Ali Hassan Sodhro

Energy-efficient adaptive transmission power control for wireless body area networks

An important constraint in wireless body area network (WBAN) is to maximise the energy-efficiency of wearable devices due to their limited size and light weight. Two experimental scenarios; ‘right wrist to right hip’ and ‘chest to right hip’ with body posture of walking are considered. It is analyzed through extensive real-time data sets that due to large temporal variations in the wireless channel, a constant transmission power and a typical conventional transmission power control (TPC) methods are not suitable choices for WBAN. To overcome these problems a novel energy-efficient adaptive power control (APC) algorithm is proposed that adaptively adjusts transmission power (TP) level based on the feedback from base station. The main advantages of the proposed algorithm are saving more energy with acceptable packet loss ratio (PLR) and lower complexity in implementation of desired tradeoff between energy savings and link reliability. We adapt, optimise and theoretically analyse the required parameters to enhance the system performance. The proposed algorithm sequentially achieves significant higher energy savings of 40.9%, which is demonstrated by Monte Carlo simulations in MATLAB. However, the only limitation of proposed algorithm is a slightly higher PLR in comparison to conventional TPC such as Gaos and Xiaos methods.

Battery-Friendly？Packet？Transmission？Strategies？for？Wireless？Capsule？Endoscopy

Wireless Capsule Endoscope (WCE) is the high data rate application of wireless body area network (WBAN) which detects patient’s Gastrointestinal (GI) tract after being swallowed. The challenging issue is the limited lifetime of battery in these devices due to high resolution image transmission. We designed Battery-Friendly packet transmission strategies- Lazy and Modified Lazy to extend the lifetime of these devices. Simulation results show that battery life time is extended.

Green and friendly media transmission algorithms for wireless body sensor networks

Video transmission is considered as a quite significant step towards health monitoring of the emergency patients during any critical incident. However, the energy hungry video transmission and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in WBSNs. Therefore, the need arose to conduct research on sustainable, “Green”, i.e., energy-efficient and “Friendly”, i.e., battery-friendly technologies to cater the need of upcoming mobile and portable devices. The main challenge addressed in this research is how to increase the battery lifetime during on-demand Variable Bit Rate (VBR) video transmission from medical video server to base station in WBSNs. In order to overcome this problem, sustainable, Green and Friendly frame transmission algorithms are enunciated i.e. Lazy Algorithm (LA) and Battery-friendly Smoothing Algorithm (BSA) with analytical battery model. The proposed algorithms minimize transmission energy consumption, battery charge consumption and high current profile. These algorithms also prolongs the battery lifetime of those sensor nodes during video transmission. Experimental results demonstrates that BSA outperforms LA to minimize battery drain by improving its lifetime up to 19.8xa0%. However, the LA performs better than BSA in context of transmission energy saving up to 49.49xa0%. Furthermore, a video transmission framework of Remote Medical Education System (RMES) for elderly persons and infants is proposed to provide viable and sustainable battery solutions to serve the community.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).

Energy efficiency comparison between data rate control and transmission power control algorithms for wireless body sensor networks

This article presents comparison between data rate or rate control, that is, video transmission rate control algorithm and transmission power control algorithms for two different cases. First, energy consumption due to high peak variable data rates in video transmission. Second, energy depletion due to high transmission power consumption and dynamic nature of wireless on-body channel. The former one focuses on constant (fixed) transmission power level and variable data rate (“severe” conditions), for example, medical monitoring of the emergency patients. The latter considers variable transmission power level and constant (fixed) data rate (“less severe” conditions), for example, electrocardiography measurement for patients in wireless body sensor networks. Besides, energy efficiency comparison analysis of battery-driven or video transmission rate control algorithm and transmission power control–driven or power control algorithm is presented. Finally, proposed algorithms are analyzed and categorized as energy-efficient and battery-friendly for medical applications in wireless body sensor networks.

Green media-aware medical IoT system

Rapid proliferation in state-of-the art technologies has revolutionized the medical market for providing urgent, effective and economical health facilities to aging society. In this context media (i.e., video) transmission is considered as a quite significant step during first hour of the emergency for presenting a big and better picture of the event. However, the energy hungry media transmission process and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in medical internet of things (MIoT) or internet of medical things (IoMT). So, promoting Green (i.e., energy-efficient) transmission during voluminous and variable bit rate (VBR) video in MIoT is a challenging and crucial problem for researchers and engineers. Therefore, the need arose to conduct research on Green media transmission techniques to cater the need of upcoming wearable healthcare devices. Thus, this research contributes in two distinct ways; first, a novel and sustainable Green Media Transmission Algorithm (GMTA) is proposed, second, a mathematical model and architecture of Green MIoT are designed by considering a 8-min medical media stream named, ‘Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ to minimize transmission energy consumption in media-aware MIoT, and to develop feasible media transmission schedule for sensitive and urgent health information from physian to patients and vice vers through extremely power hungry natured wearable devices. The experimental results demonstrate that proposed GMTA saves energy up to 41%, to serve the community.

