Wireless networks QoS optimization using coded caching and machine learning algorithms

Proactive caching shows great potential to minimize peak traffic rates by storing popular data, in advance, at different nodes in the network. We study three new angles of proactive caching that were not covered before in the literature. We develop more practical algorithms that bring proactive caching closer to practical wireless networks. The first angle is where the popularities of the cached files are changing over time and the file delivery is asynchronous. We provide an algorithm that minimizes files’ delivery rate under this setting. We show that we can use the file delivery messages to proactively and constantly update the receiver finite caches. We show that this mechanism reduces the downloaded traffic of the network. The proposed scheme uses index coding [1], and app. A to jointly encodes the delivery of different demanded files with the cache updates to other receivers to follow the changes in the file popularities. An offline and online (dynamic) versions of the scheme are proposed, where the offline version requires knowledge of the file popularities across the whole transmission period in advance and the online one requires the file popularities for one succeeding time slot only. The optimal caching for both the offline and online schemes is obtained numerically. The second angle is the study of segmented caching for delay minimization in networks with congested backhaul. Studies have mainly focused on proactively storing popular whole files. For certain categories of files like videos, this is not the best strategy. As videos can be segmented, sending later segments of videos can be less time-critical. Video is expected to constitute 82% of internet traffic by 2020 [2]. We study the effect of segmenting video caching decisions under the assumption that the backhaul is congested. We provide an algorithm for proactive segmented caching that optimizes the choice of segments to be cached to minimize delay and compare the performance to the whole file proactive caching. The third angle focuses on using reinforcement learning for coded caching in networks with changing file popularities. For such a dynamic environment, reinforcement learning has the flexibility to learn the environment and adapt accordingly. We develop a reinforcement learning-based coded caching algorithm and compare its performance to rule-based coded caching

Leveraging Mobile App Classification and User Context Information for Improving Recommendation Systems

Mobile apps play a significant role in current online environments where there is an overwhelming supply of information. Although mobile apps are part of our daily routine, searching and finding mobile apps is becoming a nontrivial task due to the current volume, velocity and variety of information. Therefore, app recommender systems provide users’ desired apps based on their preferences. However, current recommender systems and their underlying techniques are limited in effectively leveraging app classification schemes and context information. In this thesis, I attempt to address this gap by proposing a text analytics framework for mobile app recommendation by leveraging an app classification scheme that incorporates the needs of users as well as the complexity of the user-item-context information in mobile app usage pattern. In this recommendation framework, I adopt and empirically test an app classification scheme based on textual information about mobile apps using data from Google Play store. In addition, I demonstrate how context information such as user social media status can be matched with app classification categories using tree-based and rule-based prediction algorithms. Methodology wise, my research attempts to show the feasibility of textual data analysis in profiling apps based on app descriptions and other structured attributes, as well as explore mechanisms for matching user preferences and context information with app usage categories. Practically, the proposed text analytics framework can allow app developers reach a wider usage base through better understanding of user motivation and context information

Modified reinforcement learning based- caching system for mobile edge computing

International audienceCaching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links loadand reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an importantissue. Recently, the tremendous growth ofMobile Edge Computing(MEC) empowers the edge network nodes withmore computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within themobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context informationintelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the cachingperformance, the suitable methodology is to consider both context awareness and intelligence so that the cachingstrategy is aware of the environment while caching the appropriate content by making the right decision. Inspiredby the success ofreinforcement learning(RL) that uses agents to deal with decision making problems, we presentamodified reinforcement learning(mRL) to cache contents in the network edges. Our proposed solution aims tomaximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. Themodified RL differs from other RL algorithms in the learning rate that uses the method ofstochastic gradient decent(SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.Index Terms — Caching, Reinforcement Learning, Fuzzy Logic, Mobile Edge Computing

Analysis of Family-Health-Related Topics on Wikipedia

New concepts, terms, and topics always emerge; and meanings of existing terms and topics keep changing all the time. These phenomena occur more frequently on social media than on conventional media because social media allows a huge number of users to generate information online. Retrieving relevant results in different time periods of a fast-changing topic becomes one of the most difficult challenges in the information retrieval field. Among numerous topics discussed on social media, health-related topics are a major category which attracts increasing attention from the general public. This study investigated and explored the evolution patterns of family-health-related topics on Wikipedia. Three family-health-related topics (Child Maltreatment, Family Planning, and Women’s Health) were selected from the World Health Organization Website and their associated entries were retrieved on Wikipedia. Historical numeric and text data of the entries from 2010 to 2017 were collected from a Wikipedia data dump and the Wikipedia Web pages. Four periods were defined: 2010 to 2011, 2012 to 2013, 2014 to 2015, and 2016 to 2017. Coding, subject analysis, descriptive statistical analysis, inferential statistical analysis, SOM approach, and n-gram approach were employed to explore the internal characteristics and external popularity evolutions of the topics. The findings illustrate that the external popularities of the family-health-related topics declined from 2010 to 2017, although their content on Wikipedia kept increasing. The emerged entries had three features: specialization, summarization, and internationalization. The subjects derived from the entries became increasingly diverse during the investigated periods. Meanwhile, the developing trajectories of the subjects varied from one to another. According to the developing trajectories, the subjects were grouped into three categories: growing subject, diminishing subject, and fluctuating subject. The popularities of the topics among the Wikipedia viewers were consistent, while among the editors were not. For each topic, its popularity trend among the editors and the viewers was inconsistent. Child Maltreatment was the most popular among the three topics, Women’s Health was the second most popular, while Family Planning was the least popular among the three. The implications of this study include: (1) helping health professionals and general users get a more comprehensive understanding of the investigated topics; (2) contributing to the developments of health ontologies and consumer health vocabularies; (3) assisting Website designers in organizing online health information and helping them identify popular family-health-related topics; (4) providing a new approach for query recommendation in information retrieval systems; (5) supporting temporal information retrieval by presenting the temporal changes of family-health-related topics; and (6) providing a new combination of data collection and analysis methods for researchers

