Mobile data traffic will continue its tremendous growth in some markets, and has already resulted in an apparent radio spectrum scarcity. There is a strong need for more efficient methods to use spectrum resources, leading to extensive research on increasing spectrum reusability on flexible radio platforms. This study solves this problem in two sub topics, millimeter wave communication on wireless backhaul for spectrum reusability, and flexible prototyping radio platform using software-defined radio (SDR).
Wireless backhaul has received significant attention as a key technology affecting the development of future wireless cellular networks because it helps to easily deploy many small size cells, an essential part of a high capacity system. Millimeter wave is considered a possible candidate for cost-effective wireless backhaul. In the outdoor deployment using a millimeter wave, beamforming methods are key techniques to establish wireless links in the 60 GHz to 80 GHz to overcome pathloss constraints (i.e., rainfall effect and oxygen absorption). The millimeter wave communication system cannot directly access the channel knowledge. To overcome this, a beamforming method based on codebook search is considered. The millimeter wave communication cannot access channel knowledge, therefore alternatively a beamforming method based on a codebook search is considered. In the first part, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. Results show significant performance improvement with reduced search time.
In the second part of this study, SDR is discussed as an approach toward flexible wireless communication systems. Most layers of SDR are implemented by software. Therefore, only a software change is needed to transform the type of radio system. The translation of the signal processing into software performed by a regular computer opens up a huge number of possibilities at a reasonable price and effort. SDR systems are widely used to build prototypes, saving time and money. In this project, a robust wireless communication system in high interference environment was developed. For the physical layer (PHY) of the system, we implemented a channel sub-bandding method that utilizes frequency division multiplexing to avoid interference. Then, to overcome a further interfered channel, Direct Spread Spectrum System (DSSS) was considered and implemented. These prototyped testbeds were evaluated for system performance in the interference environment.

Millimeter wave beamforming for wireless backhaul and access in small cell networks and practical approaches in software-defined radio

In terms of scalability, cost and ease of deployment, the Peer-to-Peer (P2P) approach has emerged as a promising solution for video streaming applications. Its architecture enables end-hosts, called peers, to relay the video stream to each other. P2P systems are in fact networks of users who control peers. Thus, user behavior is crucial to the performance of these systems because it directly impacts the streaming flow. To understand user behavior, several measurement studies have been carried out over different video streaming systems. Each measurement analyzes a particular system focusing on specific metrics and presents insights. However, a single study based on a particular system and specific metrics is not sufficient to provide a complete model of user behavior considering all of its components and the impact of external factors on them. In this paper, we propose a comparison and a synthesis of these measurements. First of all, we review video streaming architectures, followed by a survey on the user behavior measurements in these architectures. Then, we gather insights revealed in these measurements and compare them for consensual and contrasting points. Finally, we extract components of user behavior, their external impacting factors and relationships among them. We also point out those aspects of user behavior which require further investigations.

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A Survey and Synthesis of User Behavior Measurements in P2P Streaming Systems

The Internet of Things (IoT) is a massively extensive environment that can manage many diverse applications. Security is critical due to potential malicious threats and the diversity of the connectivity. Devices can protect themselves and detect threats with the Intrusion Detection System (IDS). IDS typically uses one of two approaches: anomaly-based or signature-based. This paper proposes a model (known as “AS-IDS”) that combines these two approaches to detect known and unknown attacks in IoT networks. The proposed model has three phases: traffic filtering, preprocessing and the hybrid IDS. In the first phase, the arrival traffic is filtered at the IoT gateway by matching packet features, after which the preprocessing phase applies a Target Encoder, Z-score and Discrete Hessian Eigenmap (DHE) to encode, normalize and eliminate redundancy, respectively. In the final phase, the hybrid IDS integrates signatures and anomalies. The signature-based IDS subsystem investigates packets with Lightweight Neural Network (LightNet), which uses Human Mental Search (HMS) for traffic clustering in the hidden layer and Boyer Moore is used to search for a particular signature in the output layer that is accelerated by using the Generalized Suffix Tree (GST) algorithm and by matching the signatures it classifies the attacks as intruder, normal or unknown. The anomaly-based IDS subsystem employs Deep Q-learning to identify unknown attacks, and uses Signal to Noise Ratio (SNR) and bandwidth to classify the attacks into five classes: Denial of Service (DoS), Probe, User-to-Root (U2R), Remote-to-Local (R2L), and normal traffic. Detected packets are then generated with new signatures, using the Position Aware Distribution Signature (PADS) algorithm. The proposed AS-IDS is implemented in real-time traffic with the NSL-KDD dataset, and the results are evaluated in terms of Detection Rate (DR), False Alarm Rate (FAR), Specificity, F-measure and computation time.

