In this work, we propose a reliable, power efficient and high throughput routing protocol for Wireless Body Area Networks (WBANs). We use multi-hop topology to achieve minimum energy consumption and longer network lifetime. We propose a cost function to select parent node or forwarder. Proposed cost function selects a parent node which has high residual energy and minimum distance to sink. Residual energy parameter balances the energy consumption among the sensor nodes while distance parameter ensures successful packet delivery to sink. Simulation results show that our proposed protocol maximize the network stability period and nodes stay alive for longer period. Longer stability period contributes high packet delivery to sink which is major interest for continuous patient monitoring.

SIMPLE: Stable Increased-Throughput Multi-hop Protocol for Link Efficiency in Wireless Body Area Networks

Underwater wireless communications can be carried out through acoustic, radio frequency (RF), and optical waves. Compared to its bandwidth limited acoustic and RF counterparts, underwater optical wireless communications (UOWCs) can support higher data rates at low latency levels. However, severe aquatic channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great challenges for UOWCs and significantly reduce the attainable communication ranges, which necessitates efficient networking and localization solutions. Therefore, we provide a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes: 1) Potential network architectures; 2) Physical layer issues including propagation characteristics, channel modeling, and modulation techniques 3) Data link layer problems covering link configurations, link budgets, performance metrics, and multiple access schemes; 4) Network layer topics containing relaying techniques and potential routing algorithms; 5) Transport layer subjects such as connectivity, reliability, flow and congestion control; 6) Application layer goals and state-of-the-art UOWN applications, and 7) Localization and its impacts on UOWN layers. Finally, we outline the open research challenges and point out the future directions for underwater optical wireless communications, networking, and localization research.

Underwater Optical Wireless Communications, Networking, and Localization: A Survey

ICT for socio-economic development: A citizens’ perspective

Co-LAEEBA

Purpose




The purpose of this paper is to offer insights regarding the consequences of relationship conflict among employees in terms of their task performance, contextual performance and turnover intentions. Utilizing a resource depletion approach in the backdrop of Conservation of Resources theory, it is hypothesized that the three-dimension burnout (exhaustion, cynicism and interpersonal strain) play a mediating role in influencing the linkages between relationship conflict and outcomes.




Design/methodology/approach




Data were collected from 306 telecom engineers and their supervisors and analyzed using structural equation modeling to test the interrelationships among the study constructs.




Findings




Results indicated that relationship conflict is negatively related to task performance and contextual performance and positively relates to turnover intentions, and that the three dimensions of job burnout, i.e. exhaustion, cynicism and interpersonal strain at work, distinctively mediate the linkages between relationship conflict, task and contextual performance and turnover intentions.




Originality/value




This study explores how relationship conflict transmits its effect to job performance and turnover intentions through mediation of burnout. All in all, the findings have important practical as well as theoretical implications for managers and academicians alike.

Examining the linkages between relationship conflict, performance and turnover intentions: Role of job burnout as a mediator

In this paper, we first presented an analytically discussion about energy efficiency of Medium Access Control (MAC) protocols for Wireless Body Area Sensor Networks (WBASNs). For this purpose, different energy efficient MAC protocols with their respective energy optimization techniques; Low Power Listening (LPL), Scheduled Contention and Time Division Multiple Access (TDMA), are elaborated. We also analytically compared path loss models for In-body, On-body and Off-body communications in WBASNs. These three path loss scenarios are simulated in MATLAB and results shown that path loss is more in In-body communication because of less energy level to take care of tissues and organs located inside human body. Secondly, power model for WBASNs of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and beacon mode is also presented. MATLAB simulations results shown that power of CSMA/CA mode is less as compared to beacon mode. Finally, we suggested that hybrid mode is more useful to achieve optimization in power consumption, which consequently results in high energy efficiency.

http://textroad.com/pdf/JBASR/J.%20Basic.%20Appl.%20Sci.%20Res.,%203(4)770-781,%202013.pdf

Energy Efficient MAC Protocols in Wireless Body Area Sensor Networks A Survey

Telecom sector of Pakistan

Advances in wireless communication, system on chip and low power sensor nodes allowed realization of Wireless Body Area Network (WBAN). WBAN comprised of tiny sensors, which collect information of patient's vital signs and provide a real time feedback. In addition, WBAN also supports many applications including Ubiquitous HealthCare (UHC), entertainment, gaming, military, etc. UHC is required by elderly people to facilitate them with instant monitoring anywhere they move around. In this paper, different standards used in WBAN were also discussed briefly. Path loss in WBAN and its impact on communication was presented with the help of simulations, which were performed for different models of In-Body communication and different factors (such as, attenuation, frequency, distance etc) influencing path loss in On-Body communications.

Ubiquitous HealthCare in Wireless Body Area Networks - A Survey

Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.

/pdf/on-improved-3d-cnn-based-binary-and-multiclass-3ha9ruc7.pdf

On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods

Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.

Mustafa Shakir

Papers

Energy Efficient MAC Protocols in Wireless Body Area Sensor Networks A Survey

Telecom sector of Pakistan

Ubiquitous HealthCare in Wireless Body Area Networks - A Survey

On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods

An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy