Problem statement
Wearable devices often face challenges in optimizing energy consumption while maintaining optimal functionality. The project aims to leverage artificial intelligence (AI) to develop algorithms that dynamically adjust energy consumption based on user activity and device requirements, thus enhancing the overall efficiency of wearable devices.
Abstract
This project addresses the need for energy-efficient algorithms in wearable devices to prolong battery life and improve user experience. Utilizing AI techniques, the system will learn user behavior patterns and adapt algorithms to optimize energy consumption without compromising the device’s functionality. The project aims to strike a balance between performance and energy efficiency in wearable technology.
Outcome
- Development of AI-driven algorithms for real-time energy optimization on wearable devices.
- Improved battery life and prolonged usability of wearable devices.
- Enhanced user experience by dynamically adapting to user behavior and preferences.
Reference
The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.
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