-Internet of Medical Things (IOMT) is playing vital role in healthcare industry to increase the accuracy, reliability and productivity of electronic devices. Researchers are contributing towards a digitized healthcare system by interconnecting the available medical resources and healthcare services. As IOT converge various domains but our focus is related to research contribution of IOT in healthcare domain. This paper presents the peoples contribution of IOT in healthcare domain, application and future challenges of IOT in term of medical services in healthcare. We do hope that this work will be useful for researchers and practitioners in the field, helping them to understand the huge potential of IoT in medical domain and identification of major challenges in IOMT. This work will also help the researchers to understand applications of IOT in healthcare domain. This contribution will help the researchers to understand the previous contribution of IOT in healthcare industry.

Flying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose a bio-inspired clustering scheme for FANETs (BICSF), which uses the hybrid mechanism of glowworm swarm optimization (GSO) and krill herd (KH). The proposed scheme uses energy aware cluster formation and cluster head election on the basis of the GSO algorithm. Furthermore, we propose an efficient cluster management algorithm using the behavioral study of KH. We also use genetic operators such as mutation and crossover for the optimal position of the UAV. For route selection, we propose a path detection function based on the weighted residual energy, number of neighbors, and distance between the UAVs for efficient communication. The performance of BICSF is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with grey wolf optimization and ant colony optimization-based clustering algorithms.INDEX TERMS FANET, bio-inspired, self-organization, clustering, energy optimization, routing.

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Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm. Self-organization, clustering, Internet of Drones, routing. INDEX TERMS

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