Acceptable Delay Research Articles

IoT Based Football Player Training Session Simulation

Simulation software is considered as an important step in the development of internet of things (IoT) scenarios and has been the subject of intense research in the past decade. While most previous efforts in simulating these devices have focused on the communication protocols and the sensor node layers issues, a significant aspect remains in precisely measuring some health metrics of football players. Some of the IoT technologies are allowed to be used during the real match or training practice, while most of them were forbidden by the Fédération Internationale de Football Association (FIFA). One promising aspect, is to simulate the health metrics of the players during the training session using the concept of wireless sensor nodes with integrated sensors. The available simulation programs do not support all the required features in the football field. For that, in this paper the TrainPlayer is presented, a health level sensor network simulator which collects the required metrics for the players nodes and can send the data payload using the Message Queuing Telemetry Transport (MQTT) protocol. It developed by utilizing the object-oriented programming features and the graphical user interface in python libraries. The work simulates the movement of the player on the pitch and how the metrics are generated, collected and sent to the server. The collected data is varied during the exercises. This monitoring system helps the coaches to fix the training workload by the team trainer or coach to reach the optimal fitness level. The results show the reliability of the MQTT protocol in message delivery and the acceptable delay time. TrainPlayer’s ability to measure detailed metrics can shed new light on design proper training workload for each player. In the future, the software can be enhanced with additional features to become a complete tool for simulating football players. Index Terms— Football player, IoT, MQTT, Simulation tool, Training.

MEC Server Status Optimization Framework for Energy Efficient MEC Systems by Taking a Deep-Learning Approach

Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination.

A Novel Downhole Communication Strategy Using Loading Wave Conduction on Sucker-Rod Pumping System for Intelligent Lifting

Summary An effective and accurate downhole communication strategy is crucial for the fabrication of an intelligent lifting system for onshore oil wells. Traditional communication approaches based on the wired cable, acoustic wave, vibration wave, or fluid pressure are usually limited by downhole conditions, and issues such as cumbersome implementation, limited communication, and unstable signal modulation are encountered. Herein, a novel downhole communication strategy is proposed using the loading waves in the sucker-rod pumping system (SRPS). The loading wave is altered at the downhole pump at an extremely low frequency, and its significant variation could be captured by the surface load sensor. A controlled valve is installed between the chamber of the pump and the wellbore. The valve opening regulates the pressure in the pump chamber, leading to the generation of the controlled loading waves. The field tests are further carried out and prove the effective coding between the downhole and surface with an acceptable delay (~0.154 seconds for a well with a depth of 1000 m). For the loading wave transmission on the sucker-rod string system, the finite element method is used to solve the theoretical model considering the real circumstances, such as the coupling damping, centering device friction, and stuffing box friction. The impacts of operating parameters of the lifting system, wellbore conditions, and modulation of excitation signal on the communication process are systematically discussed. The transmission evaluation standard, applicable conditions, coding tactic, and potential engineering values are presented for the downhole communication system.

An adaptive genetic algorithm with neighborhood search for integrated O2O takeaway order assignment and delivery optimization by e-bikes with varied compartments

To improve the dining experiences, online-to-offline (O2O) takeaway services with warm-keeping or refrigerated requirements are quickly expanding and becoming popular. However, single-compartment e-bikes are commonly used in takeaway platforms, which can only meet one kind of requirements and may result in an inflexible delivery. Within this context, a new type of e-bikes, namely e-bikes with mixed compartments, is introduced. Thus, warm-keeping and refrigerated orders can be delivered by one bike, namely on one route. This paper considers an integrated order assignment and delivery problem by e-bikes with warm, refrigerated, and mixed compartments (AD-EBM). To solve the problem, we develop a novel integer programming formulation to minimize the total cost by determining order assignments and finding optimal routes, and then some properties of the solutions are provided from the view of mathematics. An algorithm is designed by combining the self-adaptive genetic algorithm with the neighborhood search method (SGA-NS). Numerical experiments are conducted based on simulated different-scale takeaway instances. The experimental results highlight the excellent performance of the SGA-NS and the results are quite encouraging compared with Gurobi solver, SGA, and NS. The results of the model comparison demonstrate that the AD-EBM offers 12.38% total cost savings on average, compared to using only single-compartment e-bikes. A sensitivity analysis is performed to explore the effects of the mixed compartment costs, the customer acceptable delay time, the penalty costs for delays, and the e-bike capacity for the platform’s daily operations. Some management insights are provided to facilitate the O2O takeaway delivery.

