Collaborative Caching Method For Heterogeneous Nodes In Internet Of Things

Current Internet of Things(Io T)systems,which are predominantly founded on a cloud computing architecture,are equipped to handle a significant amount of data.This data,generated by numerous Io T terminals and mobile users,is typically transferred to remote computing centers where it is stored and analyzed.The principal objective within the Io T context,a realm where diverse services are offered and energy consumption and transmission latency of wireless terminals are minimized,is to enhance user experience.Mobile Edge Computing(MEC),a novel architectural concept that extends cloud computing services to the edge of the mobile base stations’ network,has emerged as a potential solution to augment the service quality of Io T applications.When applied to caching tasks,MEC can remarkably alleviate the communication overhead which occurs during data transmission from macro base stations to Io T devices.This is accomplished by caching data at the network edge in advance.However,as the storage space of edge-side devices is limited,it becomes impossible to simultaneously cache vast amounts of data.Consequently,designing an energy-efficient cache content placement scheme within these constraints poses a significant challenge.Furthermore,the constant updating of data in the backend database results in the cached content becoming outdated,thus failing to meet the needs of mobile users.Ensuring the freshness of content is,therefore,a crucial issue that must be addressed.This paper primarily focuses on the collaborative caching issues within heterogeneous nodes in Io T.The key areas of research include:(1)Given the heterogeneous nature of the Io T structural hierarchy,placing content with minimal energy consumption is a significant challenge.This paper proposes an efficient caching strategy where a single file is no longer exclusively stored in one Road Side Unit(RSU).Instead,it is divided into several segments and placed across different RSUs,with the aim of reducing energy consumption.When mobile users simultaneously request the same content segment,the Macro Base Station(MBS)adopts a multicast transmission approach rather than a single transmission.An optimization problem is established with the minimum total energy consumption as the goal and cache capacity as the constraint.The problem,which has been proven to be NP-hard,is addressed through a content placement matrix optimization algorithm that ensures low energy consumption transmission while maintaining simple calculations and rapid convergence.(2)Considering the diversity and timeliness of information requests,it becomes vital to refresh the cache content in a timely manner.A freshness-aware content cache refresh scheme is introduced,which measures content freshness based on the Age of Information(Ao I).If the Ao I exceeds a certain refresh window,the cached content item will be updated to the latest version when a user request is made.Key metrics are used to quantify the cache value of dynamic content,including the refresh window,popularity,the number of users in the edge cache group,and acquisition and update costs related to the optimal decision.Cost functions are derived to establish an optimization problem aimed at minimizing costs.A mobile edge cache algorithm is designed to effectively reduce costs.Results indicate that the freshness-driven caching strategy significantly enhances edge caching utilization and drastically reduces costs.