Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring.

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection of attacks. Machine learning (ML) and its subdivision Deep Learning (DL) methods offer a promise, but they can be computationally expensive in providing better detection for resource-constrained IoT devices. Therefore, this research proposes an optimization method to train ML and DL methods for effective and efficient security monitoring of IoT devices. It first investigates the feasibility of the Light Gradient Boosting Machine (LGBM) for attack detection in IoT environments, proposing an optimization procedure to obtain its effective counterparts. The trained LGBM can successfully discern attacks and regular traffic in various IoT benchmark datasets used in this research. As LGBM is a traditional ML technique, it may be difficult to learn complex network traffic patterns present in IoT datasets. Therefore, we further examine Deep Neural Networks (DNNs), proposing an effective and efficient DNN-based security solution for IoT security monitoring to leverage more resource savings and accurate attack detection. Investigation results are promising, as the proposed optimization method exploits the mini-batch gradient descent with simulated micro-batching in building effective and efficient DNN-based IoT security solutions. Following the success of DNN for effective and efficient attack detection, we further exploit it in the context of adversarial attack resistance. The resulting DNN is more resistant to adversarial samples than its benchmark counterparts and other conventional ML methods. To evaluate the effectiveness of our proposal, we considered on-device learning in federated learning settings, using decentralized edge devices to augment data privacy in resource-constrained environments. To this end, the performance of the method was evaluated against various realistic IoT datasets (e.g. NBaIoT, MNIST) on virtual and realistic testbed set-ups with GB-BXBT-2807 edge-computing-like devices. The experimental results show that the proposed method can reduce memory and time usage by 81% and 22% in the simulated environment of virtual workers compared to its benchmark counterpart. In the realistic testbed scenario, it saves 6% of memory footprints with a reduction of execution time by 15%, while maintaining a better and state-of-the-art accuracy

Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited

Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities

Situation-aware Edge Computing

Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

Smart Metering System: Developing New Designs to Improve Privacy and Functionality

This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase thepredictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

A Secured Data Management Scheme for Smart Societies in Industrial Internet of Things Environment

Smart societies have an increasing demand for quality-oriented services and infrastructure in an Industrial Internet of Things (IIoT) paradigm. Smart urbanization faces numerous challenges. Among them, secured energy Demand Side Management (DSM) is of particular concern. The IIoT renders the industrial systems to malware, cyber attacks, and other security risks. The IIoT with the amalgamation of Big Data analytics can provide efficient solutions to such challenges. This paper proposes a secured and trusted multi-layered DSM engine for a smart social society using IIoT-based Big Data analytics. The proposed engine uses a centralized approach to achieve optimum DSM over a Home Area Network (HAN). To enhance the security of this engine, a payload-based authentication scheme is utilized that relies on a lightweight handshake mechanism. Our proposed method utilizes the lightweight features of Constrained Application Protocol (CoAP) to facilitate the clients in monitoring various resources residing over the server in an energy-efficient manner. In addition, data streams are processed using Big Data analytics with MapReduce parallel processing. The proposed authentication approach is evaluated using NetDuino Plus 2 boards that yield a lower connection overhead, memory consumption, response time and a robust defense against various malicious attacks. On the other hand, our data processing approach is tested on reliable datasets using Apache Hadoop with Apache Spark to verify the proposed DMS engine. The test results reveal that the proposed architecture offers valuable insights into the smart social societies in the context of IIo

A Secured Data Management Scheme for Smart Societies in Industrial Internet of Things Environment

Smart societies have an increasing demand for quality-oriented services and infrastructure in an Industrial Internet of Things (IIoT) paradigm. Smart urbanization faces numerous challenges. Among them, secured energy Demand Side Management (DSM) is of particular concern. The IIoT renders the industrial systems to malware, cyber attacks, and other security risks. The IIoT with the amalgamation of Big Data analytics can provide efficient solutions to such challenges. This paper proposes a secured and trusted multi-layered DSM engine for a smart social society using IIoT-based Big Data analytics. The proposed engine uses a centralized approach to achieve optimum DSM over a Home Area Network (HAN). To enhance the security of this engine, a payload-based authentication scheme is utilized that relies on a lightweight handshake mechanism. Our proposed method utilizes the lightweight features of Constrained Application Protocol (CoAP) to facilitate the clients in monitoring various resources residing over the server in an energy-efficient manner. In addition, data streams are processed using Big Data analytics with MapReduce parallel processing. The proposed authentication approach is evaluated using NetDuino Plus 2 boards that yield a lower connection overhead, memory consumption, response time and a robust defense against various malicious attacks. On the other hand, our data processing approach is tested on reliable datasets using Apache Hadoop with Apache Spark to verify the proposed DMS engine. The test results reveal that the proposed architecture offers valuable insights into the smart social societies in the context of IIo