides a strong foundation for future improvements. With added features and wider adoption, WorkEase can become an efficient, user-friendly, and highly impactful service platform for both urban and semi-urban areas.
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Publication Date
5/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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