# Influence of Crowd Participation Features on Mobile Edge Computing
## 2 Opportunistic Caching
### Content Copies: How Many Copies Should Be Stored? The gain of edge caching is actually larger than that of cellular communication. The performance improvement reaped via retrieving the content from the adjacent edge nodes should be derived theoretically. However, even if we consider the storage abilities of both user terminals and small base stations, a wide gap exists between the limited caching capacity and the continuously increasing data traffic from the wireless links. It is hence important to decide which content and how many content copies must be cached based on a holistic method for the maximization of profitability, i.e., we should prefetch content by integrating its potential popularity, transmission gains and locations of existing copies over the network topology. We can use the traditional knapsack algorithm to model the optimization caching problem and further convert it into a resource-allocation problem with a global  
Figure 3: Three phases in the data forwarding strategy. (a) Source flooding phase; (b) shortest path phase; (c) destination flooding phase. learning rate. Then, the tech-oriented solutions can be found so as to decide what to cache and how many copies to cache by analyzing the global user earning performance. We introduce the system model shown in Figure 2, which includes a single macro cell, \(N_{1}\) SBSs and \(N\) mobile UTs. The macro base station (MBS) can be regarded as a content server with the largest storage capacity, which means that it can access all files from a library. For the sake of simplicity, all files are limited to the same length. SBSs and UTs are modeled as mutually independent Poisson point distributions. The suitable content will be pre-stored to serve the requests through device-to-device (D2D) communication over short distances. The pre-store procedure will be controlled and implemented by the MBS during the off-peak hours. Each SBS is equipped with a cache of size up to \(m_{1}\). Analogously, the UTs are equipped with a buffer size \(m\). To decide the number of copies of each file and the edge nodes that store these files before they are requested, the caching hit ratio \(\Phi(C)\), i.e., the expected probability from the requesting user terminals (RUT), can be regarded as the gain function and optimal goal. From the feasible placement vector, \(C=\{C_{1},C_{2},\cdots C_{M}\}\), which denotes the number of content copies in the communication area of the MBS, and \(p_{i}\), which denotes the average request probability of the \(i\)-th file, the caching hit ratio can be modeled as \(\Phi(C)=\sum\limits_{i=1}^{M}P_{i}(1-e^{-\lambda_{i}\pi r^{2}C_{i}})\). Note that the SBSs and UTs must have different spatial intensities with parameters \(\lambda_{s}\) and \(\lambda_{u}\), i.e., the number of UTs distributed in the same space must be larger than that of SBSs for \(\lambda_{u}>\lambda_{s}\). As the SBSs and UTs are equipped with different storage capacities, \(m_{1}\) and \(m\), respectively, the intensities of the edge nodes caching the \(i\)-th file in the communication radius of the RUT can be further described in terms of the constituent components as \(\lambda=a(\lambda_{s}m_{1}+\lambda_{u}m)\), where \(a\) is defined as an intensity factor of the caching probability. In fact, the mean number of SBSs and UTs in a macro-cell can be determined by their intensities \(\lambda_{s}\) and \(\lambda_{u}\) according to the theory of PPP, i.e., \(N_{1}=\lambda_{s}\pi R^{2}\) and \(N=\lambda_{u}\pi R^{2}\). Aiming at the maximization of the hit ratio in the local network, we transform the optimization problem from a many 0-1 knapsack problem into a complete-knapsack problem with a decomposition style.