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In a generic modern multi-antenna relay system, the incremental cooperative networks use the neighbor nodes to assist the source by sending the source message to the destination for achieving spatial diversity. Therefore, the involvement of cyber-physical Systems (CPS) in smart healthcare has proportionately improved in terms of the relaying schemes. These schemes are equally implicated to terrestrial and non-terrestrial networks. For any non-terrestrial-networks (NTN), the computational intelligence in terms of artificial intelligence (AI) plays a viral role, the work have introduced a novel paradigm of NTN applications using Explainable-artificial intelligence (X-AI) approach. Initially, we propose and analyze a new cooperative relaying scheme for the incremental multi-antenna relays system; namely, incremental multi-antenna relays cooperation with a hybrid relaying scheme (IMARC-H). The IMARC-H, either Amplify-and-forward (AF) or Decode-and-forward (DF) uses relaying scheme based on the X-AI approach. Mathematical formulations have been deducted to support an optimal relationship between signal-to-noise ratio (SNR) and neighbour nodes in the physical layer. The simulations are presented to showcase the validation of the work. The analysis of the obtained simulation results shows that the proposed scheme outperforms in minimizing Bit-Error-Rate (BER) and outage probability as compared to the existing CPS-based relaying schemes such as IMARC-AF, IMARC-DF, etc.

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.