In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.
Robotics plays an important role in modern world, and they assist humans in all aspects of life. Application of robotics in industries substantially increases the production and improves quality of manufactured goods [1]. The advancement in artificial neural network field further improves the precision at which robots executes their tasks and its decision making capability [2]. The application of robotics in agricultural sector along with human work force will lead to precision farming that could save enormous electrical power and optimum utilization of water and other resources [3]. Robots are widely used in ploughing, seed sowing, watering of plants, spraying, power management, pesticides and fertilizers spraying, weed removing, etc. In agricultural sector, many farmers go by traditional sowing method and face some difficulties. Due to shortage in agricultural workers and increase in large-scale farming, robotics has been widely used. Some recent work done on agricultural robotics are briefly described here.Nithish Kumar et al. ( 2020) presented a GSM-based automated seed sowing device using ultrasonic sensors for obstacle detection, but no provision is provided for remote adjustment of seed spacing and depth [4]. Sangole et al. (2020) proposed a semi-automatic Arduino-based agricultural bot for seed sowing [5]. Madhu and Patil Raj Kumar (2017) has proposed a seed sowing machine, operated manually. This machine can sow any type of seeds and maintain equal space between the seeding points. The cost of this method is less and useful for small-scale farmers. It can be used for cultivating crops like cauliflower, potato, radish, etc. It also incorporates a seed-metering device to know the amount of seeds in the tank. At a set proportion, the seed will fall on the ground according to the rotation of the wheel. There is a disc, which is used to allow only one seed for one rotation [6].
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