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Abstract-Distributed self-organization algorithms for wireless sensor (and actuator) networks must have low message complexity from energy and bandwidth considerations. In this paper, we present a novel approach for message-efficient clustering, in which nodes allocate local growth budgets to neighbors. We introduce two algorithms that make use of this approach. Unlike the expanding ring approach [10], our algorithms do not involve the initiator in each round, and do not violate the specified upper bound on the cluster size at any time. We derive analytical performance bounds of our algorithms and also provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the expanding ring approach.

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Many health care organizations around the world are providing treatment for people with numerous medical staff and dedicated equipments for various diseases. Many health care organizations are encountering colossal challenges and difficulties during the COVID-19 pandemic period. Many people around the globe are not even visiting the hospitals even for their periodical check up thinking that there is a huge possibility of getting the spread during the waiting time in the queue. S o, in order to dri ve out the fear from the people and to provide a minimum staying time in the Hospital premises during the visit, research work proposes a Zero Queue Management S ystem (ZQMS ). This system also provides the people to book an appointment with the doctor based on availability of the doctor. It also supports the users to make online payment for their visit and facilitates them to cancel the appointment in case of change of plans. This system will also maintain the patient's previous visits and maintains the medical records for their easy accessibility. User can also make use of waiting list option available in the mobile application to enroll their name in waiting list in order to intimate them regarding any cancellation of appointment with their doctor by any other patients.

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

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