List of Titles

A survey on big multimedia data processing and management in smart cities

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2019
Type: Text , Journal article
Relation: ACM computing surveys Vol. 52, no. 3 (2019), p. 1-29
Full Text: false
Reviewed:
Description: Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.

A survey on representation learning efforts in cybersecurity domain

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2020
Type: Text , Journal article
Relation: ACM computing surveys Vol. 52, no. 6 (2020), p. 1-28
Full Text: false
Reviewed:
Description: In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.

An improved congestion-controlled routing protocol for IoT applications in extreme environments

- Adil, Muhammad, Usman, Muhammad, Jan, Mian, Abulkasim, Hussein, Farouk, Ahmed, Jin, Zhanpeng

Authors: Adil, Muhammad , Usman, Muhammad , Jan, Mian , Abulkasim, Hussein , Farouk, Ahmed , Jin, Zhanpeng
Date: 2024
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 11, no. 3 (2024), p. 3757-3767
Full Text: false
Reviewed:
Description: The Internet of Things (IoT) has shown its presence in applications that require monitoring extreme environments, such as wildfires, military operations, and coastal areas, among others. In these applications, the IoT nodes are deployed in hazardous terrains where humanistic access is hard or not possible. Hence, to ensure reliable data transmission in these applications, novel routing protocols need to be designed due to the multihop nature of communication possessed by the deployed nodes. Currently, most of the routing protocols utilized by IoT nodes follow traditional approaches, which creates congestion and contention in the network. As a result, the network performance is degraded in terms of various communication metrics. To address this problem and improve the communication statistics in extreme environments, we propose a deep- Q -learning-enable-destination-sequenced distance-vector (DQL-DSDV) framework. DQL-DSDV focuses on selecting the next hop during communication. Initially, the DSDV protocol updates routing information for connected nodes. This information is subsequently utilized by the deep- Q -learning (DQL) algorithm to compute the next hop count. This computation is based on reward functions, known as Q-values, which are conceptualized as the distance between connected nodes by taking into account the traffic flow. These distinguishing operational features of DQL and DSDV ensure that DQL-DSDV minimizes the packet lost ratio, congestion, end-to-end delay, and communication cost with improved Quality of Service (QoS). During simulations, we observed significant improvement in these performance metrics, in the presence of the existing schemes. Despite that, we checked the computation complexity of the proposed approach with existing protocols, which demonstrated noteworthy outcomes just like the other metrics. © 2014 IEEE.

PAWN: a payload‐based mutual authentication scheme for wireless sensor networks

- Jan, Mian, Nanda, Priyadarsi, Usman, Muhammad, He, Xiangjian

Authors: Jan, Mian , Nanda, Priyadarsi , Usman, Muhammad , He, Xiangjian
Date: 2017
Type: Text , Journal article
Relation: Concurrency and computation Vol. 29, no. 17 (2017), p. e3986-n/a
Full Text: false
Reviewed:
Description: Summary Wireless sensor networks (WSNs) consist of resource‐starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery‐powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy‐scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource‐constrained nature of sensor nodes, inaccessible human terrains, and error‐prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload‐based mutual authentication scheme for a cluster‐based hierarchical WSN. The proposed scheme, also known as payload‐based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes.

Performance evaluation of high definition video streaming over mobile ad hoc networks

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Alam, Muhammad

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Alam, Muhammad
Date: 2018
Type: Text , Journal article
Relation: Signal processing Vol. 148, no. (2018), p. 303-313
Full Text: false
Reviewed:
Description: A unique combinational approach of QoS and QoE for multimedia traffic in MANETs is proposed in this paper.•A feedback-based multi-path transmission strategy is adopted to minimize the data loss during HD video streaming.•An EC technique based on multi-threading-based parallel processing is used to recover the lost video frames.•We use multiple objective evaluation metrics to test the performance of the EC technique on recovered video frames.•Subjective analysis is combined with objective evaluation using subjective scores and statistical analysis to maintain QoE. Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data.

SAMS: A seamless and authorized multimedia streaming framework for wmsn-based iomt

- Jan, Mian, Usman, Muhammad, He, Xiangjian, Ur Rehman, Ateeq

Authors: Jan, Mian , Usman, Muhammad , He, Xiangjian , Ur Rehman, Ateeq
Date: 2019
Type: Text , Journal article
Relation: IEEE internet of things journal Vol. 6, no. 2 (2019), p. 1576-1583
Full Text: false
Reviewed:
Description: An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.

Time-frequency filter bank: A simple approach for audio and music separation

- Yang, Ning, Usman, Muhammad, He, Xiangjian, Jan, Mian, Zhang, Liming

Authors: Yang, Ning , Usman, Muhammad , He, Xiangjian , Jan, Mian , Zhang, Liming
Date: 2017
Type: Text , Journal article
Relation: IEEE access Vol. 5, no. (2017), p. 27114-27125
Full Text: false
Reviewed:
Description: Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.

Showing items 1 - 7 of 7