AIoT (Artificial Intelligence of Things) is a relatively new term that has recently become a hot topic that combines two of the hottest acronyms, AI (Artificial Intelligence) and IoT (Internet of Things). IoT consists of interconnected things with built-in sensors and has the potential to generate or collect a vast amount of data. Individual IoT systems can be integrated into a large-scale system for various modern applications. With that comes a lot of collected or real-time data, an intelligent and efficient data processing is essential to make effective use of the information generated from these data. The data can be analyzed and utilized with AI for problem-solving or decision-making. Without AI, IoT would have limited value. AI can multiply the value of IoT; conversely, IoT can promote the learning and intelligence of AI. However, there are many challenges while deploying AIoT in practice. For instance, machine learning is one of the key technologies to be utilized in AIoT systems. Besides, there are many other issues such as complexity, efficiency, scalability, accuracy, and robustness related to the increasing modern AIoT systems and applications.

As we know, the data transmission in the wireless networks is more unreliable than it is in the wired network environment. Although the virtual carrier sensing scheme can be used in the wireless unicast transmission, the multicast and broadcast still not utilize the acknowledgement mechanism for reliable transmission. This is due to the acknowledgement packets of broadcast transmission will cause much higher communication traffic and overhead. Since reliable data broadcast is critical and required in many applications in the wireless sensor networks, our study focuses on the ZigBee network which is a new industrial standard for sensor networks. Some previous related papers improved the broadcast reliability by introducing redundant transmission and increasing coverage ratio of every receiver node, but there still exists probability of packet loss and extra communication cost due to redundant broadcast. This paper proposes an efficient acknowledgement-based approach for reliable data broadcast in wireless sensor networks. Hierarchical acknowledgement mechanism, reduction of rebroadcast packets and ACK packets, degree-based ACK/rebroadcast Jitter, and parent-oriented retransmission are the key schemes to achieve the efficient data broadcast. Simulation results show that the proposed schemes can efficiently reduce the acknowledgement traffic as well as communication overhead and provide the high reliable data broadcast transmission in ZigBee networks..

A directional sensor network is different from conventional wireless sensor networks. It uses directional sensors instead of omnidirectional ones in the network for different applications, and the effective sensing range is characterized by directionality and size-specific sensing angle. Therefore, conditions of directional sensor networks are dissimilar to those of generic wireless sensor networks for researches, especially on the sensing coverage. This study proposed a distributed approach to enhance the overall field coverage by utilizing mobile and direction-rotatable sensors in a directional sensor network. The algorithm makes sensors self-redeploy to the new location and new direction without global information by utilizing the features of geometrical Voronoi cells. Simulations were used to evaluate and prove the effectiveness of the proposed algorithm. The results show that the approach contributes to significant field coverage improvement in directional sensor networks.