Exploring the Acceptable Delay for Elective Treatment of Patients With an Abdominal Aortic Aneurysm: A Reflection During a Pandemic and an Exploratory Analysis

IntroductionThis study sought to determine the rupture risk of asymptomatic abdominal aortic aneurysms (AAAs) undergoing interventions as a function of time to establish a maximal acceptable surgical delay. MethodsA literature review was performed from inception to August 30, 2021, to assess the risk of rupture of aneurysms over time. The analysis was limited to men with asymptomatic AAAs. The data on AAA rupture risk according to diameter and follow-up time were extracted. The acceptable mortality risk for AAA patients as a function of surgical delay was further evaluated. This acceptable mortality risk was based on the acceptable risk of cardiovascular death associated with the accepted delays of coronary revascularization in coronary artery disease populations. Data on estimated surgical delays and risks were extracted using a free web-based software (WebPlotDigitizer) and plotted using Microsoft Excel. ResultsOur study identified minimal evidence as it pertains to AAA rupture risk as a function of surgical delay. The data on rupture risk of AAAs according to diameter and time were extracted from a single review and a single meta-analysis (Figure 1). The acceptable delays of semiurgent and nonurgent invasive treatment for coronary artery disease found in literature are 6 and 12 wks respectively. These acceptable delays are associated with an estimated acceptable cardiovascular mortality risk threshold of 0.47% at 6 and 12 wks. Using this threshold of estimated maximum acceptable risk and the data on the natural history of AAAs found in our review, we found that the acceptable surgical delays for AAAs would be estimated at 13-27 ds for AAAs ≥ 7 cm, 20-42 ds for 6-6.9 cm, and 32-49 ds for 5.5-5.9 cm (Figure 1). ConclusionsThis study identified estimated surgical delays for patients with AAAs based on the acceptable maximum risk. These estimations may be used cautiously to triage patients with asymptomatic AAAs, particularly in the setting of triaging patients during local and global crises.

On-demand airport slot management: tree-structured capacity profile and coadapted fire-break setting and slot allocation

We propose an on-demand airport slot management (ODASM) approach to guide slot fire-break capacity setup and airport slot allocation. The ODASM consists of a tree-structured capacity profile and a coadapted fire-break setting and slot allocation model. The tree-structured capacity profile that is constructed using a decision tree aims to capture various fire-break capacity settings and their corresponding delays. It provides diversified fire-break schemes and delay references in the airport slot allocation process. The coadapted fire-break setting and slot allocation model are able to generate good coadaptation. That is, the fire-breaks are set to adapt to the preferences of airlines' requests to minimise the total displacements, and the airlines' requests are modified to adapt to the fire-breaks to satisfy a predetermined delay level. The approach has been tested at Shanghai Pudong Airport, and the results suggest that the ODASM can generate a moderate level of rescheduling while resulting in the expected delay that does not exceed a given acceptable delay level. It has been demonstrated that the ODASM can offer substantial advantages in contrast to a classic slot allocation method where the capacity are set as the maximum of the declared capacity. Moreover, due to the refined relationship between fire-break configuration and delay, the computation process of slot optimisation only takes a few minutes, which is completely viable for the application. This approach is compatible with the current slot guidelines while showing significant potential to improve the collaboration among stakeholders in slot management.

Receive wireless sensor data through IoT gateway using web client based on border gateway protocol

One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.

