Caching sheds a light on reducing long-distance data transmissions over networks, while raising significant privacy concerns. Moving one step ahead, collaborative edge caching is proposed to facilitate preserving user privacy via reducing the external data exposure. However, it still fails to avert the risk of privacy leakage from nearby edge devices. To tackle this issue, we develop an analytical framework for privacy preserving joint communication and content allocation algorithm for distributed collaborative edge caching systems, in which edge devices collaboratively cache and share the content items based on the dummy-based privacy preservation mechanism. Specifically, we define the system request uncertainty criterion from the perspective of information entropy to measure the privacy preservation performance. Consequently, the closed-form relationship between the system request uncertainty and the resource allocation decisions on both communication resources and content items can be derived. Then, we decompose the NP-hard resource management problem into two parts, and propose 1) an optimal dummy request allocation strategy through investigating special properties of the maximal allocation reward gain and 2) an asymptotically optimal content item allocation strategy with low complexity based on the extract penalty method (EPM), which are iterated to obtain a viable solution, followed by the proof of convergence and asymptotic monotone property. Finally, the performance improvements are verified by simulations.

Content popularity forecast is a key enabler toward the realization of proactive content caching, contributing to significant reduction of content fetching delay. Different from most of the existing literature that concentrating on enhancing the forecast accuracy, we tailor the popularity forecast and user clustering algorithms for improving the caching performance. Specifically, through analyzing the caching performance drop incurred by inaccurate popularity forecast from the Bayesian perspective, we obtain two critical insights, which trigger the following designs: 1) as the utility of forecast varies according to the content rank, we propose a content-caching-oriented popularity forecast algorithm based on Gaussian process (GP), where more computational resource is allocated to forecast the popularity of prioritized contents and 2) to alleviate the influence of forecast error on the rank of prioritized contents, we propose a content-caching-oriented user clustering algorithm based on the K-means algorithm. Since the involved optimization problem is NP-hard, we propose an iterative algorithm, whose convergence property in terms of region stability is proved, as the objective function may vary before a local minima is reached. Finally, the simulation results demonstrate the superiority of the proposed framework.

Edge learning is a promising enabler to leverage the distributed local data for powering the artificial intelligence at the edge network. Moreover, incorporating the external domain knowledge into purely data-driven learning models can further enhance the performance. In this paper, by taking both the benefits of edge learning and knowledge fusion, we propose a novel distributed knowledge-aware edge learning framework, in which the edge devices individually train the learning models with the assistance of the local knowledge bases at the edge devices and the global knowledge base at the edge server. Due to the limited cache capability, the edge device can only cache a small-scale local knowledge base, which restricts the performance gain by local knowledge fusion. Meanwhile, uploading local data from multiple edge devices for global knowledge fusion may lead to the air-interface congestion. To overcome these issues, we first formulate the global loss decay maximization problem with transmission scheduling decisions. Specifically, we derive the closed-form relationship between transmission scheduling and the learning performance. Then, we depict the implicit relationship between the knowledge fusion and the global loss decay via establishing a specific multi-armed bandit (MAB) framework, and derive an asymptotically-optimal solution accordingly. Extensive simulations demonstrate that the proposed policies outperform the state-of-art policies.

The explosive growth of e-commerce and online service has led to the development of recommender system. Aiming to provide a list of items to meet a user’s personalized need by analyzing his/her interaction 1 history, recommender system has been widely studied in academic and industrial communities. Different from conventional recommender systems, sequential recommender systems attempt to capture the pattern of users’ sequential behaviors and the evolution of users’ preferences. Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence. An item interaction sequence also contains rich contextual information for capturing the item’s dynamic characteristic, since an item’s dynamic characteristic can be reflected by the users who interact with it in a period. Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics. Hence, we propose a novel D ouble A ttention C onvolution N eural N etwork (DACNN) , which incorporates user interaction sequence and item interaction sequence into an integrated neural network framework. DACNN leverages the strength of attention mechanism to capture the temporary suitability and adopts CNN to extract local sequential features. Experimental evaluations on the real datasets show that DACNN outperforms the baseline approaches.

The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strategy while considering both the transmission and caching resources, which jointly result in the complicated coupling among pushing decisions and lead to high computational complexity. Unlike most existing approaches which simplify the pushing problem via bypassing the complicated coupling, in this paper, we propose a multicast content pushing strategy to maximize the offloaded traffic with the cost on content caching based on structured deep learning. Specifically, we design the convolution stage to extract the spatio-temporal correlations of one content item between different pushing decisions, and construct the fully-connected stage to capture the spatial coupling among the decisions of pushing different content items to different user devices. Moreover, to address the absence of the ground truth on multicast content pushing, we relax the transmission constraint to derive a performance upper bound for guiding the training direction. This relaxed problem is solved based on dynamic programming in a bottom-up manner. Compared to the state-of-the-art baselines including both the traditional model-based and the general neural network-based strategies, the proposed pushing strategy achieves significant performance gain in both the random-generated dataset and the real LastFM dataset. In addition, it is also shown that the proposed strategy is robust to the uncertainty of user request information.

