Next-generation wireless networks have to be robust and self-sustained. Internet of things (IoT) is reshaping the technological adaptation in the daily life of human beings. IoT applications are highly diverse, and they range from critical applications like smart city, health-based industries, to industrial IoT. Machine learning (ML) techniques are integrated into IoT to make the network efficient and autonomous. Deep learning (DL) is one of the types of ML, and it is computationally complex and expensive. One of the challenges is to merge deep learning methods with IoT to overall improve the efficiency of the IoT applications. An amalgamation of these techniques, maintaining a balance between computational cost and efficiency is crucial for next-generation IoT networks. In consideration of the requirements of ML and IoT and seamless integration demands overhauling the whole communication stack from physical layer to application layer. Hence, the applications build on top of modified stack will be significantly benefited, and It also makes it easy to widely deploy the network.

下一代无线网络必须健壮且能够自我维持。物联网（IoT）正在重塑人类日常生活中的技术适应性。物联网应用高度多样化，范围从智慧城市，基于健康的行业等关键应用到工业物联网。机器学习（ML）技术已集成到IoT中，以使网络高效且自治。深度学习（DL）是ML的一种，它计算复杂且昂贵。挑战之一是将深度学习方法与物联网相结合，以全面提高物联网应用程序的效率。这些技术的融合，在计算成本和效率之间保持平衡，对于下一代物联网网络至关重要。考虑到ML和IoT的需求以及无缝集成的需求，需要对从物理层到应用程序层的整个通信堆栈进行彻底检查。因此，在修改后的堆栈之上构建的应用程序将受益匪浅，并且还可以轻松地广泛部署网络。