Distributed Resources Management in Ubiquitous Smart Cities
Rutuja ChirwatkarAssistant Professor Information Technology Yeshwantrao Chavan College of Engineering, Nagpur, India. rutujachirwatkar@gmail.com0000-0003-4120-0079
Beemkumar NagappanProfessor, Department of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Karnataka, India. n.beemkumar@jainuniversity.ac.in0000-0003-3868-0382
Lakshman SinghSchool of Engineering & Computing, Dev Bhoomi Uttarakhand University, Dehradun, India. lakshman@dbuu.ac.in0009-0005-7018-3855
Anoop DevCentre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India. anoop.dev.orp@chitkara.edu.in0009-0001-1301-6891
Rakhi ChakrabortyAssitant Professor, Department of Computer Science & IT, ARKA JAIN University, Jamshedpur, Jharkhand, India. rakhi.c@arkajainuniversity.ac.in0000-0003-4012-3497
Dr. Sarita MohapatraAssistant Professor, Department of Computer Applications, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India. saritamahapatra@soa.ac.in0009-0004-6374-8005
Another recent paradigm for enabling ubiquitous smart cities to be efficient, resilient, and innovative is Distributed Resource Management (DRM), in which heterogeneous devices, infrastructures, and services operate autonomously and continuously. This paper discusses more advanced concepts of edge-cloud synergy, decentralized coordination, cyber-physical integration, and context-aware optimization to address the increasing burden on urban energy, transportation, communication, and environmental systems. These are the main objectives: (1) to create scalable DRM frameworks with the features of real-time decision, (2) to enhance the interoperability of distributed heterogeneous resources, and (3) to enhance sustainability and service quality as a result of flexible allocation schemes. The proposed solutions will be the multi-agent systems, distributed ledger technologies (DLT), machine-learning-based prediction systems, and dynamic resource-orchestration algorithms. A hybrid simulation-prototype was applied to test the performance based on the metrics of latency, reliability, load balancing, and energy efficiency. Results suggest that significant improvements (up to a 35 percent reduction in resource contention, a 28 percent reduction in response time, and a 20 percent increase in system robustness under high-density urban workloads) have been achieved. A qualitative measure also fosters greater transparency and trust in cross-domain operations. In totality, the paper identifies that the disruptive potential of decentralized management systems can make smart-city ecologies adaptive, secure, and sustainable.