Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.

IMU: A Content Replacement Policy for CCN, Based on Immature Content Selection

In-network caching is the essential part of Content-Centric Networking (CCN). The main aim of a CCN caching module is data distribution within the network. Each CCN node can cache content according to its placement policy. Therefore, it is fully equipped to meet the requirements of future networks demands. The placement strategy decides to cache the content at the optimized location and minimize content redundancy within the network. When cache capacity is full, the content eviction policy decides which content should stay in the cache and which content should be evicted. Hence, network performance and cache hit ratio almost equally depend on the content placement and replacement policies. Content eviction policies have diverse requirements due to limited cache capacity, higher request rates, and the rapid change of cache states. Many replacement policies follow the concept of low or high popularity and data freshness for content eviction. However, when content loses its popularity after becoming very popular in a certain period, it remains in the cache space. Moreover, content is evicted from the cache space before it becomes popular. To handle the above-mentioned issue, we introduced the concept of maturity/immaturity of the content. The proposed policy, named Immature Used (IMU), finds the content maturity index by using the content arrival time and its frequency within a specific time frame. Also, it determines the maturity level through a maturity classifier. In the case of a full cache, the least immature content is evicted from the cache space. We performed extensive simulations in the simulator (Icarus) to evaluate the performance (cache hit ratio, path stretch, latency, and link load) of the proposed policy with different well-known cache replacement policies in CCN. The obtained results, with varying popularity and cache sizes, indicate that our proposed policy can achieve up to 14.31% more cache hits, 5.91% reduced latency, 3.82% improved path stretch, and 9.53% decreased link load, compared to the recently proposed technique. Moreover, the proposed policy performed significantly better compared to other baseline approaches.

An Efficient Content Store-Based Forwarding Scheme for Internet of Things

One of the main advantages of information-centric networking (ICN) is that a requested piece of content can be retrieved from a content store (CS) at any intermediate node, instead of its original content producer. In existing ICN designs, nodes forward Interest packets mainly based on forwarding information base (FIB). FIB is constructed from name prefixes registered by content producers with a list of next hops to the name prefixes. The ICN forwarding engine uses those information to forward Interest packets towards corresponding content producers. CS information of a node is currently used only for checking the availability of cached content objects at the node and is not considered in the data plane of existing ICN forwarding mechanisms. This paper highlights the importance of CS information in an ICN forwarding mechanism and enables neighbor CS information in the data plane to improve the cache hit ratio and forwarding efficiency, especially for resource-constraint Internet of Things (IoT). We propose an efficient CS-based forwarding scheme for IoT. The proposed forwarding scheme exploits CS information of neighbors to find efficient routes to forward Interest packets toward nearby nodes with corresponding cached content. For that, we carefully design an efficient way for CS information sharing using counting bloom filter. We implement the proposed scheme and compare with state-of-the-art ICN forwarding schemes in IoT. Experimental results indicate that the proposed forwarding scheme achieves a significant improvement in terms of cache hit ratio, energy efficiency, content retrieval latency, and response rate.

Social-Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Caching in device-to-device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off-peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real-world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q -networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.

A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems

Compared with the traditional hard-disk drives (HDDs), solid-state drives (SSDs) have adopted NAND flash memory and become the current popular storage devices. However, when the free space in NAND flash memory is not enough, the garbage collection will be triggered to recycle the free space. The activities of the garbage collection include a large amount of data written and time-consuming erase operations that can reduce the performance of NAND flash memory. Therefore, DRAM is usually added to NAND flash memory as cache to store frequently used data. The typical cache methods mainly utilize the data characteristics of temporal locality and spatial locality to keep the frequently used data in the cache as much as possible. In addition, we find that there are not only temporal/spatial locality, but also certain associations between the accessed data. Therefore, we suggest that a cache policy should not only consider the temporal/spatial locality but also consider the association relationship between the accessed data to improve the cache hit ratio. In the paper, we will propose a cache policy based on request association analysis for reliable NAND-based storage systems. According to the experimental results, the cache hit ratio of the proposed method can be increased significantly when compared with the typical cache methods.

