Browsing by Author "Usama Masood"

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    A Research On Clock-Synchronous Sleep And Wake
    (Publisher COMSATS University Islambad Lahore Campus, 2016) Usama Masood; FA12-MSEE-018; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]; LHR TP 6980
    A Research on Clock-Synchronous Sleep and Wake Scheduling Scheme in Wireless Sensor Networks Network Wireless Sensor Network (WSN) consist of a unique set of resources like on-board battery and wireless communication devices with limited bandwidth. WSN offers a wide range of applications for monitoring space or targets. WSN is capable of performing simple processing tasks like tracking, detection of an event, or classification and may consist of multiple nodes that can process the information and communicate with nearby nodes in real-time for environmental monitoring, event detection, surveillance, object tracking, battlefield situation monitoring, and data collection etc. However, there are certain limitations in deploying WSN efficiently such as in terms of limited power resource for a single node in WSN, limited processing capability and varying network life time. It had been shown that wireless communication to and from sensor nodes consumes significantly more battery power in comparison to power expended in sensing, computation and memory access procedures. One of the possible solutions to this problem is to communicate as sparingly as possible through efficient sleep/wake scheduling for WSN nodes to extend node and network lifetime. A major research issue in WSN is to develop an energy efficient MAC protocol that not only provides increase in network lifetime but also addresses latency. In this research, a new MAC protocol is designed using sleep/wake scheduling for WSN. Though energy consumption in WSN is unavoidable due to communication necessity and for different stages like idle listening, retransmission, channel sensing and overhearing; this proposed protocol will help in decreasing this energy consumption. Energy efficiency and latency of the proposed sleep/wake scheduling scheme is evaluated and compared with the state of the art research. An AEL (Accounting for Energy and Latency) factor is introduced which is the deciding element for active and sleep cycles of the node. This said AEL factor defines the minimum duty cycle among the network nodes and is specified prior to nodes deployment depending upon application requirements. In the proposed protocol, the nodes adjust their duty cycles according to this AEL factor depending upon traffic load, their position and their connectivity in the network. The effect of this AEL factor on energy efficiency and delay is evaluated for different network densities in this research thesis. While incorporating sleep/wake scheduling for energy efficiency, delays are added in the network to route packet from node towards sink. Therefore, it is necessary to address latency for MAC protocol especially for delay constrained applications. This research thesis focuses on sleep/wake scheduling scheme ensuring energy efficiency, decreased latency and increased network lifetime by selecting an appropriate AEL value. The research includes the comparison of the proposed protocol with state of the art research and has shown significant percentage improvements in energy efficiency and delay from S-MAC and Anycast protocol
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    Multi-Pollutant based Hybrid Framework for Green Smart Cities using Machine Learning Techniques.
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Usama Masood; FA21-RCS-013; LHR TP 8676; Dr. Tariq Umer
    Achieving a sustainable environment is one of the most emerging issues discussed in the smart cities concept. Due to the rapid population growth in the world, the concentration of greenhouse gases is increasing day by day. A lot of research studies have focused on different techniques and technologies to reduce environmental pollution. To achieve a sustainable environment, it is important to consider the multi-pollutant factors involve in polluting the environment. In MS thesis, we proposed a multi-pollutants-based intelligent hybrid framework using machine learning that considers the multiple sources of pollution in the city environments. The research emphasizes a comprehensive comparisons of machine learning algorithm for predicting the air quality index in smart cities. In this research. The framework considers the concentration of the pollutants in the environment and will make intelligent predictions on their combined effect on the environment as well as the individual groups based on the similar characteristics of gases concerning the guidelines of WHO. The effect of the concentration of multi-pollutants on air quality and water quality will be analyzed. Machine Learning Techniques including Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, and Long Short Term Memory (LSTM) will be applied to predict air quality index and for the classification of pollutants. The evaluation measure used in this research is Accuracy, Precision, F1 score and Recall to find the accuracy of these models.