Effectiveness of offloading interventions for people with diabetes-related foot ulcers: A systematic review and meta-analysis.

BACKGROUND: Offloading treatment is crucial to heal diabetes-related foot ulcers (DFU). This systematic review aimed to assess the effectiveness of offloading interventions for people with DFU. METHODS: We searched PubMed, EMBASE, Cochrane databases, and trials registries for all studies relating to offloading interventions in people with DFU to address 14 clinical question comparisons. Outcomes included ulcers healed, plantar pressure, weight-bearing activity, adherence, new lesions, falls, infections, amputations, quality of life, costs, cost-effectiveness, balance, and sustained healing. Included controlled studies were independently assessed for risk of bias and had key data extracted. Meta-analyses were performed when outcome data from studies could be pooled. Evidence statements were developed using the GRADE approach when outcome data existed. RESULTS: From 19,923 studies screened, 194 eligible studies were identified (47 controlled, 147 non-controlled), 35 meta-analyses performed, and 128 evidence statements developed. We found non-removable offloading devices likely increase ulcers healed compared to removable offloading devices (risk ratio [RR] 1.24, 95% CI 1.09-1.41; N = 14, n = 1083), and may increase adherence, cost-effectiveness and decrease infections, but may increase new lesions. Removable knee-high offloading devices may make little difference to ulcers healed compared to removable ankle-high offloading devices (RR 1.00, 0.86-1.16; N = 6, n = 439), but may decrease plantar pressure and adherence. Any offloading device may increase ulcers healed (RR 1.39, 0.89-2.18; N = 5, n = 235) and cost-effectiveness compared to therapeutic footwear and may decrease plantar pressure and infections. Digital flexor tenotomies with offloading devices likely increase ulcers healed (RR 2.43, 1.05-5.59; N = 1, n = 16) and sustained healing compared to devices alone, and may decrease plantar pressure and infections, but may increase new transfer lesions. Achilles tendon lengthening with offloading devices likely increase ulcers healed (RR 1.10, 0.97-1.27; N = 1, n = 64) and sustained healing compared to devices alone, but likely increase new heel ulcers. CONCLUSIONS: Non-removable offloading devices are likely superior to all other offloading interventions to heal most plantar DFU. Digital flexor tenotomies and Achilles tendon lengthening in combination with offloading devices are likely superior for some specific plantar DFU locations. Otherwise, any offloading device is probably superior to therapeutic footwear and other non-surgical offloading interventions to heal most plantar DFU. However, all these interventions have low-to-moderate certainty of evidence supporting their outcomes and more high-quality trials are needed to improve our certainty for the effectiveness of most offloading interventions.

Guidelines on offloading foot ulcers in persons with diabetes (IWGDF 2023 update).

AIMS: Offloading mechanical tissue stress is arguably the most important of multiple interventions needed to heal diabetes-related foot ulcers. This is the 2023 International Working Group on the Diabetic Foot (IWGDF) evidence-based guideline on offloading interventions to promote healing of foot ulcers in persons with diabetes. It serves as an update of the 2019 IWGDF guideline. MATERIALS AND METHODS: We followed the GRADE approach by devising clinical questions and important outcomes in the PICO (Patient-Intervention-Control-Outcome) format, undertaking a systematic review and meta-analyses, developing summary of judgement tables and writing recommendations and rationales for each question. Each recommendation is based on the evidence found in the systematic review, expert opinion where evidence was not available, and a careful weighing of GRADE summary of judgement items including desirable and undesirable effects, certainty of evidence, patient values, resources required, cost effectiveness, equity, feasibility, and acceptability. RESULTS: For healing a neuropathic plantar forefoot or midfoot ulcer in a person with diabetes, use a non-removable knee-high offloading device as the first-choice offloading intervention. If contraindications or patient intolerance to non-removable offloading exist, consider using a removable knee-high or ankle-high offloading device as the second-choice offloading intervention. If no offloading devices are available, consider using appropriately fitting footwear combined with felted foam as the third-choice offloading intervention. If such a non-surgical offloading treatment fails to heal a plantar forefoot ulcer, consider an Achilles tendon lengthening, metatarsal head resection, joint arthroplasty, or metatarsal osteotomy. For healing a neuropathic plantar or apex lesser digit ulcer secondary to flexibile toe deformity, use digital flexor tendon tenotomy. For healing rearfoot, non-plantar or ulcers complicated with infection or ischaemia, further recommendations have been outlined. All recommendations have been summarised in an offloading clinical pathway to help facilitate the implementation of this guideline into clinical practice. CONCLUSION: These offloading guideline recommendations should help healthcare professionals provide the best care and outcomes for persons with diabetes-related foot ulcers and reduce the person's risk of infection, hospitalisation and amputation.

