Deep Learning and Social Relationship Based Cooperative Caching Strategy for D2D Communications

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multilayer edge caching in the integrated satellite-terrestrial network to reduce the communication delay, in which the base station cache, the satellite cache, and the gateway cache cooperatively provide content service for ground users. We first propose the three-layer cooperative caching model of the network, based on which we analyze the content retrieving process and derive the cache hit probability for different caching locations. Considering limited cache sizes, we formulate the content placement problem to minimize the average content retrieving delay of users. Then, two caching strategies, the non-cooperative caching strategy and the cooperative caching strategy, are proposed with exhaustive theoretical analysis. By introducing the concept of delay reduction gains, the optimal caching strategies are obtained based on the proposed iterative algorithms. Finally, numerical results are presented to demonstrate the performance of the proposed cooperative caching architecture and the caching strategies.

In this paper we propose a cluster-based cooperative caching scheme for Mobile Ad Hoc Networks (MANETs). We also adopt a prefetching scheme to complement the caching scheme. A cross-layer design approach is employed to further improve the performance of cooperative caching and prefetching schemes. Cross-layer information is shared among network protocol layers. The effect of network size and the Time-To-Live value on network performance was investigated using simulation. Experimental results demonstrate that the proposed scheme improves caching performance in terms of data accessibility, query delay and query distance compared to the caching scheme that does not adopt the cooperative caching strategy.

Cooperative caching in the device-to-device (D2D) networks is an effective solution to the problem of offloading data traffic at peak times. However, one of the main challenges of cooperative caching is the selfish nature of the user, and some users only cache what they are interested in and don't care about other people's needs. In this paper, we investigate a physical-social-based cooperative caching framework, considering the physical-social tie as a non-monetary incentive to motivate the data cache and share between users by using D2D communication. Specifically, we first define a novel physical-social tie, combining the physical factors and social relationship. After that, we formulate two mathematical programming models with or without the consideration of indirect physical-social tie, to find an optimal caching strategy with the target of maximizing the social group utility. Moreover, we adopt Six Degrees of Separation to analyze the indirect physical-social tie between users when these social users with direct physical-social tie cannot meet the date request of the user, and then devise an indirect social group formation algorithm (ISGFA) to obtain the indirect social group of the user. Finally, simulation results shows that the caching scheme obtained from our model is efficient and outperforms other caching schemes that without considering the physical-social tie.

With caching popular contents at the small-cell base stations (SBSs), cooperative edge caching has emerged as an effective approach to offload explosively increasing network traffic from a massive number of users in mobile edge networks (MENs). However, most previous works ignored impact of user mobility on caching strategy, thus having limited practical applications. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Accordingly, in this paper, we propose a mobility-aware and cooperative coded caching (MoCoCoCa) for achieving traffic offloading in MENs, by considering user mobility and hard service deadline constraints. Specifically, we first develop the MoCoCoCa framework to satisfy users' requests locally and conduct the construction of access trajectory set via random walks on a Markov chain, which takes the randomness of contact into account. Then, based on user mobility predictions, we formulate the caching problem into a mixed integer nonlinear programming (MINLP) problem to minimize the load of core network, subject to finiteness of network resources (e.g., storage capacity, transmission rate) and non-uniformity of content popularity constraints. The objective function's submodular property is proved and a cooperative placement algorithm is given, which can achieve (1-1/e) -optimality. Furthermore, to adapt the given content placement configuration to the spatio-temporal dynamics of realtime request flow, the original placement problem is decomposed into some independent subproblems of content placement at individual SBSs. Each subproblem is converted into a submodular optimization problem, and a request-aware distributed replacement algorithm with linear computational complexity is proposed. Trace-based simulations and numerical results demonstrate that our proposed caching strategy can offload up to 69% network traffic than existing caching strategies.

Several protocols have been proposed to improve data accessibility in MANETs. Some of these proposals have adopted the cooperative caching scheme, allowing multiple mobile hosts within a neighborhood to cache and share data items in their local caches. Cross-layer optimization has not been fully exploited to further improve the performance of cooperative caching in these proposals. In this paper, we propose a cluster-based cooperative caching scheme which uses a cross-layer design approach and prefetching to further improve the performance of cooperative caching scheme. The cross-layer information is maintained in a separate data structure and is shared among network protocol layers. The performance evaluation was carried out in the NS-2 simulation environment and the results show that the proposed approach improves system performance.

