arXiv subjects

Rasheed Hussain

Publications and source records attributed to Rasheed Hussain.

27 records · Page 2Linked to original sources

arXiv2019

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

cs.NI↗

arXiv2019

Machine Learning in IoT Security: Current Solutions and Future Challenges

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.

cs.CR↗

arXiv2019

An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT).

cs.CY↗

arXiv2018

Teaching Programming and Design-by-Contract

This paper summarizes the experience of teaching an introductory course to programming by using a correctness by construction approach at Innopolis University, Russian Federation. In this paper we claim that division in beginner and advanced groups improves the learning outcomes, present the discussion and the data that support the claim.

cs.SE↗

arXiv2018

On M2M Micropayments : A Case Study of Electric Autonomous Vehicles

The proliferation of electric vehicles has spurred the research interest in technologies associated with it, for instance, batteries, and charging mechanisms. Moreover, the recent advancements in autonomous cars also encourage the enabling technologies to integrate and provide holistic applications. To this end, one key requirement for electric vehicles is to have an efficient, secure, and scalable infrastructure and framework for charging, billing, and auditing. However, the current manual charging systems for EVs may not be applicable to the autonomous cars that demand new, automatic, secure, efficient, and scalable billing and auditing mechanism. Owing to the distributed systems such as blockchain technology, in this paper, we propose a new charging and billing mechanism for electric vehicles that charge their batteries in a charging-on-the-move fashion. To meet the requirements of billing in electric vehicles, we leverage distributed ledger technology (DLT), a distributed peer-to-peer technology for micro-transactions. Our proof-of-concept implementation of the billing framework demonstrates the feasibility of such system in electric vehicles. It is also worth noting that the solution can easily be extended to the electric autonomous cars (EACs).

cs.CY↗

arXiv2018

Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks

Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of $k$ binary networks vs. a single $k$-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.

cs.CV↗

arXiv2018

Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction

Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhibited in the data but at the expense of loosing its original representation. To cope with the said issue, an unsupervised non-linear segmented and non-segmented stacked denoising autoencoder (UDAE) based BR method is proposed. Our aim is to find an optimal mapping and construct a lower-dimensional space that has a similar structure to the original data with least reconstruction error. The proposed method first confronts the original hyperspectral data into smaller regions in a spatial domain and then each region is processed by UDAE individually. This results in reduced complexity and improved efficiency of BR for both semi-supervised and unsupervised tasks, i.e. classification and clustering. Our experiments on publicly available hyperspectral datasets with various types of classifiers demonstrate the effectiveness of UDAE method which equates favorably with other state-of-the-art dimensionality reduction and BR methods.

cs.CV↗

arXiv2018

Reputation in M2M Economy

Triggered by modern technologies, our possibilities may now expand beyond the unthinkable. Cars externally may look similar to decades ago, but a dramatic revolution happened inside the cabin as a result of their computation, communications, and storage capabilities. With the advent of Electric Autonomous Vehicles (EAVs), Artificial Intelligence and ecological technologies found the best synergy. Several transportation problems may be solved (accidents, emissions, and congestion among others), and the foundation of Machine-to-Machine (M2M) economy could be established, in addition to value-added services such as infotainment (information and entertainment). In the world where intelligent technologies are pervading everyday life, software and algorithms play a major role. Software has been lately introduced in virtually every technological product available on the market, from phones to television sets to cars and even housing. Artificial Intelligence is one of the consequences of this pervasive presence of algorithms. The role of software is becoming dominant and technology is, at times pervasive, of our existence. Concerns, such as privacy and security, demand high attention and have been already explored to some level of detail. However, intelligent agents and actors are often considered as perfect entities that will overcome human error-prone nature. This may not always be the case and we advocate that the notion of reputation is also applicable to intelligent artificial agents, in particular to EAVs.

cs.CY↗

arXiv2015

PBF: A New Privacy-Aware Billing Framework for Online Electric Vehicles with Bidirectional Auditability

Recently an online electric vehicle (OLEV) concept has been introduced, where vehicles are propelled through the wirelessly transmitted electrical power from the infrastructure installed under the road while moving. The absence of secure-and-fair billing is one main hurdle to widely adopt this promising technology. This paper introduces a secure and privacy-aware fair billing framework for OLEV on the move through the charging plates installed under the road. We first propose two extreme lightweight mutual authentication mechanisms, a direct authentication and a hash chain-based authentication between vehicles and the charging plates that can be used for different vehicular speeds on the road. Second we propose a secure and privacy-aware wireless power transfer on move for the vehicles with bidirectional auditability guarantee by leveraging game-theoretic approach. Each charging plate transfers a fixed amount of energy to the vehicle and bills the vehicle in a privacy-aware way accordingly. Our protocol guarantees secure, privacy-aware, and fair billing mechanism for the OLEVs while receiving electric power from the road. Moreover our proposed framework can play a vital role in eliminating the security and privacy challenges in the deployment of power transfer technology to the OLEVs.

cs.CR↗