Learning-based Decision Making in Wireless Communications

Fueled by emerging applications and exponential increase in data traffic, wireless networks have recently grown significantly and become more complex. In such large-scale complex wireless networks, it is challenging and, oftentimes, infeasible for conventional optimization methods to quickly solve critical decision-making problems. With this motivation, in this thesis, machine learning methods are developed and utilized for obtaining optimal/near-optimal solutions for timely decision making in wireless networks. Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. In this context, we in the first part of the thesis study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. Initially, we develop a learning-based caching policy for a single base station aiming at maximizing the long-term cache hit rate. Then, we extend this study to a wireless communication network with multiple edge nodes. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. Next, with the purpose of making efficient use of limited spectral resources, we develop a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework\u27s tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. Following the analysis of the proposed learning-based dynamic multichannel access policy, we consider adversarial attacks on it. In particular, we propose two adversarial policies, one based on feed-forward neural networks and the other based on deep reinforcement learning policies. Both attack strategies aim at minimizing the accuracy of a deep reinforcement learning based dynamic channel access agent, and we demonstrate and compare their performances. Next, anomaly detection as an active hypothesis test problem is studied. Specifically, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. Separately, we also regard the detection of threshold crossing as an anomaly detection problem, and analyze it via hierarchical generative adversarial networks (GANs). In the final part of the thesis, to address state estimation and detection problems in the presence of noisy sensor observations and probing costs, we develop a soft actor-critic deep reinforcement learning framework. Moreover, considering Byzantine attacks, we design a GAN-based framework to identify the Byzantine sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total probing cost needed for detection

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

Securing Enterprise Networks with Statistical Node Behavior Profiling

The substantial proliferation of the Internet has made it the most critical infrastructure in today\u27s world. However, it is still vulnerable to various kinds of attacks/malwares and poses a number of great security challenges. Furthermore, we have also witnessed in the past decade that there is always a fast self-evolution of attacks/malwares (e.g. from worms to botnets) against every success in network security. Network security thereby remains a hot topic in both research and industry and requires both continuous and great attention. In this research, we consider two fundamental areas in network security, malware detection and background traffic modeling, from a new view point of node behavior profiling under enterprise network environments. Our main objectives are to extend and enhance the current research in these two areas. In particular, central to our research is the node behavior profiling approach that groups the behaviors of different nodes by jointly considering time and spatial correlations. We also present an extensive study on botnets, which are believed to be the largest threat to the Internet. To better understand the botnet, we propose a botnet framework and predict a new P2P botnet that is much stronger and stealthier than the current ones. We then propose anomaly malware detection approaches based directly on the insights (statistical characteristics) from the node behavior study and apply them on P2P botnet detection. Further, by considering the worst case attack model where the botmaster knows all the parameter values used in detection, we propose a fast and optimized anomaly detection approach by formulating the detection problem as an optimization problem. In addition, we propose a novel traffic modeling structure using behavior profiles for NIDS evaluations. It is efficient and takes into account the node heterogeneity in traffic modeling. It is also compatible with most current modeling schemes and helpful in generating better realistic background traffic. Last but not least, we evaluate the proposed approaches using real user trace from enterprise networks and achieve encouraging results. Our contributions in this research include: 1) a new node behavior profiling approach to study the normal node behavior; 2) a framework for botnets; 3) a new P2P botnet and performance comparisons with other P2P botnets; 4) two anomaly detection approaches based on node behavior profiles; 4) a fast and optimized anomaly detection approach under the worst case attack model; 5) a new traffic modeling structure and 6) simulations and evaluations of the above approaches under real user data from enterprise networks. To the best of our knowledge, we are the first to propose the botnet framework, consider the worst case attack model and propose corresponding fast and optimized solution in botnet related research. We are also the first to propose efficient solutions in traffic modeling without the assumption of node homogeneity

Measuring urban activities using Foursquare data and network analysis: a case study of Murcia (Spain)

Among social networks, Foursquare is a useful reference for identifying recommendations about local stores, restaurants, malls or other activities in the city. In this article, we consider the question of whether there is a relationship between the data provided by Foursquare regarding users’ tastes and preferences and fieldwork carried out in cities, especially those connected with business and leisure. Murcia was chosen for case study for two reasons: its particular characteristics and the prior knowledge resulting from the fieldwork. Since users of this network establish, what may be called, a ranking of places through their recommendations, we can plot these data with the objective of displaying the characteristics and peculiarities of the network in this city. Fieldwork from the city itself gives us a set of facilities and services observed in the city, which is a physical reality. An analysis of these data using a model based on a network centrality algorithm establishes a classification or ranking of the nodes that form the urban network. We compare the data extracted from the social network with the data collected from the fieldwork, in order to establish the appropriateness in terms of understanding the activity that takes place in this city. Moreover, this comparison allows us to draw conclusions about the degree of similarity between the preferences of Foursquare users and what was obtained through the fieldwork in the city.This work was partially supported by Spanish Govern, Ministerio de Economía y Competividad, the reference number of which is TIN2014-53855-P