Convergence of IoT and product lifecycle management in medical health care

Abstract Emerging trends in Internet of Medical Things (IoMT) or Medical Internet of Things (MIoT), and miniaturized devices with have entirely changed the landscape of the every corner. Main challenges that heterogeneous sensor-enabled devices are facing during the connectivity and convergence with other domains are, first, the information/knowledge sharing and collaboration between several communicating parties such as, from manufacturing engineer to medical expert, then from hospitals/healthcare centers to patients during disease diagnosis and treatment. Second, battery lifecycle and energy management of wearable/portable devices. This paper solves first problem by integrating IoMT with Product Lifecycle Management (PLM), to regulate the information transfer from one entity to another and between devices in an efficient and accurate way. While, second issue is resolved by proposing two, battery recovery-based algorithm (BRA), and joint energy harvesting and duty-cycle optimization-based (JEHDO) algorithm for managing the battery lifecycle and energy of the resource-constrained tiny wearable devices, respectively. Besides, a novel joint IoMT and PLM based framework is proposed for medical healthcare applications. Experimental results reveal that BRA and JEHDO are battery-efficient and energy-efficient respectively.

Energy-Efficiency of Tools and Applications on Internet

Abstract This chapter presents the comparative analysis of the various tools and applications in terms of energy-efficiency. Due to increasing demand of Green technology it is very challenging to minimize the energy consumption of software and hardware components. In addition, the use of applications or software on our computers consumes energy and it also affects the energy drain of several hardware components and system resources. Consequently, running web browsers, media players, file transfer protocols, wired and wireless security protocol applications will utilize the considerable amount of energy. In this remarkable research, we have run different types of experiments which contain the use of several measuring tools. Firstly, joulemeter and powertop (pTop) are used to monitor and calculate the energy drain of hardware and software while running web-based and stand-alone applications with Windows 7 and Linux (i.e. Ubuntu 16.04) operating systems on desktop and laptop computers. Secondly, it is presented that how much energy is consumed by an ordinary citizen on typical things of everyday use on the web.

Medical Quality of Service Optimization Over Internet of Multimedia Things

Abstract This chapter gives an innovative research for streaming of video over the Internet of Multimedia Things (IoMT) – an enrichment to the Internet of Things (IoT). Its primary goal is to support video streaming as a part of the implementation of IoT with Telemedicine and medical quality of service (m-QoS) optimization. The IoT enabled systems cannot be successful if the idea of pervasive connectivity of everything is unable to include ‘multimedia objects’. This problem is analyzed and focused by envisioning the concept of IoT and depicting a motivation concerning the vision of IoMT. Moreover, this chapter proposes three novel algorithms namely, Modified Lazy Video Transmission Algorithm (MLVTA), Online Video Transmission Algorithm (OVTA) and Rate Control Video Transmission Algorithm (RCVTA) for applications such as, Tele-monitoring, Tele-vision news casts and Tele-surgery by considering video streams of pre-recorded (i.e., stored), online and high-definition (HD) videos respectively, through work ahead transmission to optimize m-QoS in terms of standard deviation throughput, peak-to-mean ratio, jitter, average jitter, delay, and average delay over IoMT network.

Mobile edge computing based QoS optimization in medical healthcare applications

Abstract Emerging trends in mobile edge computing for developing the efficient healthcare application such as, remote monitoring of the patients with central electronics clouds (e-Clouds) and their increasing voluminous multimedia have caught the attention of everyone in industry and academia. So, clear visualization, big sensing level, and better quality of service (QoS) is the foremost priority. This paper proposes the window-based Rate Control Algorithm (w-RCA) to optimize the medical quality of service (m-QoS) in the mobile edge computing based healthcare by considering the network parameters for instance, peak-to-mean ratio (PMR), standard deviation (Std.dev), delay and jitter during 8u2009min medical video stream named “Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ transmission over 5u2009G networks. The performance of the proposed w-RCA is evaluated and compared with the conventional battery smoothing algorithm (BSA) and Baseline by using MPEG-4 encoder for optimizing m-QoS at the source or the server side. The experimental results demonstrate that the w-RCA outperforms the BSA and Baseline by optimizing QoS in remote healthcare application i.e., Tele-surgery. Besides, it is observed and analyzed that w-RCA produces better and effective results at small buffer and window sizes unlike BSA and Baseline by adopting large buffer size during QoS optimization.