## 2 Related Work  
To cope with the exponential growth of data traffic, a new paradigm known as ICN is introduced. The proposed model facilitates the end-users by satisfying the request of their neighbour users. It keeps packets of the content at the network's edge, which helps the users acquire their desired data from the edge. In [11], the authors proposed a social based QoS routing scheme for ICN. The authors devised three types of relationships between the nodes namely neighbors (NB), interest friends (IF) and response friends (RF). The author used content popularity based cache scheme to check whether the content should be cached or not in CS. A content with high probability to be requested in future is given higher priority to be placed in limited sized CS and when CS is full, the content that has low probability to be requested in future is replaced by the high probability requested content. In [12], the authors proposed a scheme known as Cache Everything (CEE), to mitigate the average hop latency by placing the content at the neighbor user. It reduces the hop count \begin{table}
\begin{tabular}{c c} \hline \hline
**Keywords** & **Abbreviations** \\ \hline D2D & Device-to-Device \\ NFC & Near Field Communication \\ ICN & Information centric network \\ UGC & User generated content \\ PIT & Pending information Base \\ CS & Content store \\ Node & Communication Device (fixed or mobile) \\ FIB & Future information Base \\ SOCC & Self organized cooperative caching \\ CBR & Constant bit rate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abbreviations of key words. for content retrieval but also faces the problem of cache redundancy. The cache redundancy problem arises along the path of sender and provider, where each node cache the content in their respective memory buffer. Thus, all the nodes in the network cache contain the redundant data in their memory buffers. The mobile devices has limited storage capacity, thus it apply the eviction scheme whenever the memory of these devices are full. In [13], the authors proposed a deep learning based mobile network architecture that identify QoS by directly mapping the state of mobile networks. The cache redundancy problem arises along the path of sender and provider, where each node cache the content in their respective memory buffer. To overcome the problem of data redundancy, the authors in [14] devised a probabilistic caching scheme. In this scheme, the data redundancy problem is reduced by calculating the probability of each data content that arrived at the ICN node between the requester and provider to determine whether the data needs to be cached. In [15], the authors investigated the performance of different caching schemes with various content request models. The authors in [16], used the approach known as WAVE to overcome the cache redundancy problem. In this approach, the content is divided into small chunks. These small chunks contain the data packets. The requester sends the request for every chunk to acquire a data packet. This approach reduces the data redundancy problem and packet loss as we all know that most traffic overhead occurs due to popular or most liked content. The user interest data can be placed at its network edge or nearer to the end users to reduce traffic overhead. In [17], authors proposed an approach to calculate the user's interest content from the network details and placed the desired contents to their network edge by using a social distance parameter. On this parameter the arrived content is cached. The authors in [18], proposed a new energy-aware scheme known to rely on the improvement the LEACH protocol to overcome energy consumption. This approach optimally determine the cluster heads (CH) to minimize the average energy consumption and extend the life span of a wireless sensor network.