AS-IDS: Anomaly and Signature Based IDS for the Internet of Things

The explosive development of electrical engineering in the early 19th century marked the birth of the 2nd industrial revolution, with the use of electrical energy in place of steam power, as well as changing the history of human development. The versatility of electricity allows people to apply it to a multitude of fields such as transportation, heat applications, lighting, telecommunications, and computers. Nowadays, with the breakout development of science and technology, electric energy sources are formed by many different technologies such as hydroelectricity, solar power, wind power, coal power, etc. These energy sources are connected to form grid systems to transmit electricity to cities, businesses and homes for life and work. Electrical energy today has become the backbone of all modern technologies. To ensure the safe, reliable and energy-efficient operation of the grid, a wide range of grid management applications have been proposed. However, a significant challenge for monitoring and controlling grids is service response time. In recent times, to solve this problem, smart grid management applications based on IoT and edge computing have been proposed. In this work, we perform a comprehensive survey of edge computing for IoT-enabled smart grid systems. In addition, recent smart grid frameworks based on IoT and edge computing are discussed, important requirements are presented, and the open issues and challenges are indicated. We believe that in the Internet of Things era, the smart grid will be the future of energy. We hope that these study results will contribute important guidelines for in-depth research in the field of smart grids and green energy in the future.

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Edge Computing for IoT-Enabled Smart Grid: The Future of Energy

Containers emerged as a lightweight alternative to virtual machines that offer better microservice architecture support. They are widely used by organizations to deploy their increasingly diverse workloads derived from modern applications such as big data, IoT, and edge/fog computing in either proprietary clusters or private, public cloud data centers. With the growing interest in container-based virtualization technologies, the requirement to explore the deployment and orchestration of clusters of containers has become a central research problem. Although progress has been made to study containerization, systematic consolidation of the existing literature with a summative evaluation is still missing. To fill this gap, in this paper, we first taxonomically classify the existing research studies on the performance comparison between hypervisor and container technology and then analyze state-of-the-art for container cluster management orchestration systems, its performance monitoring tools, and finally future research trends. This results in a better understanding of container technology with attention to provide summative analysis in terms of (i) how much performance overhead is generated by a hypervisor compared to container-based virtualization, (ii) which container technology is suited for a cloud application deployment based on the type of benchmark executing, (iii) how to provide management of containers deployed in a cluster environment, (iv) container performance monitoring tools, and (v) finally emerging concerns for future research directions.

Virtualization in Cloud Computing: Moving from Hypervisor to Containerization—A Survey

Every day, a massive amount of text, audio, and video data is published on websites all over the world. This valuable data can be used to gauge global trends and public perceptions. Companies are showcasing their preferred advertisements to consumers based on their online behavioral trends. Carefully analyzing this raw data to uncover useful patterns is indeed a challenging task, even more so for a resource-constrained language such as Urdu. A unique Urdu language-based multimodal dataset containing 1372 expressions has been presented in this paper as a first step to address the challenge to reveal useful patterns. Secondly, we have also presented a novel framework for multimodal sentiment analysis (MSA) that incorporates acoustic, visual, and textual responses to detect context-aware sentiments. Furthermore, we have used both decision-level and feature-level fusion methods to improve sentiment polarity prediction. The experimental results demonstrated that integration of multimodal features improves the polarity detection capability of the proposed algorithm from 84.32% (with unimodal features) to 95.35% (with multimodal features).

Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms

Internet of Things (IoT) networks consist of tiny devices with limited processing resources and restricted energy budget. These devices are connected to the world-wide web (www) using networking protocols. Considering their resource limitations, they are vulnerable to security attacks by numerous entities on the Internet. The classical security solutions cannot be directly implemented on top of these devices for this reason. However, an Intrusion Detection System (IDS) is a classical way to protect these devices by using low-cost solutions. IDS monitors the network by introducing various metrics, and potential intruders are identified, which are quarantined by the firewall. One such metric is reputation management, which monitors the behavior of the IoT networks. However, this technique may still result in detection error that can be optimized by combining this solution with honeypots. Therefore, our aim is to add some honeypots in the network by distributing them homogeneously as well as randomly. These honeypots will team up with possible maliciously behaving nodes and will monitor their behavior. As per the simulation results, this technique reduces the error rate within the existing IDS for the IoT; however, it costs some extra energy. This trade-off between energy consumption and detection accuracy is studied by considering standard routing and MAC protocol for the IoT network.

https://www.mdpi.com/2079-9292/9/3/415/pdf

Reputation Management Using Honeypots for Intrusion Detection in the Internet of Things

Energy plays a pivotal role for economic development of a country. A reliable energy source is needed to improve the living standards of people. To achieve such a goal, governments and industries are trying to install a new energy infrastructure called the “Smart Grid”. This helps to manage the electricity generation and distribution in an efficient manner. Buildings and other structures are the biggest consumers of electricity. There is a need to reduce the energy consumption so that the resources can be utilized efficiently. Therefore, in this paper, we give a comprehensive state-of-the-art on various recent techniques and solutions which provide energy savings in smart homes and buildings. This includes statistical models, cloud computing based solutions, fog computing and smart metering based architectures, and several other IoT (internet of things) inspired solutions. We also present a hypothetical model that treats energy supply and usage in buildings as a self-managing energy system (SES). This paper is concluded by highlighting several open issues and challenges related to energy management in buildings

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Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

Layered video streaming in peer-to-peer (P2P) networks has drawn great interest, since it can not only accommodate large numbers of users, but also handle peer heterogeneity. However, there’s still a lack of comprehensive studies on chunk scheduling for the smooth playout of layered streams in P2P networks. In these situations, a playout smoothing mechanism can be used to ensure the uniform delivery of the layered stream. This can be achieved by reducing the quality changes that the stream undergoes when adapting to changing network conditions. This paper complements previous efforts in throughput maximization and delay minimization for P2P streaming by considering the consequences of playout smoothing on the scheduling mechanisms for stream layer acquisition. The two main problems to be considered when designing a playout smoothing mechanism for P2P streaming are the fluctuation in available bandwidth between peers and the unreliability of user-contributed resources—particularly peer churn. Since the consideration of these two factors in the selection and scheduling of stream layers is crucial to maintain smooth stream playout, the main objective of our smoothing mechanism becomes the determination of how many layers to request from which peers, and in which order. In this work, we propose a playout smoothing mechanism for layered P2P streaming. The proposed mechanism relies on a novel scheduling algorithm that enables each peer to select appropriate stream layers, along with appropriate peers to provide them. In addition to playout smoothing, the presented mechanism also makes efficient use of network resources and provides high system throughput. An evaluation of the performance of the mechanism demonstrates that the proposed mechanism provides a significant improvement in the received video quality in terms of lowering the number of layer changes and useless chunks while improving bandwidth utilization.

https://hal.archives-ouvertes.fr/hal-00875223/document

An efficient playout smoothing mechanism for layered streaming in P2P networks

Most P2P VoD schemes focused on service architectures and overlays optimization without considering segments rarity and the performance of prefetching strategies As a result, they cannot better support VCR-oriented services Despite the remarkable popularity in VoD systems, there exists no prior work that studies the performance gap between different prefetching strategies In this paper we analyze and understand the performance of different prefetching strategies Our analytical characterization brings us not only a better understanding of several fundamental tradeoffs in prefetching strategies, but also important insights on the design of P2P VoD system On the basis of this analysis, we finally proposed a cooperative prefetching strategy namely "COOCHING"In this strategy, the segments requested in VCR interactivities are prefetched into session beforehand using the information collected through gossips

/pdf/cooching-cooperative-prefetching-strategy-for-p2p-video-on-ut59mzr6c1.pdf

Ubaid Abbasi

Papers

Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms

Reputation Management Using Honeypots for Intrusion Detection in the Internet of Things

Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

An efficient playout smoothing mechanism for layered streaming in P2P networks

COOCHING: Cooperative Prefetching Strategy for P2P Video-on-Demand System