دانلود کتاب شبکه‌های حسگر بدون باتری برای اتصال پایدار اینترنت اشیا نسل بعدی

Battery- Free Wireless Sensor Networks (BF- WSNs) represent a transformative approach to solving the limitations of traditional wireless sensor networks (WSNs), particularly in terms of energy consumption and network lifetime. Conventional WSNs heavily rely on battery- powered sensor nodes, which often require regular maintenance and replacement, limiting their longevity and operational efficiency. With the rapid expansion of the Internet of Things (IoT) and the increasing demand for connected devices, the sustainability of these networks has become a major challenge. The idea of battery- free networks, where sensor nodes harvest energy from environmental sources such as light, heat, or vibrations, offers a potential solution to these limitations. These self- powered nodes eliminate the need for battery replacements, enabling long- term, maintenance- free operation in remote or difficult- to- access environments. The concept of energy harvesting in BF- WSNs has gained significant attention in recent years, as it provides a viable method for powering sensor nodes without relying on traditional batteries or power supplies. Energy harvesting techniques, such as solar, wind, thermoelectric, and piezoelectric harvesting, allow BF- WSNs to capture ambient energy from their surroundings, making them more sustainable and adaptable to diverse environmental conditions. Additionally, energy storage and management are critical components of BF- WSNs, as they ensure efficient energy use, power availability, and network performance over extended periods. Dynamic energy allocation strategies are essential in these networks to optimize energy consumption while maintaining reliable data transmission and connectivity. However, the integration of BF- WSNs into the IoT ecosystem introduces new challenges. Existing algorithms and protocols developed for conventional WSNs often do not directly apply to battery- free systems due to their unique energy constraints and self- powered operation. Networking protocols, data acquisition methods, and energy management techniques must be specifically designed or adapted to meet the requirements of BF- WSNs. Furthermore, the adoption of advanced techniques such as machine learning for adaptive behavior and cognitive radio for spectrum management are crucial in enabling these networks to function efficiently in real- world applications. As BF- WSNs continue to evolve, they have the potential to revolutionize the way IoT devices are connected, contributing to more sustainable, scalable, and resilient networks. This book aims to provide a comprehensive understanding of the principles, challenges, and emerging solutions that drive the development of BF- WSNs and their pivotal role in the future of IoT connectivity.