{'fraction_non_alphanumeric': 0.028298126743722597, 'fraction_numerical': 0.0015942606616181746, 'mean_word_length': 5.3867684478371505, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Information Caching Strategy for Cyber Social Computing Based Wireless Networks

Cyber social computing has brought great changes and potential intelligent technologies for wireless networks. Among these technologies, information caching strategies are promising approaches to achieving lower delay, higher throughput and energy efficiency (EE) of user equipment (UE) in 5G wireless networks, by deploying intelligent caching and computing at the mobile edge. However, the static information caching strategies ignore the relevance of traffic fluctuation among different base stations (BSs) and the variance of users’ interests. Thus in this paper, an information caching strategy for cyber social computing based wireless network is proposed, taking advantages of two layer social cyberspaces in both traffic correlation between BSs and the social relationship between UEs. In the first layer, a base station social network (BSSN) is constructed based on the social relationship between BSs, which is defined as social-tie factor (STF). In the second layer, the Indian Buffet Model (IBM) is used to describe the social influence of one UE to another. To reduce base station’s traffic load, users with similar social interest can share the contents they cached with each other. Therefore, device-to-device (D2D) communication is taken as the underlay to cellular networks in our proposed information caching strategy. By utilizing the social characteristic of BSSN, the very important BSs (VIBSs) with higher averages STF are selected. Then the normal small cells (NSCs) within the VIBS’s coverage are linked to the VIBSs only and the other unique small cells (USCs) will be routed back into the core network (CN) directly. Limited cache and backhaul capacity in the whole network are only shared by VIBSs and USCs. UEs will communicate with each other via D2D links only if they have i) similar interests, ii) enough encounter duration between users and iii) are adjacent with each other. Otherwise, the UE shall obtain the required contents via cellular networks. With the tool of stochastic geometry process, key performance indicators, e.g., coverage probability, network throughput power consumption, EE, delay and offloaded traffic are studied. Both the theoretical and numerical results show that the proposed information caching strategy for cyber social computing can achieve high coverage probability, throughput, EE and delay by optimizing STF threshold, VIBS coverage and D2D communication radius.