Interest-based cooperative caching in multi-hop wireless networks

Abstract—New communication protocols, as WiFi Direct, are now available to enable efficient Device-to-Device (D2D) communications in wireless networks based on portable devices. At the same time, new network paradigms, as Content-Centric-Networking (CCN), allow a communication focused on the content and not its location within the network, enabling a flexible location for the content, which can be cached in the nodes across the network. In such context, we consider a multi-hop wireless network adopting CCN-like cooperative caching, in which each user terminal acts also as a caching node. We propose an interestbased insertion policy for the caching, based on the concept of “social-distance ” borrowed by online recommendation systems, to improve the performance of the overall network of caches; the main idea is to store only the contents which appear to be of interest for the local user. We show that our proposed scheme outperforms other well-known insertion policies, that are oblivious of such social-distance, in terms of cache hit probability and access delays. I

Cooperative Local Caching under Heterogeneous File Preferences

Local caching is an effective scheme for leveraging the memory of the mobile terminal (MT) and short range communications to save the bandwidth usage and reduce the download delay in the cellular communication system. Specifically, the MTs first cache in their local memories in off-peak hours and then exchange the requested files with each other in the vicinity during peak hours. However, prior works largely overlook MTs' heterogeneity in file preferences and their selfish behaviours. In this paper, we practically categorize the MTs into different interest groups according to the MTs' preferences. Each group of MTs aims to increase the probability of successful file discovery from the neighbouring MTs (from the same or different groups). Hence, we define the groups' utilities as the probability of successfully discovering the file in the neighbouring MTs, which should be maximized by deciding the caching strategies of different groups. By modelling MTs' mobilities as homogeneous Poisson point processes (HPPPs), we analytically characterize MTs' utilities in closed-form. We first consider the fully cooperative case where a centralizer helps all groups to make caching decisions. We formulate the problem as a weighted-sum utility maximization problem, through which the maximum utility trade-offs of different groups are characterized. Next, we study two benchmark cases under selfish caching, namely, partial and no cooperation, with and without inter-group file sharing, respectively. The optimal caching distributions for these two cases are derived. Finally, numerical examples are presented to compare the utilities under different cases and show the effectiveness of the fully cooperative local caching compared to the two benchmark cases

Mediator-assisted multi-source routing in information-centric networks

Among the new communication paradigms recently proposed, information-centric networking (ICN) is able to natively support content awareness at the network layer shifting the focus from hosts (as in traditional IP networks) to information objects. In this paper, we exploit the intrinsic content-awareness ICN features to design a novel multi-source routing mechanism. It involves a new network entity, the ICN mediator, responsible for locating and delivering the requested information objects that are chunked and stored at different locations. Our approach imposes very limited signalling overhead, especially for large chunk size (MBytes). Simulations show significant latency reduction compared to traditional routing approaches

Asymptotic Laws for Joint Content Replication and Delivery in Wireless Networks

We investigate on the scalability of multihop wireless communications, a major concern in networking, for the case that users access content replicated across the nodes. In contrast to the standard paradigm of randomly selected communicating pairs, content replication is efficient for certain regimes of file popularity, cache and network size. Our study begins with the detailed joint content replication and delivery problem on a 2D square grid, a hard combinatorial optimization. This is reduced to a simpler problem based on replication density, whose performance is of the same order as the original. Assuming a Zipf popularity law, and letting the size of content and network both go to infinity, we identify the scaling laws and regimes of the required link capacity, ranging from O(\sqrt{N}) down to O(1)