Delay Tolerance Research Articles

HaptoFloater: Visuo-Haptic Augmented Reality by Embedding Imperceptible Color Vibration Signals for Tactile Display Control in a Mid-Air Image.

We propose HaptoFloater, a low-latency mid-air visuo-haptic augmented reality (VHAR) system that utilizes imperceptible color vibrations. When adding tactile stimuli to the visual information of a mid-air image, the user should not perceive the latency between the tactile and visual information. However, conventional tactile presentation methods for mid-air images, based on camera-detected fingertip positioning, introduce latency due to image processing and communication. To mitigate this latency, we use a color vibration technique; humans cannot perceive the vibration when the display alternates between two different color stimuli at a frequency of 25 Hz or higher. In our system, we embed this imperceptible color vibration into the mid-air image formed by a micromirror array plate, and a photodiode on the fingertip device directly detects this color vibration to provide tactile stimulation. Thus, our system allows for the tactile perception of multiple patterns on a mid-air image in 59.5 ms. In addition, we evaluate the visual-haptic delay tolerance on a mid-air display using our VHAR system and a tactile actuator with a single pattern and faster response time. The results of our user study indicate a visual-haptic delay tolerance of 110.6 ms, which is considerably larger than the latency associated with systems using multiple tactile patterns.

Double Deep Q- energy aware Service allocation based on Dynamic fractional frequency reusable technique for lifetime maximization in HetNet-LTE network

The development of mobile communication in heterogeneous networks is incredible in providing various services through wireless cellular communication through advanced long-term evaluation networks. Increasing multi-concern services and frequencies in spectrum channels are highly layered to select the bandwidth to provide the fastest network without interference. Selecting the channel through macro cell selection is essential to improve network communication and provide the quickest service. Most frequency reuse techniques use service optimality and route selection-based protocols to enrich the packet flow. Still, the improper spectrum delights create more delay tolerance due to short-range service optimality due to energy loss by selecting the short spectrum signal to reuse, which doesn't support the lifetime improvement of the LTE network. To resolve these problems, we propose a Double Deep Q- energy-aware Service allocation based on a Dynamic fractional frequency reusable technique for lifetime maximization in the HetNet-LTE network. Initially, the heterogenous communication environment and node deplanement were carried out to construct the LTE network under the WCC. The communication logs are Route Table (RT), and its services are taken by all node LTE Communication Impact Rate (LTE-CIR). Then, the Backhaul Traffic Algorithm (BTA) is applied to predict the interference on traffic rate from the channel frequency margin. Select the balanced node using the Channel Interference Macro Cell Selection (CIMCS) technique. Considering frequency limits with the Double Deep Q- Network (DDQN) approach, energy-aware selects the optimal route to reuse the frequency level using Frequency Domain Packet Scheduling (FDPS) to improve communication. The proposed system improves the overall throughput by up to 97.8 % with adopted channel selection from the macro unit to improve the latency performance. Also, the interference frequency limits are dynamically reused at an energy optimal level with low-level delay tolerance to improve the link stability by up to 98.4 % with higher lifetime maximation in the LTE network.

New insights in the mechanisms of opioid analgesia and tolerance: Ultramicronized palmitoylethanolamide down-modulates vascular endothelial growth factor-A in the nervous system