The growing demand for low delay vehicular content has put tremendous strain on the backbone network. As a promising alternative, cooperative content caching among different cache nodes can reduce content access delay. However, heterogeneous cache nodes have different communication modes and limited caching capacities. In addition, the high mobility of vehicles renders the more complicated caching environment. Therefore, performing efficient cooperative caching becomes a key issue. In this paper, we propose a cross-tier cooperative caching architecture for all contents, which allows the distributed cache nodes to cooperate. Then, we devise the communication link and content caching model to facilitate timely content delivery. Aiming at minimizing transmission delay and cache cost, an optimization problem is formulated. Furthermore, we use a multi-agent deep reinforcement learning (MADRL) approach to model the decision-making process for caching among heterogeneous cache nodes, where each agent interacts with the environment collectively, receives observations yet a common reward, and learns its own optimal policy. Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

Video is one of the main causes of the dramatic increase in data traffic over cellular networks. Caching is an effective mechanism that decreases the download rate from base stations and, as a result, the load on the base station, by storing the most popular files or videos on the caches and providing them to the users. The problem of efficient content placement on the caches is known as an NP-complete problem. In this paper, we study the role of network coding by increasing the amount of available data to the users through the cache nodes. We propose a network coding-based content placement method, and we compare it to the best uncoded content placement and the best triangular network coding strategies. Our method not only increases the amount of available data to the users, but also results in a fair distribution of data.

The combination of underlay spectrum sharing and coordinated multi-point technologies promises substantial spectral efficiency (SE) gains for 5G cellular networks. In this work, we present a family of simple cooperative mobile edge caching strategies that create joint transmission (JT) opportunities and make use of the non-computational-demanding score-gated least recently used (SG-LRU) caching scheme to achieve high cache hit rate, thus increasing the sum-SE and reducing both the backhaul traffic and the content access latency. In addition, we derive a low-complexity coordinated quality-of-service (QoS) aware resource allocation scheme that maximizes the sum-SE of coordinated beamforming (CBF) under given transmission power per base station, inter-system interference power, and per-user QoS constraints as well as simple alternatives for both CBF and JT. We consider a use case where a cellular network coexists with a fixed satellite service earth station in the 3.7-4.2 GHz C-band. Numerical simulations illustrate the performance gains of the proposed coordinated caching and resource allocation strategies and shed light on the impact of various parameters on their efficiency.

Caching the contents of unmanned aerial vehicles (UAVs) could significantly improve the content fetching performance of request users (RUs). In this paper, we study UAV trajectory design, content fetching, power allocation, and content placement problems in multi-UAV-aided networks, where multiple UAVs can transmit contents to the assigned RUs. To minimize the energy consumption of the system, we develop a constrained optimization problem that simultaneously designs UAV trajectory, power allocation, content fetching, and content placement. Since the original minimization problem is a mixed-integer nonlinear programming (MINLP) problem that is difficult to solve, the optimization problem was first transformed into a semi-Markov decision process (SMDP). Next, we developed a new technique to solve the joint optimization problem: option-based hierarchical deep reinforcement learning (OHDRL). We define UAV trajectory planning and power allocation as the low-level action space and content placement and content fetching as the high-level option space. Stochastic optimization can be handled using this strategy, where the agent makes high-level option selections, and the action is carried out at a low level based on the chosen option’s policy. When comparing the proposed approach to the current technique, the numerical results show that it can produce more consistent learning performance and reduced energy consumption.

Content Centric Mobile Ad-hoc NETwork (CCMANET) applies the advantages of Content Centric networking (CCN) into Mobile Ad Hoc Network(MANET) to overcome the drawbacks of low efficiency and unstable in transmission. Caching scheme is one of the key components of CCMANET. However, the caching scheme in CCMANET has not been well explored. In this paper, a novel cooperative caching scheme based on generalized dominating set and local content popularity for CCMANET is proposed. First, a virtual backbone in CCMANET is constructed by generalized dominating set to make the arbitrary topology become two-level hierarchy and a collaborative cache placement scheme between the two hierarchies is designed. Second, we propose a method of computing the local content popularity and a cache replacement scheme based on the local content popularity. Simulation results show that the proposed caching scheme can effectively reduces both path stretch value and server load, and improves the hit ratio compared with the existing caching schemes.

Proactive caching of the most popular contents in the cache memory of fog-access points (F-APs) is regarded as a promising solution for the 5G and beyond cellular communication to address latency-related issues caused by the unprecedented demand of multimedia data traffic. However, it is still challenging to correctly predict the user’s content and store it in the cache memory of the F-APs efficiently as the user preference is dynamic. In this article, to solve this issue to some extent, the deep learning-based content caching (DLCC) method is proposed due to recent advances in deep learning. In DLCC, a 2D CNN-based method is exploited to formulate the caching model. The simulation results in terms of deep learning (DL) accuracy, mean square error (MSE), the cache hit ratio, and the overall system delay is displayed to show that the proposed method outperforms the performance of known DL-based caching strategies, as well as transfer learning-based cooperative caching (LECC) strategy, randomized replacement (RR), and the Zipf’s probability distribution.

