Browsing by Author "Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]"

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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
A Research On Feasibility Of Intra Vehicle
(Publisher COMSATS University Islambad Lahore Campus, 2018) Naeem Mirza,; FA13-MSEE-016; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]
In Intra Vehicular Wireless Sensor Network (IVWSN), Engine Control Units (ECUs) gathers information about the vehicle from the sensors over wireless channels. This information is then transferred to On-Board Unit (OBU) through Controller Area Network (CAN) in order to monitor and maintain vehicular operations. CAN bus is widely used wired media for communication between ECU and sensors by several car manufacturers but it requires very careful sensor deployment and wiring. In this research thesis, we have achieved low cost and energy efficient communication between (1) sensor nodes and ECU using CAN bus and (2) OBU and ECU within Vehicular Ad Hoc Network (VANET) using Bluetooth low energy (BLE) CC2540/CC2541 modules that can be readily employed in densely urban environment for wireless inter vehicular (up to 50m range) as well as wireless intra vehicular (up to 10m range) communication. The BLE system was invented for the purpose of transmitting small packets of data at once, while consuming less power than Basic Rate & Enhanced Data Rate (BR & EDR) and IEEE 802.15.4 standard based ZigBee devices. A typical communication scenario involves either a Peripheral/ Central or Broadcaster/ Observer device pair. For our tests, we have chosen the Peripheral/ Central modes as they provide greater flexibility to configure application parameters and allow bidirectional communication. We have included comparison between BLE and CAN bus for communication between (1) ECUs and sensors and (2) ECU and OBU, in terms of energy efficiency, throughput, latency and coverage area. Furthermore, wireless communication between ECUs and sensors had been a
Human Activity Recognition System For Long Term
(Publisher COMSATS University Islambad Lahore Campus, 2020) Shan E Ali,; SP18-REE-026; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]; LHR TP 6444
The study of Human Activity Recognition (HAR) for Long Term Health Monitoring (LTHM) has gained significant importance for its wide range of applications. These applications range from sports and rehabilitation sciences to assisted living for older people. In addition to that LTHM is an efficient solution for the prevention of lifestyle diseases like stroke, heart failure, and various health problems that occur due to prolonged inactivity. With the increased availability of accelerometer sensors embedded in mobile phones, we can efficiently explore the Activities of Daily Living (ADLs) of an individual. This research aims to develop a LTHM system for evaluating ADLs of a person using a mobile phone-based accelerometer sensor and the ‘MyNeuroHealth’ application. Data collected in an unconstrained environment by various individuals throughout the day to create templates of ADLs. Collected data is prepared and preprocessed by assigning hourly labels to the ADLs, encoding categorical values and random sampling of data. This data is used for training the machine learning model and for classifying activities according to their energy expenditure or user exhaustion levels. Collected dataset further extended to daily, weekly, and monthly basis to provide long-term health profiling (LTHP). 23 types of basic, complex and transitional activities were evaluated for each day. The results show that an Artificial Neural Network (ANN) can efficiently identify and detect ADLs with more than 90% accuracy. Person independent ADLs templates for weeks 1, 2, 3 and 4 achieved an accuracy of 89, 96, 93 and 89 percent correspondingly. On the other hand, person dependent ADLs templates from various walks of life achieved on average 94% accuracy. Person independent ADLs templates for weeks 1, 2, 3 and 4 achieved an accuracy of 89%, 96%, 93% and 89% percent correspondingly.