Growing evidence suggests that opioid analgesics modulate angiogenesis during pathophysiological processes. Vascular endothelial growth factor-A (VEGF-A) was recently proposed to be involved in pain development. To date, no anti-angiogenic drug is used for pain management. When administered in a bioavailable formulation, (i.e., ultramicronized) N-palmitoylethanolamine (PEA) delays the onset of morphine tolerance, improves morphine analgesic activity and reduces angiogenesis in in vivo models. This study aimed at investigating whether VEGF-A is involved in PEA-induced delay of morphine tolerance. The anti-VEGF-A monoclonal antibody bevacizumab was used as a reference drug. Preemptive and concomitant treatment with ultramicronized PEA delayed morphine tolerance and potentiated the analgesic effect of morphine, while counteracting morphine-induced increase of VEGF-A in the nervous system. Similar results were obtained when bevacizumab was administered together with morphine. Of note, bevacizumab showed an analgesic effect per se. In equianalgesic treatment regimens (obtained through increasing morphine doses and associating PEA), PEA resulted in lower expression of VEGF-A in dorsal root ganglia (DRG) and spinal cord compared to morphine alone. Similar results were observed for plasma levels of the soluble VEGF receptor 1 (sFLT-1). Moreover, in morphine-treated animals, two pain-related genes (i.e., Serpina3n and Eaat2) showed a more than 3-fold increase in their expression at spinal cord and DRG level, with the increase being significantly counteracted by PEA treatment. This study supports the hypothesis that the effects of PEA on morphine analgesia and tolerance may be mediated by the down-modulation of VEGF-A and sFLT-1 in the nervous system and plasma, respectively.

Teaching delay tolerance to a child with Smith‐Magenis syndrome in a classroom using a simplified approach

AbstractDelay tolerance training is used to teach children to accept delayed access to a requested item or event without exhibiting challenging behaviors. It is used during schedule thinning following acquisition of a functional communication response (FCR) or trained on its own in a skills‐based treatment package. Typically, delay tolerance training occurs in a clinic or hospital, and rarely in a school classroom. Most often delay tolerance training has been used with children with autism spectrum disorder (ASD). This study integrated findings from previous research on delay tolerance training to evaluate training in a classroom, with a child with Smith‐Magenis syndrome, using a simplified protocol. The training resulted in acquisition of delay tolerance for 5 min from a baseline of 32 s (average). The participant's teachers were then trained to use similar strategies throughout the school day and quickly acquired and applied the procedure with reported benefits.

Load-balanced offloading of multiple task types for mobile edge computing in IoT

The continuous development of mobile networks poses new challenges for end devices with limited computing power. Mobile or multi-access edge computing (MEC) has been proposed for providing the computing resources close to the end devices that need them. However, in real network environments, MEC servers also have limited computing resources that need to be shared among many devices and efficient resource allocation is critical to ensure that the limited resources are optimally used. In view of this, we propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. The tasks to be offloaded are first prioritized according to their type, size and maximum tolerable delay; then different offloading algorithms are executed for different priority tasks according to the level of the priority and the current load on the MEC server. Following which, the optimal offloading policy is determined iteratively. Simulation results show that BOMT can effectively reduce system delay, increase user coverage and offload task completion rates.

Operational Tests for Delay-Tolerant Network between the Moon and Earth Using the Korea Pathfinder Lunar Orbiter in Lunar Orbit

The Korea Pathfinder Lunar Orbiter (KPLO) was launched on 5 August 2022, equipped on the SpaceX Falcon 9 launch vehicle. At present, the KPLO is effectively carrying out its scientific mission in lunar orbit. The KPLO serves as a cornerstone for the development and validation of Korean space science and deep space technology. Among its payloads is the DTNPL, enabling the first-ever test of delay-tolerant network (DTN) technology for satellites in lunar orbit. DTN technology represents a significant advancement in space communication, offering stable communication capabilities characterized by high delay tolerance, reliability, and asymmetric communication speeds—a necessity for existing satellite and space communication systems to evolve. In this paper, we briefly give an overview of the Korea Lunar Exploration Program (KLEP) and present scientific data gathered through the KPLO mission. Specifically, we focus on the operational tests for DTN-ION conducted for message and file transfer, as well as real-time video streaming, during the initial operations of the KPLO. Lastly, this study offers insights and lessons learned from KPLO DTNPL operations, with the goal of providing valuable guidance for future advancements in space communication.