Offloading and adherence through technological advancements: Modern approaches for better foot care in diabetes.

OBJECTIVE: This manuscript aims to provide a review and synthesis of contemporary advancements in footwear, sensor technology for remote monitoring, and digital health, with a focus on improving offloading and measuring and enhancing adherence to offloading in diabetic foot care. METHODS: A narrative literature review was conducted by sourcing peer-reviewed articles, clinical studies, and technological innovations. This paper includes a review of various strategies, from specifically designed footwear, smart insoles and boots to using digital health interventions, which aim to offload plantar pressure and help prevent and manage wounds more effectively by improving the adherence to such offloading. RESULTS: In-house specially made footwear, sensor technologies remotely measuring pressure and weight-bearing activity, exemplified for example, through applications like smart insoles and SmartBoot, and other digital health technologies, show promise in improving offloading and changing patient behaviour towards improving adherence to offloading and facilitating personalised care. This paper introduces the concept of gamification and emotive visual indicators as novel methods to enhance patient engagement. It further discusses the transformative role of digital health technologies in the modern era. CONCLUSIONS: The integration of technology with footwear and offloading devices offers unparallelled opportunities for improving diabetic foot disease management not only through better offloading but also through improved adherence to offloading. These advancements allow healthcare providers to personalise treatment plans more effectively, thereby promising a major improvement in patient outcomes in diabetic foot ulcer healing and prevention.

Intention offloading: Domain-general versus task-specific confidence signals.

Intention offloading refers to the use of external reminders to help remember delayed intentions (e.g., setting an alert to help you remember when you need to take your medication). Research has found that metacognitive processes influence offloading such that individual differences in confidence predict individual differences in offloading regardless of objective cognitive ability. The current study investigated the cross-domain organization of this relationship. Participants performed two perceptual discrimination tasks where objective accuracy was equalized using a staircase procedure. In a memory task, two measures of intention offloading were collected, (1) the overall likelihood of setting reminders, and (2) the bias in reminder-setting compared to the optimal strategy. It was found that perceptual confidence was associated with the first measure but not the second. It is shown that this is because individual differences in perceptual confidence capture meaningful differences in objective ability despite the staircase procedure. These findings indicate that intention offloading is influenced by both domain-general and task-specific metacognitive signals. They also show that even when task performance is equalized via staircasing, individual differences in confidence cannot be considered a pure measure of metacognitive bias.

Perseveration on cognitive strategies.