Cluster-Based Cooperative Cache Deployment and Coded Delivery Strategy in C-V2X Networks

Cellular vehicle-to-everything- (C-V2X-) based communications can support various content-oriented applications and have gained significant progress in recent years. However, the limited backhaul bandwidth and dynamic topology make it difficult to obtain the multimedia service with high-reliability and low-latency communication in C-V2X networks, which may degrade the quality of experience (QoE). In this paper, we propose a novel cluster-based cooperative cache deployment and coded delivery strategy for C-V2X networks to improve the cache hit ratio and response time, reduce the request-response delay, and improve the bandwidth efficiency. To begin with, we design an effective vehicle cluster method. Based on the constructed cluster, we propose a two-level cooperative cache deployment approach to cache the frequently requested files on the edge nodes, LTE evolved NodeB (eNodeB) and cluster head (CH), to maximize the overall cache hit ratio. Furthermore, we propose an effective coded delivery strategy to minimize the network load and the ratio of redundant files. Simulation results demonstrate that our proposed method can effectively reduce the average response delay and network load and improve both the hit ratio and the ratio of redundant files.

Path Segmentation-Based Hybrid Caching in Information-Centric Networks

Information-centric networks (ICNs) have received wide interest from researchers, and in-network caching is an important characteristic of ICN. The management and placement of contents are essential due to cache nodes’ limited cache space and the huge Internet traffic. This paper focuses on coordinating two cache metrics, namely user access latency and network resource utilization, and proposes a hybrid caching scheme called the path segmentation-based hybrid caching scheme (PSBC). We temporarily divide each data transmit path into a user-edge area and non-edge area. The user-edge area adopts a heuristic caching scheme to reduce user access latency. In contrast, the non-edge area implements caching network content migration and optimization to improve network resource utilization. The simulation results show that the proposed method positively affects both the cache hit ratio and access latency.

Performance Analysis of Cache Based on Popularity and Class in Named Data Network

The communication network is growing with some unique characteristics, such as consumers repeatedly request the same content to the server, similarity in local demand trend, and dynamic changes to requests within a specific period. Therefore, a different network paradigm is needed to replace the IP network, namely Named Data Network (NDN). The content store, which acts as a crucial component in the NDN nodes is a limited resource. In addition, a cache mechanism is needed to optimize the router’s content store by exploiting the different content services characters in the network. This paper proposes a new caching algorithm called Cache Based on Popularity and Class (CAPIC) with dynamic mechanism, and the detail explanation about the static method also presented. The goal of Static-CAPIC was to enhance the total cache hit ratio on the network by pre-determining the cache proportion for each content class. However, this technique is not appropriate to control the cache hit ratio for priority class. Therefore, the Dynamic-CAPIC is used to provide flexibility to change the cache proportion based on the frequency of requests in real-time. The formula involves considering the consumers’ request all the time. It gives a higher cache hit ratio for the priority content class. This method outperforms Static-CAPIC, and the LCD+sharing scheme in the total network cache hit ratio parameter and channels it to the priority class.

A Heuristic Video Recommendation Algorithm based on Similarity Computation for Multiple Features Analysis

Objective:: The short video applications have achieved great success in recent years. The number of videos being shot and uploaded to these platforms has increased greatly. In this way, mining and recommending videos for users based on their interests have become a very difficult problem in these video distribution platforms. Under this case, it becomes particularly important to design efficient video recommendation algorithms for these platforms. In order to solve the problem faced by high sparsity and large scale data sets in the field of media big data mining and recommendation, a heuristic video recommendation algorithm for multi-dimensional feature analysis and filtering is proposed. Methods:: Firstly, the video features are extracted from multiple dimensions such as user behavior and video tags. Then, the similarity analysis is carried out, and the video similarity degree is calculated by weighting, so as to obtain the similar video candidate set, and filter the similar video candidate set. After that, the videos with the highest scores are recommended to users by sorting. Finally, the video recommendation algorithm proposed in this paper is implemented by using the C language. Results:: Compared with the benchmark, the proposed video recommendation algorithm has improved the accuracy by 6.1%-136.4%, the recall rate by 19.3%-30.9%, the coverage rate by 55.6%-59.5%, the running time by 42.7%-60.4%, and the cache hit ratio by 10.9%-47.4%. Conclusion:: The proposed algorithm can effectively and greatly improve the accuracy, recall rate, coverage rate, running time and cache hit ratio.

A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.