An Adaptive Multi-Objective Genetic Algorithm for Solving Heterogeneous Green City Vehicle Routing Problem

Intelligent scheduling plays a crucial role in minimizing transportation expenses and enhancing overall efficiency. However, most of the existing scheduling models fail to comprehensively account for the requirements of urban development, as exemplified by the vehicle routing problem with time windows (VRPTW), which merely specifies the minimization of path length. This paper introduces a new model of the heterogeneous green city vehicle routing problem with time windows (HGCVRPTW), addressing challenges in urban logistics. The HGCVRPTW model considers carriers with diverse attributes, recipients with varying tolerance for delays, and fluctuating road congestion levels impacting carbon emissions. To better deal with the HGCVRPTW, an adaptive multi-objective genetic algorithm based on the greedy initialization strategy (AMoGA-GIS) is proposed, which includes the following three advantages. Firstly, considering the impact of initial information on the search process, a greedy initialization strategy (GIS) is proposed to guide the overall evolution during the initialization phase. Secondly, the adaptive multiple mutation operators (AMMO) are introduced to improve the diversity of the population at different evolutionary stages according to their success rate of mutation. Moreover, we built a more tailored testing dataset that better aligns with the challenges faced by the HGCVRPTW. Our extensive experiments affirm the competitive performance of the AMoGA-GIS by comparing it with other state-of-the-art algorithms and prove that the GIS and AMMO play a pivotal role in advancing algorithmic capabilities tailored to the HGCVRPTW.

A shared ride matching approach to low-carbon and electrified ridesplitting

Ridesplitting has the potential to enhance transportation efficiency and reduce CO2 emissions from ridesourcing services by matching multiple shared rides to the same trip. However, an inappropriate matching can significantly diminish the environmental performance of ridesplitting. This study aims to propose a shared ride matching approach to low-carbon and electrified ridesplitting. Firstly, feasible ridesplitting trips within time constraints are identified and represented using a shareability network. Subsequently, the CO2 emission reduction of each feasible ridesplitting trip is calculated by comparing it with its corresponding single ride alternative. To maximize the total CO2 emission reduction from ridesplitting trips, an optimization model for shared ride matching is established and solved using a graph-theoretic algorithm. Additionally, a priority assignment policy is designed to preferentially assign available electric vehicles to feasible ridesplitting trips that have greater potential for emission reductions. Comparative experiments conducted in Chengdu, China show that the proposed approach can achieve higher levels of CO2 emission reduction without compromising service quality in ridesplitting operations. Furthermore, significant improvements in emission reductions can be achieved by implementing the priority assignment policy instead of a random assignment policy for electrified ridesplitting trips, particularly when electric vehicles are limited. Finally, the effects of maximum tolerable delay and electric vehicle penetration are further analyzed to provide valuable insights for the government and ridesourcing companies aiming to enhance both the economic and environmental benefits associated with ridesplitting.

Energy efficiency optimization of NB-IoT using integrated Proxy & ERAI technique

The 3rd Generation Partnership Project (3GPP) standardized the NarrowBand Internet of Things (NB-IoT) technology in Release-13, aiming to support applications requiring minimal device power consumption, efficient small data transfer, and tolerance for delays. This paper provides two major contributions: first, it characterizes the NB-IoT architecture and its enhancements in subsequent 3GPP releases; second, it introduces an energy-saving technique for NB-IoT user equipment based on semi-Markov processes. Integrating a Proxy state and an enhanced release assistant indication (ERAI), the proposed technique improves upon the traditional Discontinuous Reception (DRX) method in the NB-IoT standard. This novel approach reduces energy consumption during low data communication, distinguishing it from other power-saving methodologies. The effectiveness of this technique is assessed through the power-saving factor (PSF) under various conditions, such as different eDRX durations (T_eDRX), PSM durations (T_PSM), DRX cycle periods (T_ACTIVE+T_SLEEP), and DRX active periods (T_ACTIVE). Our findings confirm that the proposed Proxy & ERAI technique significantly boosts the power-saving capabilities of NB-IoT devices, especially for small data packet transmissions. Theoretical and simulation results indicate that the PSF can be enhanced by up to 99.4% with optimized eDRX durations (T_eDRX) and up to 99.9% with optimized PSM (T_PSM) durations. These results underscore the significant energy-saving potential of our proposed technique, making it highly effective for NB-IoT applications with low data transmission needs.

Enhanced Call Admission Control based on History in BWA Networks

WiMAX is a wireless technology that supports many blooming applications. To efficiently assign resources to traffic flows, bandwidth allocation and Connection Admission Control (CAC) modules are included in MAC layer and Burst Allocation (BA) module is added to PHY layer. It is essential to lessen delay and effectively schedule requests for BA. In this paper, CAC-based on History (CACH) and scheduler based on Delay Tolerance (SDT) for BA are proposed for WiMAX networks. CACH accepts/ rejects connections based on values in Contention Window (CW) in former Time Frame (TF). SDT and a Bucket-based BA (BBA) assign resources to flows with least amount of DT. Performance is analyzed based on throughput, Packet Loss Ratio (PLR) and delay. Index Terms: WiMAX, Contention Window, Contention Delay, Delay Tolerance, Traffic Priority, Connection Admission Control, Burst Allocation

Dynamic task offloading and service caching based on game theory in vehicular edge computing networks

Vehicular edge computing (VEC) enables task offloading from vehicle to edge servers deployed on Road Side Units (RSUs) and to vehicle with idle resources, which is considered as a promising vehicle network architecture. However, efficient task offloading and task resource cache management in the VEC network is challenging. In this work, we formulate a joint task offloading and service caching problem (JTOSCP) for VEC with edge-vehicle cooperation, aiming to minimize the total task delay-energy value and cost value of all vehicles while guaranteeing task processing delay tolerance. The formulated JTOSCP is proven as NP-hard problem. Due to the complexity of JTOSCP, we propose a cooperative task service caching and task offloading algorithm named CO-MATCH. In this algorithm, we design the dynamic programming-based service caching (DPSC) Algorithm to incentivize edge services and volunteer vehicles to cache, which takes into account both service caching preferences and social similarity. The Many-to-One Matching Game (MOMG) Algorithm is proposed to stimulate edge device games and achieve the optimal task offloading. Then, the CO-MATCH method is proven to be stable. Simulation results demonstrate that our proposed approach can significantly improve the quality of service of user vehicles compared to the other methods.

A Scheduling Scheme for Minimizing Age Under Delay Tolerance in IoT Systems With Heterogeneous Traffic

A Scheduling Scheme for Minimizing Age Under Delay Tolerance in IoT Systems With Heterogeneous Traffic

Discriminative stimuli promote the efficacy of delay tolerance training.

Discriminative stimuli promote the efficacy of delay tolerance training.

Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

Consensus-based secondary control for DC microgrids with communication delays via a networked predictive control strategy

In today’s cyber–physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids’ communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber–physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems’ stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy.

Appointment Scheduling with Delay Tolerance Heterogeneity

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service time uncertainty. We aim to capture the delay tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance sensitivity, monotonicity, and convexity and positive homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the convexity of TAD index ensures a tractable modeling of the collective delay dissatisfaction in the appointment scheduling problem. Using the TAD index, we then develop an appointment model with known empirical service time distribution that minimizes the overall tolerance-aware delays of all users. We analyze the impact of delay tolerance on the sequence and schedule decisions and show that the resultant TAD appointment model can be reformulated as a mixed-integer linear program (MILP). Furthermore, we extend the TAD appointment model by considering service time ambiguity. In particular, we encode into the TAD index a moment ambiguity set and a Wasserstein ambiguity set, respectively. The former captures effectively the correlation among service times across positions and user types, whereas the latter captures directly the service time data information. We show that both of the resultant TAD models under ambiguity can be reformulated as polynomial-sized, mixed-integer conic programs (MICPs). Finally, we compare our TAD models with some existing counterpart approaches and the current practice using synthetic data and a case of real hospital data, respectively. Our results demonstrate the effectiveness of the TAD appointment models in capturing the user delay tolerance with heterogeneity and mitigating the worst-case delays. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled “Econometric Modeling and Economic Policy Studies”], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], and the Major Program of National Natural Science Foundation of China [Grant 72192843]. S. Wang was also supported by a grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0025 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0025 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

