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Hierarchical Matching With Peer Effect for Low-Latency and High-Reliable Caching in Social IoT

机译:具有Peer效果的分层匹配可实现社交IoT中的低延迟和高可靠性缓存

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摘要

The Internet of Things (IoT) is expected to bring great benefits to users, operators, and manufactures in different application scenarios. Given that smart objects which can exploit their own social networks and share contents via device-to-device (D2D) communications, the combined social IoT promises to collect information as well as to provide services more efficiently. This application scenario requires lower latency and higher reliability, and then we adopt D2D-based caching to reduce the downloading latency while guaranteeing the reliable delivery. To achieve this joint optimization objective, we conceive the interdependence with the framework of hierarchical bipartite graph. In this way, this combinatorial problem can be decoupled into a content sharing problem and a resource allocation problem. To solve the first one, we propose a content sharing-oriented matching algorithm with projecting social characters onto physical links. To solve the second one, we formulate this resource allocation problem as equivalent to a many-to-one matching game with peer effect. We then design a novel distributed algorithm with rotation-swap, which can converge to a stable state with limited number of iterations. Formulating the convergence procedure as another NP-hard problem, we further design a coloring-based heuristic algorithm to find a near-optimal solution. We conduct extensive simulations to demonstrate that our proposed schemes can achieve a better tradeoff between performance and complexity than other benchmarks.
机译:物联网(IoT)有望在不同的应用场景中为用户,运营商和制造商带来巨大利益。鉴于智能对象可以利用自己的社交网络并通过设备间通信(D2D)共享内容,因此组合后的社交物联网有望收集信息并更有效地提供服务。此应用程序场景要求较低的延迟和较高的可靠性,然后我们采用基于D2D的缓存来减少下载延迟,同时保证可靠的传递。为了实现这一联合优化目标,我们设想了与分层二部图框架的相互依赖性。这样,该组合问题可以分解为内容共享问题和资源分配问题。为了解决第一个问题,我们提出了一种面向内容共享的匹配算法,该算法将社会角色投射到物理链接上。为了解决第二个问题,我们将此资源分配问题公式化为等效于具有对等效应的多对一匹配游戏。然后,我们设计了一种新型的具有旋转交换的分布式算法,该算法可以通过有限的迭代次数收敛到稳定状态。将收敛过程表述为另一个NP难题,我们进一步设计了一种基于着色的启发式算法,以找到接近最佳的解决方案。我们进行了广泛的仿真,以证明我们提出的方案可以在性能和复杂性之间取得比其他基准更好的折衷。

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