Development of an Optimized Energy Consumption System for Iot Networks Using Constraint Satisfaction Programming

The rapid expansion of the Internet of Things (IoT) ecosystem has resulted in unparalleled connectivity in smart homes, smart health care systems, industrial automation, agriculture, and smart cities. Although it has been transformative, energy inefficiency has been one of the most essential obstacles in the deployment of the IoT. The majority of IoT devices are powered by small battery capacities or only intermittent energy collection capabilities and must simultaneously satisfy demanding quality of service (QoS) performance metrics (latency, reliability, and throughput). Existing energy-aware routing, duty cycling, cooperative communication, renewable harvesting, and AI-based adaptation address isolated aspects of the problem and lack system-wide coordination (Silva et al., 2023; Ansere et al., 2020; Cui et al., 2018). This paper forms a complete energy optimization model of the IoT network based on CSP. The presented system poses a multi-constraint optimization problem with energy budgets, device availability, latency constraints, QoS, and renewable energy dynamics as the task scheduling and resource allocation. An architecture based on simulations was developed in Python and Google OR-Tools CP-SAT solver to measure the performance in real-world smart home and industrial IoT settings. The findings show that the energy balancing process was improved among heterogeneous nodes, fewer premature energy depletion, high tasks process levels, and packet delivery reliability under limited conditions. The results indicate that CSP is a deterministic optimization scheme that can balance energy efficiency and QoS guarantees in dynamic IoT settings at a small-to-moderate scale. However, scalability to larger deployments remains to be validated. This study contributes a unified, constraint-based optimization framework that supports the design of performance-aware and sustainable IoT system design