# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 2 Related Work  
Caching strategy is a hot topic for ICN and CCN (Content Centric Network), and researchers have done lots of work on it. In ICN, Leave Copy Everywhere (LCE) cache policy is used as the default policy. That is, all nodes will cache received packets in the network, and it is the basic network cache policy. Although the algorithm is simple and easy to deploy, the network is redundant [11] and the cache utilization is reduced. Due to the lack of default strategies, some improved strategies have been proposed by relevant researchers. For example, in Left Copy Down (LCD) [12], the content is only cached on the direct downstream of the hit node, avoiding repeated caching of the same content, but this means that a certain frequency of access is needed to push the required content from the server to the network edge. A Random cache placement algorithm [13] was proposed by [PERSON] et al., which is improved on the basis of random selection of cache location, realizing the choice of equal probability. This strategy is simple and easy to implement, but its effectiveness is not considerable. The ProbCache strategy was proposed in [14], and the length of transmission path and the caching capability of nodes are taken into account. Since each node is to be calculated, the overhead is large and the implementation is difficult. [PERSON] et al. proposed a caching strategy based on node inversion [15], where the distribution data is stored on the node with the greatest in-position on the path. However, there will be uneven distribution of caching, resulting in some users being unable to get the requiredcontent in time. [PERSON] et al. proposed WAVE (popularity-based and collaborative) strategy [16], in which content with high thermal content is stored to nodes close to the users. Not only is the hit ratio of caching improved, but the number of cache replacements is also reduced. However, the complexity of the algorithm is high, and the majority of users cannot be taken into account at the same time. A distributed cache random placement strategy [17] was proposed by [PERSON] et al. As a random caching strategy, data is only cached on a single node, so not only is redundancy of the cache resources avoided, but also the cache performance is improved to some extent. However, ignoring the situation in which content is needed by multiple users, the cache efficiency has not been effectively improved. With the above strategies, the cache effectiveness has been effectively improved to a certain extent, but the choice of placement nodes is limited and related only to the strategy itself, and other factors in the network have not been taken into consideration. In order to improve the disadvantages of single-point cache, [PERSON] and [PERSON] proposed cooperative caching [18] to better meet the growing number of users in the network. Through this algorithm, the caching performance of the network is improved to a certain extent and the load of the network is reduced, but its algorithm is complex and its implementation is somewhat difficult. A content placement algorithm based on cooperative caching [19] was proposed by [PERSON] et al. In this method, the content activity is defined to reflect the frequent rate of the content being requested, and the cache performance is improved. However, the increase in the number of users is not taken into account, and placement nodes are only selected from the perspective of the service but not from the perspective of the user. A caching decision based on stay time [20] was proposed by [PERSON] et al. Content with high popularity is effectively selected and cached, but the cost of staying time is increased. A heuristic caching mechanism based on probability was proposed in [21]. Cache performance and cache service efficiency have been improved, taking into account both cache benefit and content heat. But storage and computing overhead is greatly increased.