ASIC Design and Implementation of Automated Coffee and Tea Brewing System with FPGA Validation

Abstract: This paper presents a pioneering approach to enhance the efficiency of automated coffee and tea brewing systems through the integration of an ASIC-GDSII and FPGA validation. Leveraging the power of 45nm CMOS Technology, the design achieves substantial reductions in area, power consumption, and delay. The Verilog HDL code undergoes meticulous verification using Cadence tools like Genus and Innovus, ensuring optimal design performance. Innovus validates timing constraints, ensuring adherence to acceptable delay parameters. The resulting compact design not only minimizes power consumption but also effectively addresses leakage issues. These comprehensive optimizations meet stringent performance, area, and power requirements, elevating the operational efficiency of automated brewing systems. Furthermore, the control algorithm is synthesized and implemented using Xilinx's ISE Design Suite, followed by validation on SPARTAN 6 FPGA, providing invaluable insights for future synthesis and implementation endeavors.

PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting

Consensus protocols, as crucial components of blockchain technology, play a vital role in ensuring data consistency among distributed nodes. However, the existing voting-based and proof-based consensus protocols encounter scalability issues within the blockchain system. Moreover, most consensus protocols are serialized, which further limits their scalability potential. To address this limitation, parallelization methods have been employed in both types of consensus protocols. Surprisingly, however, novel fusion consensus protocols demonstrate superior scalability compared with these two types but lack the utilization of parallelization techniques. In this paper, we present PnV, an efficient parallel fusion protocol integrating proof-based and voting-based consensus features. It enhances the data structure, consensus process, transaction allocation, and timeout handling mechanisms to enable concurrent block generation by multiple nodes within a consensus round. Experimental results demonstrate that PnV exhibits superior efficiency, excellent scalability, and acceptable delay compared with Proof of Vote (PoV) and BFT-SMART. Moreover, at the system level, the performance of the PnV-based blockchain system optimally surpasses that of the FISCO BCOS platform. Our proposed protocol contributes to advancing blockchain technology by providing a more efficient and practical solution for achieving decentralized consensus in distributed systems.

DRAMS: Double-RIS assisted multihop routing scheme for device-to-device communication

Reconfigurable intelligent surfaces (RISs) is a promising solution for enhancing the performance of multihop wireless communication networks. In this paper, we propose a double-RIS assisted multihop routing scheme for a device-to-device (D2D) communication network. Specifically, the scheme is dependent on the already deployed RISs and users in the surroundings. Besides the RISs, the emphasis of this work is to make more use of the existing intermediate users (IUs), which can act as relays. Hence, the density of RIS deployment in the surroundings can be reduced, which leads to the avoidance of resource wastage. However, we cannot solely depend on the IUs because this implies complete dependence on their availability for relaying and as a result, the aspect of reliability in terms of delay-constrained information transfer cannot be guaranteed. Moreover, the IUs are considered capable of energy harvesting and as a result, they do not waste their own energy in the process of volunteering to act as a relay for other users. Numerical results demonstrate the advantage of the proposed scheme over some existing approaches and lastly, useful insights related to the scheme design are also drawn, where we characterize the maximum acceptable delay at each hop under different set-ups.

Rapid detection of tomato spotted wilt virus by real-time RT-LAMP and in-field application

