A wireless sensor network is a collection of small sensor nodes that have limited energy and are usually not rechargeable. Because of this, the lifetime of wireless sensor networks has always been a challenging area. One of the basic problems of the network has been the ability of the nodes to effectively schedule the sleep and wake-up time to overcome this problem. The motivation behind node sleep or wake-up time scheduling is to take care of nodes in sleep mode for as long as possible (without losing data packet transfer efficiency) and thus extend their useful life. This research going to propose scheduling of nodes sleeps and wake-up time through reinforcement learning. This research is not based on the nodes' duty cycle strategy (which creates a compromise between data packet delivery and nodes energy saving delay) like other existing researches. It is based on the research of reinforcement learning which gives independence to each node to choose its own activity from the transmission of packets, tuning or sleep node in each time band which works in a decentralized way. The simulation results show the qualified performance of the proposed algorithm under different conditions.
There was an outbreak of pneumonia in the month of December 2019 in Wuhan, China that spread with a rapid rate throughout the country and shook the world by spreading across the globe causing many deaths due. This disease is confirmed by means of molecular method as a novel coronavirus and was named as 2019 novel coronavirus (2019-nCoV) in its initial stage; however, on February 11, 2020, World Health Organization (WHO) renamed this disease COVID-19, which means corona virus disease. COVID-19 has impacted nearly the entire world, affecting more than 100 countries including India. The Coronavirus Study Group consisting of the International Committee on Taxonomy of Viruses renamed this virus, which was provisionally named 2019-nCoV, as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This nomenclature is based on taxonomy, phylogeny, and established practice. As on March 2020, WHO has confirmed 692,575 number of cases of COVID-19 with 33,099 deaths, which are distributed across the globe: Western Pacific region with 103,775 confirmed cases with 3,649 deaths; European region with 392,757 number of confirmed cases with 23,962 deaths; South East Asia region with 4,084 confirmed cases with 158 deaths; Eastern Mediterranean region with 46,392 confirmed cases with 2,813 number of deaths; America region with 142,081 confirmed cases with 2,457 deaths; African region with 3,486 confirmed cases with 60 deaths. This paper focuses on these areas and regions and tries to find establish the relationship between numbers of deaths and number of cases with respect to the temperature. This paper takes the study of specific areas around the world and also the case study of India to study the effect of temperature on the rise of and death due to COVID-19 virus.
All pass filters (APFs) are used in dispersion compensation which is the foremost requirement in an optical fiber link. All pass filters can correct any order of dispersion by the careful design of multistage all pass filters starting from very simple components with the use of N port devices. Multiple channels, as in wavelength division multiplexed (WDM) system, can be compensated with a single device since these filters are periodic in phase response. In this paper we have designed and implemented these filters to compensate dispersion an some results has shown.
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