## 6 Internet of Things (IoT)
### Resource Allocation
Demonstrate that the proposed technique improves the system's energy efficiency as compared to an IP-based implementation. The suggested technique also provides a framework for implementing application-level access control and QoS support. In the context of the 5G environment, three studies [163; 164; 165] suggested revolutionary centralised energy balancing uneven concentric chain clustering technique for the IoT system. To lessen the stress on the cluster head, it incorporates a probability-based suboptimal multi-hop path selection algorithm that adjusts the cluster head diameter based on the energy level and distance to the base station. Three publications [166; 167; 168] focused on the resource allocation challenge in FiWi access networks supporting IoT services presented a one-of-a-kind system running at 4.8 GHz frequency for measuring the WCI of post-surgical falls and other human activities. The total system makes use of low-cost wireless hardware such as a network interface card, an RF signal generator, an omnidirectional antenna, and a desktop PC. ML algorithms such as SVM, NB, and DT are used to classify falls and other human behaviours. The accuracy rises at a rate of 4% to 5% per year. Two papers [169; 170] presented a collaborative distributed Q-learning approach for resource-constrained MTC devices to enable them to locate unique RA slots for their transmissions, hence reducing the number of possible collisions. Two papers [171; 172] proposed a resource allocation for Industry 4.0 based on software-defined networking and network function virtualization technologies, machine learning tools, and the slicing paradigm, in which each slice of the network is dedicated to a category of services with similar QoS requirements. Another paper [173] described a unique MTC architecture that provides NFC as a service rather than collecting raw large-volume MTC data. Different modules of the communication infrastructure are orchestrated into a directed network topology for a given application demand, and each module is assigned an appropriately defined atomic function over the input data, allowing the desired global network function to be evaluated over MTC data and a requested MTC-NFC service to be delivered. One paper [174] proposed a network architecture that combines green IoT and 5G networks. To analyse massive data in 5G networks, we presented a human-enabled green IoT system. Green IoT is achieved by grouping together various mobile devices. Two articles [175; 176] in 6G-based edge computing, SSPS was proposed for MCS. In addition to task acceptance rate and weighted schedulability, QoS is taken into account in SSPS to improve the system's service quality. Jobs are assigned to each processor via the SSPS, and some tasks can be moved to other processors as quickly as possible. Three papers [177; 178; 179] proposed a low-cost sensor array design technique based on irregular subarrays, which divides the practical application challenge into two subproblems based on scene scale. To address these issues, other articles [180] offer practical recommendations such as deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication systems. Three publications [181; 182; 183] provide a process-oriented optimization methodology for simultaneously assigning sub-channels, transmit power, and hovering durations that takes into account the entire flight process of UAVs and only needs slowly-varying large-scale channel state information (CSI). Another article [184] describes a two-tier matching method that combines the Gale-Shapley-based matching algorithm for users and HAPs with the random path to a pairwise-stable matching technique for HAPs and satellites. The proposed algorithms' effectiveness is demonstrated by numerical results. A solution based on an integrated DL algorithm was offered in several articles [185; 186; 187; 188].