Timely response to changes of monitored objects is the key to ensuring the safety and reliability of cyber-physical systems (CPSs). There are two kinds of tasks in CPSs: update tasks and control tasks. Update tasks are responsible for updating the data in the system based on the state of the objects they monitor. Control tasks are responsible for making decisions based on the data in the system. The response time of the system to the change of a monitored object consists of two parts: the time taken by update tasks to reflect the change to the system, and the time taken by control tasks to make decisions according to the data in the system. Deadlines and periods of update tasks and control tasks directly affect the response time. Reasonable deadline and period assignment is the key to ensuring timely response to the changes of monitored objects. In this paper, we study the deadline and period assignment in CPSs. To the best of our knowledge, all existing work only focuses on the deadline and period assignment for update tasks with the goal of ensuring the freshness of the data in CPSs, and this is the first study focusing on the deadline and period assignment for both update tasks and control tasks with the goal of ensuring timely response to the changes of monitored objects. A new problem about response time control and system workload control is defined in this paper. Two deadline and period assignment methods are proposed to solve the defined problem. All the proposed methods can be used in the CPSs adopting the earliest deadline first (EDF) scheduling method. Experiments with randomly generated tasks are conducted to evaluate the performance of the proposed methods in terms of acceptance ratio and execution efficiency.

Content popularity prediction plays an important role on proactive content caching. Different to most of the existing works which focus on improving the popularity prediction accuracy, in this article, we consider the content caching oriented popularity prediction through a weighted clustering approach in order to improve the caching performance. We formulate the loss of the cache hit ratio as the system regret to indicate the caching performance, and construct a clustering-based popularity prediction framework for overcoming the user request sparsity with considering the similarity of popularity evolution trends. For depicting the explicit relationship between the caching performance and the popularity prediction accuracy, we derive the popularity prediction error distribution of each content, and design the caching threshold. By extracting the insights in the relationship between the popularity prediction accuracy and the user clustering strategy, we develop a weighted clustering-based popularity prediction algorithm, which takes the caching regret probability of files as the weights. Based on two real-world datasets, the simulation results demonstrate that the proposed popularity prediction scheme achieves better caching performance than the state-of-the-art schemes.

The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods— I tem L evel Similarity M atrix F actorization (ILMF) and U ser L evel Similarity M atrix F actorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose I tem- U ser L evel Similarity M atrix F actorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.

Content caching should be updated according to the time-varying content popularity. However, with the consideration of the time-consuming cache replacement process, it is necessary to predict the content popularity and adjust the cached contents beforehand. In this paper, we propose a popularity prediction scheme for content caching. To overcome the request sparsity and exploit the diversity of popularity evolution trends, the users are grouped into non-overlapped clusters for predicting content popularity for each cluster respectively. Different to most of the existing works which focus on the accuracy of prediction, we consider the effect of the prediction error to content caching and adopt the loss of the cache hit ratio as the system regret. In the proposed clustered popularity prediction scheme, the system regret is estimated by analyzing its own prediction error distribution and obtaining the influence from those of other contents through an online learning framework. To achieve the optimal system performance, we design a K-mean clustering algorithm according to the expected regret and the popularity evolution trends. The simulation results show that the proposed clustered popularity prediction scheme achieves better caching performance than the state-of-the-art caching schemes.

With mobile edge computing (MEC), the data compression at the edge devices can effectively improve the communication efficiency by transmitting the compressed data. In this paper, we construct a joint data compression and transmission scheduling framework to optimize the system throughput with the limited transmission resources. Different to most of the existing works, we consider the interaction between the data compression and data transmission to achieve the optimal throughput. Specifically, to explore the effect of data compression, we construct a queue system through constructing the mapping between the original data queues and the compressed data queues under different compression schemes (including the uncompressed queues). We design the transmission scheduling algorithm based on Lyapunov optimization according to the original data queues. Due to the nature that the data compression does not change the original data queue length directly, we choose the optimal data compression scheme considering the achieved utilities when the compressed data are transmitted, which can be estimated via Q-learning. In addition, we theoretically prove the queue stability under our proposed joint data compression and transmission scheduling algorithm. The simulation results show that the proposed algorithm has better delay performance than the conventional schemes.

Cooperative caching is a promising technology for enhancing user experience and reducing redundant transmissions through the participation of multiple caching nodes. In this paper, we design a clustering algorithm for the sectionalized caching, in which each user divides its caching space into two parts and the contents cached in these two parts are determined according to the individual interest and the joint interest of all users in the same cluster respectively. Different to most of the existing works forming the clusters based on the interest similarity of all files, we adopt the piecewise interest similarity as the criterion of clustering, which takes advantage of the content diversity and contributes to the reduction of the transmission delay. We measure the gain of the cooperation between two users and obtain the piecewise interest similarity for two users accordingly. Since the gain of clustered caching highly depends on the formed cluster structure, we estimate the gain of the clustered caching based on the piecewise interest similarities by online learning and propose an affinity propagation (AP) based clustering algorithm. Finally, our proposed clustering algorithm is evaluated by simulation to show its superiority over the conventional clustering algorithms.