To acquire and process information, performers can frequently rely on both internal and extended cognitive strategies. However, after becoming acquainted with two strategies, performers in previous studies exhibited a pronounced behavioral preference for just one strategy, which we refer to as perseveration. What is the origin of such perseveration? Previous research suggests that a prime reason for cognitive strategy choice is performance: Perseveration could reflect the preference for a superior strategy as determined by accurately monitoring each strategy's performance. However, following our preregistered hypotheses, we conjectured that perseveration persisted even if the available strategies featured similar performances. Such persisting perseveration could be reasonable if costs related to decision making, performance monitoring, and strategy switching would be additionally taken into account on top of isolated strategy performances. Here, we used a calibration procedure to equalize performances of strategies as far as possible and tested whether perseveration persisted. In Experiment 1, performance adjustment of strategies succeeded in equating accuracy but not speed. Many participants perseverated on the faster strategy. In Experiment 2, calibration succeeded regarding both accuracy and speed. No substantial perseveration was detected, and residual perseveration was conceivably related to metacognitive performance evaluations. We conclude that perseveration on cognitive strategies is frequently rooted in performance: Performers willingly use multiple strategies for the same task if performance differences appear sufficiently small. Surprisingly, other possible reasons for perseveration like effort or switch cost avoidance, mental challenge seeking, satisficing, or episodic retrieval of previous stimulus-strategy-bindings, were less relevant in the present study.

The saving enhanced memory effect can be observed when only a subset of items are saved.

Saving one list of words, such as on a computer or by writing them down, can improve a person's ability to learn and remember a second list of words that are not saved. This phenomenon, known as the saving enhanced memory effect, is typically observed by comparing the recall of nonsaved items when other items are saved versus when they are not saved. In past research, the effect has been shown to occur when participants save an entire list before learning a new list. In the current research, we examined whether the effect can be observed when participants save a subset of items within a single list. The results of two experiments confirmed that partial saving can lead to a saving enhanced memory effect, with the effect observed regardless of whether participants saved items by clicking a button on the computer or writing them out by hand. The effect was observed on an item-specific cued-recall test (Experiment 1) as well as a free recall test that did not control the order of output (Experiment 2). However, the effect size did vary as a function of how participants attempted to recall the items on the final test. Specifically, participants who initiated their output by recalling nonsaved items exhibited a significantly larger saving enhanced memory effect than those who initiated their output by reproducing saved items. Together, these findings expand our understanding of the saving enhanced memory effect and shine new light on the impacts of cognitive offloading on human memory.

Is precrastination related to updating and inhibition aspects of executive function?

ABSTRACTPrecrastination is the act of completing a task as soon as possible even at the expense of extra effort. Past research has suggested that individuals precrastinate due to a desire to reduce their cognitive load, also known as the cognitive load-reduction (CLEAR) hypothesis [VonderHaar, R. L., McBride, D. M., & Rosenbaum, D. A. (2019). Task order choices in cognitive and perceptual-motor tasks: The cognitive-load-reduction (CLEAR) hypothesis. Attention, Perception, & Psychophysics, 81(7), 2517-2525. https://doi.org/10.3758/s13414-019-01754-z]. This idea stems from the notion that it is taxing to hold intentions in working memory and completing a task as soon as possible releases cognitive resources for other tasks. Based on this hypothesis, we predicted that aspects of executive function may play a role in precrastination. We tested this prediction using a box-moving task developed in a previous study to measure precrastination. We also incorporated tasks measuring updating and inhibition aspects of executive function: the Stroop interference (both experiments) and Simon tasks (Experiment 2) to measure inhibition and the 2-Back memory task (Experiment 1) to measure updating. We found that the majority of participants precrastinated significantly throughout the box-moving task trials, consistent with results from past studies. However, no relation was found between the executive function tasks and rates of precrastination. These results may be due to the automaticity of precrastination when cognitive resources are limited.

UAV-Assisted Mobile Edge Computing: Dynamic Trajectory Design and Resource Allocation.

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design.

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey.

Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments.