Tomato spotted wilt virus (TSWV) is considered one of the most threatening viruses worldwide for different economically important agricultural crops. In this scenario, it is important to perform an early detection by laboratory tests to prevent TSWV spread. A rapid and sensitive TSWV detection protocol based on real time reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay was developed in this work, also using cost-effective and simplified sample preparation procedure, to assess the suitability of the RT-LAMP assay in field conditions on tomato and pepper samples. A set of six primers was designed within the nucleotide sequence region coding for the nucleocapsid protein (N) of segment S, targeting a 220-nucleotide sequence. Sensitivity, specificity, accuracy, and in-field application of the real-time RT-LAMP assay were evaluated. The developed real-time RT-LAMP assay proved to be one thousand and one hundred times more sensitive than end-point RT-PCR and real-time RT-PCR methods, respectively, detecting a total of 9.191 × 101 genome copies as minimum target, and no cross-reactivity were detected with other viruses belonging to Tospoviridae and Bromoviridae families used as outgroup. In addition, the in-field application of the assay using the rapid sample preparation gave adequate and reliable results within 60 minutes, with an acceptable reaction delay when compared to canonical RNA extraction. The in-field analyses showed an increase of TSWV-positive samples (37%) detection compared with end-point RT-PCR and real-time RT-PCR (32% and 29%, respectively), particularly on asymptomatic samples, confirming that the real-time RT-LAMP assay can be implemented as a routine test both in-field and laboratory conditions as a rapid and sensitive technique for TSWV detection.

Features of software implementing the prefix search method in cryptographically protected databases

The article addresses the specific considerations associated with the development of software implementing the prefix search method in cryptographically protected databases. This method is a variant of symmetric searchable encryption, which allows search among the encrypted data. The prefix search method allows searching for prefixes among encrypted data without the need for decryption. Such an approach resolves the issue of maintaining data confidentiality stored on remote or cloud servers. However, its usage introduces a set of issues that must be considered during the development of the corresponding software. The paper analyzes the requirements for software that implements the prefix search method, defines the software architecture, and justifies the choice of technologies and tools for software implementation, including ASP.NET, Java, JavaScript, PHP, Python programming languages, MongoDB database management system, and the FastAPI framework. A description of the deployment process of the corresponding software is provided. To assess the performance of the developed software, the well-known Apache JMeter tool for conducting load testing was utilized. The obtained performance evaluations of the proposed solution indicate acceptable time delays in processing relevant data search queries.

43251 Duration of acceptable delay until subsequent full body skin examination, given prior history of skin cancer or lesions suspicious for skin cancer

43251 Duration of acceptable delay until subsequent full body skin examination, given prior history of skin cancer or lesions suspicious for skin cancer

The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field.

Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm.

Distributed Unified Control for Global Economic Operation and Resilience Reinforcement of Hybrid AC–DC Microgrids

The paralleled bidirectional interlinking converters (BILCs) of the hybrid AC/DC microgrid (HMG) provide a flexible and reliable power interaction way between AC and DC subgrids with high power density. A distributed unified control (DUC) is proposed for BILCs to achieve both resilience reinforcement and global economic operation of the HMG. For distributed generators (DGs), the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{ac}-\lambda _{ac}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$v_{dc}-\lambda _{dc}$</tex-math></inline-formula> economic droop controls are employed for AC DGs and DC DGs to decrease the individual subgrid's generation expense by equaling DGs' incremental expenses (IEs). For BILCs, the normalized AC subgrid's frequency and DC subgrid's voltage are coordinated to achieve the economic power interaction (EPI), which further decreases the total generation expense (TGE). Besides economic operation, the proposed DUC is capable of adopting different MG operation modes into a unified control structure without any mode switching. Paralleled BILCs are managed to reinforce the system resilience by supporting the DC voltage or AC voltage once all DC or AC DGs fail. In addition, the proposed DUC enables BILCs with plug-and-play characteristics, resistance to the maximum acceptable communication delay, and the communication disruption robustness. The corresponding hardware-in-the-loop (HIL) results verify the effectiveness of the proposed control strategy.

Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station

The primary purpose of green communication is to reduce energy use. The base station (BS) is a radio receiver/transmitter that acts as the wireless network’s hub. It serves as a link between a wired and wireless network. To receive and transmit messages, BS uses a lot of energy. The use of effective sleep and wake-up/setup activities with an acceptable delay helps reduce base station power consumption. In this paper, the BS’s service process is modelled as a finite buffer queue with close down, sleep, and setup. After a certain number of user requests (URs) have accumulated in the system, to awaken the BS from multiple sleeps (MS) the -Policy is implemented. To produce probability generating functions, the supplementary variable approach is applied. The UR’s mean delay and the BS’s mean power consumption are calculated using simulation. According to computational studies, multiple sleeps with -policy consume less power than multiple sleeps without -policy.

