Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks.

The Time-Slotted Channel Hopping (TSCH) protocol is known for its suitability in highly reliable applications within industrial wireless sensor networks. One of the most significant challenges in TSCH is determining a schedule with a minimal slotframe size that can meet the required throughput for a heterogeneous network. We proposed a Priority-based Customized Differential Evolution (PCDE) algorithm based on the determination of a collision- and interference-free transmission graph. Our schedule can encompass sensors with different data rates in the given slotframe size. This study presents a comprehensive performance evaluation of our proposed algorithm and compares the results to the Traffic-Aware Scheduling Algorithm (TASA). Sufficient simulations were performed to evaluate different metrics such as the slotframe size, throughput, delay, time complexity, and Packet Delivery Ratio (PDR) to prove that our approach achieves a significant result compared with this method.

Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks.

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics.

Perceived barriers and solutions identified by healthcare professionals in utilizing web-based reminiscence therapy to support dementia care during the pandemic.

This paper presents findings from a qualitative study conducted in Ontario, Canada, exploring healthcare professionals' perceptions of barriers and solutions for implementing Web-Based Reminiscence Therapy (WBRT) in an institutionalized settings for dementia care during the COVID-19 pandemic. The study identified five major barriers, including the lack of on-boarding/educational training, need for technology availability and technical support, limited attention span of persons with dementia (PWD), availability of multi-sensory features, and time constraints due to staff workload. Seven major themes emerged related to proposed solutions/suggestions: (1) involving younger generations, (2) focusing on technology training, (3) integrating with other digital platforms, (4) adding narratives/descriptions to recollect memories, (5) ensuring accessibility, (6) adding QR codes for retrieving information, and (7) combining digital/traditional reminiscence methods. These findings provide valuable insights for implementing WBRT to facilitate dementia care and for the future refinement of its application.

Healthcare professionals' perception of using a web-based reminiscence therapy to support person with dementia during the COVID-19 pandemic.

BACKGROUND: Reminiscence therapy (RT) is the most common non-pharmacological treatment for dementia care. The therapy stimulates the senses to evoke memories having the potential to reduce Behavioral and Psychological Symptoms of Dementia (BPSD). Digital RT, such as web-based reminiscence therapy (WBRT), has the potential to support dementia care and reduce the caregiving burden. AIMS: This study aimed to explore healthcare professionals (HCPs) perceptions of utilizing WBRT in institutionalized settings to support persons with dementia during the COVID-19 pandemic. METHODS: A qualitative phenomenological descriptive study was adopted and guided by Graham's Knowledge to Action framework. Online training on the use of WBRT was conducted, followed by interviews with HCPs. RESULTS: Four major themes were identified on the potential use of WBRT in dementia care, including usability and efficacy, impact on caregiving, capability of reducing BPSD, and. feasibility during COVID-19 social distancing. DISCUSSION: This study recognized the potential use of WBRT to support the person with dementia during the pandemic in institutionalized settings. CONCLUSION: The knowledge generated from this study will guide the future application of WBRT to support dementia care in diverse healthcare settings.