IHIBE: A Hierarchical and Delegated Access Control Mechanism for IoT Environments

Ensuring authorized access control in the IoT is vital for privacy and safety protection. Our study presents the novel IHIBE framework, which combines IOTA (a distributed ledger technology) with hierarchical identity-based encryption (HIBE), thereby enhancing both IoT security and scalability. This approach secures access tokens and policies while reducing the computational demand on data owners. Our empirical findings reveal a significant performance gap, with access rights delegation on the Raspberry Pi 4 exceeding those on AWS by over 250%. Moreover, our analysis uncovers optimal identity policy depths: up to 640 identities on AWS and 640 on the Raspberry Pi 4 for systems with higher tolerable delays, and 320 identities on AWS versus 160 on the Raspberry Pi 4 for systems with lower tolerable delays. The system shows practical viability, exhibiting insignificant operational time differences compared to Zhang et al.’s schemes, particularly in access rights verification processes, with a minimal difference of 33.35%. Our extensive security assessment, encompassing scenarios like encrypted token theft and compromise of authority, affirms the efficacy of our challenge-response and last-word challenge (LWC) mechanisms. This study underscores the importance of platform choice in IoT system architectures and provides insights for deploying efficient, secure, and scalable IoT environments.

Adaptive channel reservation multiple access protocol based on differentiated traffic guarantee

Ad Hoc network is widely used because of its distributed flexibility, in which nodes may generate different types of traffic, and some high-priority traffic requires lower delay and other Quality of service (QoS) requirements. However, as the load of traffic increases, the probability of conflict increases significantly, which makes it difficult to guarantee the requirement of these traffic. It is significant to design the efficient multiple access protocol to ensure the quality of different kinds of traffic. Channel reservation mechanism is an important method to ensure QoS. However, the existing channel reservation mechanism does not fine-adjust reservation parameters according to the QoS when dealing with various traffic differently, and the differentiation mechanism is rough. In addition, it is difficult to ensure the performance of common services when low-latency services are met. Therefore, a differentiated service guarantee adaptive reservation mechanism (DSGARM) to solve the above problems is proposed. The core idea of the protocol is to provide differentiated services in detail according to the delay tolerance requirements of each business. Absolute reservation mechanism is used to guarantee delay-sensitive services, and relative reservation mechanism is used to service relatively delay-insensitive services. In the protocol, a model is established to design an adaptive algorithm to quantitatively calculate reservation parameters that meet the delay requirements. On the premise of prioritized reservation for various services, appropriate reservation parameters can be adjusted according to network conditions to ensure the delay tolerance requirements of each type of service. The present work can be widely applied to scenarios with differentiated business requirements. The simulation results show the advantages of the present protocol and have great practical significance.

Disturbance observer-based sliding mode control of active vertical suspension for high-speed rail vehicles

Disturbance observer-based sliding mode control (SMC) on the active vertical suspension (AVS) is developed to suppress the car body’s rigid and flexible vibration to improve ride comfort for a high-speed rail vehicle. A vertical-dynamics-intended vehicle model is foremost built with the bounce, pitch, and bending mode of the car body involved. Two sliding surfaces and disturbance observers are then designed for SMC, which only require the car body states, obviating the bogies’ states. The observer is successful in estimating the effect of unknown track irregularities perturbation as well as externally straightforwardly excitations on the car body with good accuracy. Control efficacy is verified through comparison with passive cases and sky-hook controls. With modal damping below 2% in passive cases, the mode shape function, especially with large displacement at the suspension support, would enlarge the car body acceleration. Moreover, the longitudinal traction rod can significantly multiply the flexible vibration at certain speeds. Comparative studies indicate that SMC provides satisfactory outcomes in the rigid and bending vibration suppression when all the car body’s three modes are considered in the sliding surface. However, it requires a high-frequency response actuator and allows a tighter tolerance of the control time delay than the sky-hook control.

Optimized Edge Node Allocation Considering User Delay Tolerance for Cost Reduction

Optimized Edge Node Allocation Considering User Delay Tolerance for Cost Reduction