Mubashar Mushtaq

Bio: Mubashar Mushtaq is an academic researcher from Quaid-i-Azam University. The author has contributed to research in topics: Quality of service & Video quality. The author has an hindex of 9, co-authored 27 publications receiving 263 citations. Previous affiliations of Mubashar Mushtaq include Forman Christian College & University of Bordeaux.

Smooth Video Delivery for SVC Based Media Streaming Over P2P Networks

Abstract: In this paper, we leverage the characteristics of scalable video coding (SVC) and P2P networks in order to propose an efficient and adaptive video streaming mechanism. We aim to maintain smooth content delivery of certain acceptable QoS (quality of service) for the received video. The proposed system is composed of an adaptive streaming mechanism which is based on the efficient SVC content scheduling to coordinate among receiver and sender peers for smooth media streaming. Performance evaluation of our system conducted using NS-2 simulation demonstrates a significant improvement in the received video quality compared to conventional streaming approaches.

Adaptive Packet Video Streaming Over P2P Networks Using Active Measurements

Abstract: In this paper we consider the problem of real-time streaming of IP packet video over Peer-to-Peer networks (P2P) from multiple senders to a single receiver. P2P networks are characterized by a potentially large and highly dynamic population of hosts that join and leave the network frequently. We present the design and evaluation of a quality adaptation streaming mechanism in a multi-source streaming to a single receiver. Multimedia streaming is a real time application so, the main challenges in the design of this mechanism are (1) selection of senders peers nodes; (2) stream switching among the peers; (3) optimizing video quality by active measurements of links; and (4) enhancing the overall Quality of Service (QoS). Our key technique to provide quality adaptation is based on active measurements of network links and selection of sender peers to enhance the overall throughput. We used video traffic organized as MDC (Multiple Description Coding) layers, which provides high error resilient. Our simulations results using ns2 show that our solution allows to efficiently utilize available network bandwidth of sending peers and allow maximizing streaming qualities at the reception peer.

CommuniMents: A Framework for Detecting Community Based Sentiments for Events

Abstract: Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.

Adaptive packet video streaming over P2P networks

Abstract: Our concern in this paper is the real-time streaming of IP packet video. We consider the scenario where we have multiple senders that stream the same video to single receiver over Peer-to-Peer networks. We propose an adaptive scheme for video streaming over P2P network that encompasses an efficient mechanism for the selection and the maintenance of sender peers nodes. Furthermore, we perform active measurements of links between the receiver and stream senders in order to optimize the overall video quality. Finally, the evaluation conducted over ns2 simulations shows that our solution allows to efficiently utilize available network bandwidth of sending peers and allow maximizing streaming qualities at the reception peer.

Trustworthy Electronic Voting Using Adjusted Blockchain Technology

Abstract: The electronic voting has emerged over time as a replacement to the paper-based voting to reduce the redundancies and inconsistencies. The historical perspective presented in the last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time. This paper suggests a framework by using effective hashing techniques to ensure the security of the data. The concept of block creation and block sealing is introduced in this paper. The introduction of a block sealing concept helps in making the blockchain adjustable to meet the need of the polling process. The use of consortium blockchain is suggested, which ensures that the blockchain is owned by a governing body (e.g., election commission), and no unauthorized access can be made from outside. The framework proposed in this paper discusses the effectiveness of the polling process, hashing algorithms’ utility, block creation and sealing, data accumulation, and result declaration by using the adjustable blockchain method. This paper claims to apprehend the security and data management challenges in blockchain and provides an improved manifestation of the electronic voting process.

Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States

Abstract: Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify relevant attributes in the dataset which have a significant impact on classifying a patient’s mental health status. For mental health situations, correlation analysis has been performed in Weka, which involves a dataset of depressive disorder symptoms and situations based on weather conditions, as well as emotion classification based on physiological sensor readings. Pearson’s product moment correlation and other different classification algorithms have been utilized for this analysis. The results show interesting correlations in weather attributes for bipolar patients, as well as in features extracted from physiological data for emotional states.

Video streaming over P2P networks: Challenges and opportunities

Abstract: A robust real-time video communication service over the Internet in a distributed manner is an important challenge, as it influences not only the current Internet structure but also the future Internet evolution. In this context, Peer-to-Peer (P2P) networks are playing an imperative position for providing efficient video transmission over the Internet. Recently, several P2P video transmission systems have been proposed for live video streaming services or video-on-demand services over the Internet. In this paper, we describe and discuss existing video streaming systems over P2P. Efficient (delay tolerant and intolerant) data sharing mechanisms in P2P and current video coding trends are elaborated in detail. Moreover, video streaming solutions (live and on-demand) over P2P from the perspective of tree-based and mesh-based systems are explained. Finally, the conclusion is drawn with key challenges and open issues related to video streaming over P2P.