In a device-to-device (D2D) caching system that utilizes a device's available storage space as a content cache, a device called a helper can provide content requested by neighboring devices, thereby reducing the burden on the wireless network. To enhance the efficiency of a limited-size cache, one can consider not only macro caching, which is content-based caching based on content popularity, but also micro caching, which is chunk-based sequential prefetching and stores content chunks slightly behind the one that a nearby device is currently viewing. If the content in a cache can be updated intermittently even during peak hours, the helper can improve the hit ratio by performing micro caching, which stores chunks that are expected to be requested by nearby devices in the near future. In this paper, we discuss the performance and effectiveness of micro D2D caching when there are multiple operators, the helpers can communicate with the devices of other operators, and the operators are under a low load independently of each other. We also discuss the ratio of micro caching in the cache area when the cache space is divided into macro and micro cache areas. Good performance can be achieved by using micro D2D caching in conjunction with macro D2D caching when macro caching alone does not provide sufficient performance, when users are likely to continue viewing the content they are currently viewing, when the content update cycle for the cache is short and a sufficient number of chunks can be updated for micro caching, and when there are multiple operators in the region.

Towards a Remote Patient Monitoring Platform for Comprehensive Risk Evaluations for People with Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stays and amputation. We suggest a remote monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, characteristics of the wound, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10); categorized as low, medium, and high risk for unfavorable outcomes; and visually represented using color-coded radar plots. The initial results of the case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms.

The Stress of Measuring Plantar Tissue Stress in People with Diabetes-Related Foot Ulcers: Biomechanical and Feasibility Findings from Two Prospective Cohort Studies.

Reducing high mechanical stress is imperative to heal diabetes-related foot ulcers. We explored the association of cumulative plantar tissue stress (CPTS) and plantar foot ulcer healing, and the feasibility of measuring CPTS, in two prospective cohort studies (Australia (AU) and The Netherlands (NL)). Both studies used multiple sensors to measure factors to determine CPTS: plantar pressures, weight-bearing activities, and adherence to offloading treatments, with thermal stress response also measured to estimate shear stress in the AU-study. The primary outcome was ulcer healing at 12 weeks. Twenty-five participants were recruited: 13 in the AU-study and 12 in the NL-study. CPTS data were complete for five participants (38%) at baseline and one (8%) during follow-up in the AU-study, and one (8%) at baseline and zero (0%) during follow-up in the NL-study. Reasons for low completion at baseline were technical issues (AU-study: 31%, NL-study: 50%), non-adherent participants (15% and 8%) or combinations (15% and 33%); and at follow-up refusal of participants (62% and 25%). These underpowered findings showed that CPTS was non-significantly lower in people who healed compared with non-healed people (457 [117; 727], 679 [312; 1327] MPa·s/day). Current feasibility of CPTS seems low, given technical challenges and non-adherence, which may reflect the burden of treating diabetes-related foot ulcers.

Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks.

With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme.

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.

Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks.

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

A realist evaluation of devices used for the prevention of heel pressure ulcers: An ethnographic study of clinical practice.

BACKGROUND: It is known that heel offloading devices are widely used in clinical practice for the prevention of heel pressure ulcers, even though there is a lack of robust, good quality evidence to inform their use. OBJECTIVE: To explore how and why heel offloading devices are used (or not used) and reasoning behind their use in population at high risk of developing heel pressure ulcers. METHODS: An ethnographic study was conducted as part of a realist evaluation in three orthopaedic wards in a large English hospital. Twelve observations took place, with 49 h and 35 min of patient care observed. A total of 32 patients were observed and 19 members of the nursing team were interviewed and in-depth interviews with the three ward managers were conducted. RESULTS: Although the focus of the study was on offloading devices, constant low pressure heel specific devices were also observed in use for pressure ulcer prevention, whilst offloading devices were perceived to be for higher risk patients or those already with a heel pressure ulcer. Nursing staff viewed leadership from the ward manager and the influence of the Tissue Viability Nurse Specialists as key mechanisms for the proactive use of devices. CONCLUSIONS: This study informs trial design as it has identified that a controlled clinical trial of both types of heel specific devices is required to inform evidence-based practice. Involving the ward managers and Tissue Viability Nurse Specialists during set up phase for clinical equipoise could improve recruitment. Tweetable abstract How, for whom, and in what circumstances do devices work to prevent heel pressure ulcers? Observations of clinical practice.