Reinforcement Learning-Based Joint User Scheduling and Link Configuration in Millimeter-Wave Networks

In this paper, we develop algorithms for joint user scheduling and three types of mmWave link configuration: relay selection, codebook optimization, and beam tracking in millimeter wave (mmWave) networks. Our goal is to design an online controller that dynamically schedules users and configures their links to minimize system delay. To solve this complex scheduling problem, we model it as a dynamic decision-making process and develop two reinforcement learning-based solutions. The first solution is based on deep reinforcement learning (DRL), which leverages the proximal policy optimization to train a neural network-based solution. Due to the potential high sample complexity of DRL, we also propose an empirical multi-armed bandit (MAB)-based solution, which decomposes the decision-making process into a sequential of sub-actions and exploits classic maxweight scheduling and Thompson sampling to decide those sub-actions. Our evaluation of the proposed solutions confirms their effectiveness in providing acceptable system delay. It also shows that the DRL-based solution has better delay performance while the MAB-based solution has a faster training process.

Organic superbase-mediated synthesis of borohydride ionic liquids as novel composite hypergolic fuels

This study prepared a series of novel hypergolic fluids based on borohydride ionic liquids and organic superbase using an in situ synthetic method. In these hypergolic fluids, ionic liquids in 1,5-diazabicyclo[4.3.0]non-5-ene (DBN) and 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) acted as triggers for the self-ignition of DBN and DBU upon contact with high-concentration hydrogen peroxide (H2O2). These hypergolic fluids had high densities (>1.000 ​g ​cm−3), low viscosities (as low as 34.03 ​cP), and acceptable ignition delay times (IDT). The ignition processes of the hypergolic fluids with 90% H2O2 as an oxidizer were first investigated in this study, and they differed from the previously reported ignition phenomena. Different from the case with white fuming acid (WFNA) as an oxidizer, the ignition processes of hypergolic fluids with 90% H2O2 as an oxidizer did not exhibit secondary rebound and splashing and formed a homogeneous mixed layer when the droplets were in contact with 90% H2O2. The different ignition processes significantly influenced the properties of hypergolic fluids. Compared with the hypergolic fluids with WFNA as an oxidizer, those with 90% H2O2 as an oxidizer showed a shorter IDT (IDTmin[90% H2O2]=28.3 ​ms, IDTmin[WFNA]=126 ​ms) and formed stable flames without secondary combustion. These results demonstrate that the in-situ synthesized fuels in this study hold great promise as green fuels in hypergolic propulsion systems.

A dual-mode MAC protocol with service differentiation for industrial IoT networks using wake-up radio

The Internet of Things (IoT) has successfully revolutionized our society and all aspects of our daily lives. Wake-up radio (WuR) technology on its part has contributed to the success of IoT by significantly reducing latency and power consumption. Moreover, emerging mission critical Industrial IoT (IIoT) applications are time sensitive and generate more stringent delay and reliability constraints. Therefore, WuR-based MAC protocols must be carefully designed to meet these requirements. In this paper, a Dual-Mode MAC protocol (D2MAC) is proposed to fulfill QoS requirements of both delay-tolerant and real-time IoT applications by using service differentiation in the context of WuR-based IoT networks. Our protocol adopts two operation modes: normal mode, used in the case of normal traffic conditions, and priority mode for time–critical data. A Markov chain model is used to evaluate the performance of D2MAC. A simulation model based on MiXiM/ OMNet++ framework is also proposed to validate this analytical model. We illustrate the benefits of using the priority mode (PCA-WuR) to fulfill the requirements of IIoT applications. We also demonstrate that the Performance of PCA-WuR strongly depends on the acceptable delay limit for packet transmission.