Space-Ground Integrated Networks (SGINs) hold the potential to enable seamless and high-quality global coverage in an economically viable manner. However, the limited bandwidth and relatively long delay of satellite-ground links pose significant challenges in meeting the increasing demands driven by surging traffic. To address this issue, we propose a three-tier cooperative caching architecture incorporating base stations (BSs), satellites, and a content server, which aims to reduce the average content retrieval delay through cooperative caching between BSs and satellites. The joint optimization problem among them is both challenging to solve and non-scalable due to its exponentially increasing computational complexity. Meanwhile, the geographic characteristics of requests are overlooked in existing work. As a consequence, we propose a novel block partitioning algorithm based on the popularity similarity across areas to facilitate cooperative caching, which reduces the computational complexity, and ensures the scalability and the effectiveness of cooperative caching. Based on the partitioned blocks, the optimization problem is decomposed into two subproblems: intra-block cooperative optimization among BSs and inter-block cooperative optimization among satellites, effectively catering to the characteristics of wide-area coverage in SGINs. For the subproblems with finite dimensions, Semidefinite Relaxation (SDR) based intra-block and inter-block cooperative caching approaches are proposed to obtain the optimal cooperative caching strategies. Extensive simulations demonstrate that, compared to directly solving the original problem, the proposed algorithm reduces the solving time from exponential to linear growth. Moreover, our algorithm outperforms existing schemes by reducing 13% average retrieval delay and improving 12% overall cache hit rate.

The past decade has experienced a continued increase in the popular use of Web services. Unfortunately, the growth of Web services, coupled with their their role as a vital source of news and information has lead to many well published sources of stress that tend to cripple performance at both the client and the server side [1, 4]. Our work considers one such type of stress, called a flash crowd. A flash crowd arrives as a tidal wave, where the initial (and largest) spike in traffic generally occurs within the first few minutes. This gives client and servers only a few tens of seconds to adapt to the incoming traffic! In addition, flash crowds are infrequent and unpredictable. Hence, any solution deployed to mitigate flash crowd must consist of: (1) proactive and real-time mechanisms to detect the flash crowd while (or even before) it happens, and (2) an initiation of immediate action that can mask the effects of the stress.A variety of system designs for content delivery have emerged over the past few years. Based on their underlying design methodologies, we classify these existing designs into four classes. We use this taxonomy to compare the effectiveness of the existing designs to mitigate flash crowds. The four classes are: (1) Single server solutions that target core server performance, such as SEDA [6]; (2) Server cooperation solutions that distribute content through cooperation amongst multiple servers, such as Coral [2]; (3) Server-Client solutions that require changes to the behavior of both the client and server side, such as Overhaul [5]; (4) Client cooperative caching solutions that utilize peer-to-peer routing substrates, such as Squirrel [3].We follow a four-step approach in our study of flash crowds:• Characteristics of flash crowds: We study the properties of six real flash crowds based on traces collected from the respective websites.• Real-time Flash Crowd Detector: We present the design and implementation of a real-time stochastic detector of flash crowds, running at the server. A detector must continuously monitor visitor traffic and resource utilization, and be able to immediately and accurately flag a flash crowd in real-time. Additionally, the detector must run efficiently, without consuming significant computational and memory resources, especially during normal operations. We find that our design enables us to detect a flash crowd while the event is happening.• Comparison of existing solutions: We use flash crowd traces to compare the effectiveness of three of the four methodologies discussed above. We measure both network utilization of the server (a resource which directly affects the content provider's costs) and the quality of service as perceived by the clients (i.e., the turnaround latency for each request).• Design of an Adaptive Cooperative Caching Solution: We combine our real-time flash crowd detector at the server, with a cooperative caching scheme among clients, to design an adaptive cooperative caching solution for flash crowds. We compare the performance of this scheme with the existing methodologies. Our results reveal that such an approach is extremely effective in turning a flash crowd into a smart mob.

Network coding has been proven as a very promising technique to improve wireless network throughput and energy-efficiency (EE). Meanwhile, cooperative caching, which allows the sharing and coordination of cached data among multiple nodes, can improve the performance of data access. In this paper, we aim at exploiting the potential benefits brought by cooperative caching for the multi-rate wireless networks, which have been applied to network coding scheme. We propose a cooperative caching scheme combining network coding technique, called NC-COCA, which exploits the cache spaces of relatively idle MHs, and performs encoding operation to utilize the restricted cache resource with the potential gain of network coding. Simulation results based on NS-2 demonstrate that NC-COCA can effectively improve the cache hit ratio and decrease the average retrieval delay, and the improvement of data accessibility comes from the sacrifice of messages overhead.