PERFORMANCE ANALYSIS OF PRIORITY BASED MEMORY BALANCING TECHNIQUES IN IOT USING MACHINE LEARNING

Abstract

Mostly the term unanimous would be more apt to coin as Internet of Things to be most promising technology which has grown many inner branches like Internet of Medical Things and so on. Managing or maintaining of storage of the IoT based components gets crucial for work over of pace for handling along with reaction of selected gadgets. The manuscript supports Priority Based Memory or Storage Balancing (PBMB) procedure in successful treatment of storage to work on the pace of reaction of the actuator gadgets. The memory adjusting plan is based upon successive AI calculation that breaks down the occasional way of behaving of the device in dealing with demands. In view of the examination, the accessible memory space is designated and liberated for getting demands and putting away data. The growing experience is convinced through time-subordinate transmission conduct perceptions. The utilized memory leads technique works in a flexible manner for changing time-essential and non-concede merciful applications by restricting storing and access delay at the device level. The proposed methodology is intended to restrict memory misuse, organization delay and to assemble of the solicitation handling rates.