Millimeter-wave (MMW) radar is becoming an essential sensing technology in smart environments due to its light and weatherindependent sensing capability. Such capabilities have been widely explored and integrated with intelligent vehicle systems, often deployed in industry-grade MMW radars. However, industry-grade MMW radars are often expensive and difficult to attain for deployable community-purpose smart environment applications. On the other hand, commercially available MMW radars pose hidden underpinning challenges that are yet to be well investigated for tasks such as recognizing objects, and activities, real-time person tracking, object localization, etc. Such tasks are frequently accompanied by image and video data, which are relatively easy for an individual to obtain, interpret, and annotate. However, image and video data are light and weather-dependent, vulnerable to the occlusion effect, and inherently raise privacy concerns for individuals. It is crucial to investigate the performance of an alternative sensing mechanism where commercially available MMW radars can be a viable alternative to eradicate the dependencies and preserve privacy issues. Before championing MMW radar, several questions need to be answered regarding MMW radar's practical feasibility and performance under different operating environments. To answer the concerns, we have collected a dataset using commercially available MMW radar, Automotive mmWave Radar (AWR2944) from Texas Instruments, and reported the optimum experimental settings for object recognition performance using several deep learning algorithms in this study. Moreover, our robust data collection procedure allows us to systematically study and identify potential challenges in the object recognition task under a cross-ambience scenario.

Search and rescue, wildfire monitoring, and flood/hurricane impact assessment are mission-critical services for recent IoT networks. Communication synchronization, dependability, and minimal communication jitter are major simulation and system issues for the time-based physics-based ROS simulator, event-based network-based wireless simulator, and complex dynamics of mobile and heterogeneous IoT devices deployed in actual environments. Simulating a heterogeneous multi-robot system before deployment is difficult due to synchronizing physics (robotics) and network simulators. Due to its master-based architecture, most TCP/IP-based synchronization middlewares use ROS1. A real-time ROS2 architecture with masterless packet discovery synchronizes robotics and wireless network simulations. A velocity-aware Transmission Control Protocol (TCP) technique for ground and aerial robots using Data Distribution Service (DDS) publish-subscribe transport minimizes packet loss, synchronization, transmission, and communication jitters. Gazebo and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP protocols tested our ROS2-based synchronization middleware for packet loss probability and average latency. A thorough ablation research replaced NS-3 with EMANE, a real-time wireless network simulator, and masterless ROS2 with master-based ROS1. Finally, we tested network synchronization and jitter using one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our middleware shows that a large-scale IoT infrastructure with a diverse set of stationary and robotic devices can achieve low-latency communications (12% and 11% reduction in simulation and real) while meeting mission-critical application reliability (10% and 15% packet loss reduction) and high-fidelity requirements.