Delay Tolerance Research Articles

Joint optimization of social interactivity and server provisioning for interactive games in edge computing

Distributed interactive applications (DIAs), like immersive virtual reality and multiplayer cloud games, where players team-up and form social communities to participate in collaborative virtual events, dominate the modern cloud multimedia industry. Still, due to the massive workload, intensive graphics processing and delay intolerance, conventional cloud-only models are increasingly becoming ineffective. Thankfully, with the shift towards Mobile Edge Computing, game providers can leverage existing cellular infrastructure to deploy edge servers that supply gaming services in a highly accessible fashion. One fundamental challenge, that naturally arises, is the discovery of the most suitable player-to-edge server assignment strategy, that achieves a balanced trade-off between conflicting objectives that refer to high social interactivity on the one hand and low-cost server provisioning on the other. In this article, the core properties of social interactivity for gaming DIAs are investigated through formal formulation under diverse social aspects and strict assignment rules, respecting, in the meantime, the provisioning cost and constraints of the edge servers. We call this the “Social Interactivity-oriented Edge Allocation” (SIEA) problem and prove its NP-hardness. Based on our theoretical analysis, we present SIEA-H, a two-phase heuristic algorithm to efficiently assign players to servers and then iteratively refine the edge allocation strategy. SIEA-H is evaluated using comprehensive trace-driven simulations. The results demonstrate how it surpasses the baseline and state-of-the-art assignment alternatives in reducing the total cost, especially as the number of servers and their capacity grows, or the coverage area expands, while for players it exhibits the highest admission rate and the best overall resource management.

A comparative analysis of procedures to teach delay tolerance.

A comparative analysis of procedures to teach delay tolerance.

How long can I wait and how much frustration can I stand? Volitional traits and students’ PhD-intention and pursuit

The purpose of the present investigation is to analyze the relation of frustration tolerance and delay of gratification with PhD-intention and expectations. We conducted one correlational and two experimental studies. In Study 1 (N1 = 171 undergraduates), we found the hypothesized positive association between delay of gratification and frustration tolerance and the intention to obtain a PhD. In Studies 2 and 3, we used experimental vignette designs. In Study 2, doctoral students and postdocs (N2 = 180) evaluated a fictitious student regarding PhD-intention and a successful PhD-process. As expected, students with high gratification delay and frustration tolerance were judged as more likely to start and complete a PhD than students described low in these volitional traits. In Study 3, we contrasted Study 2’s findings by asking employees of the private sector (N3 = 150) to rate the same students’ intention to join a company instead. None of the factors influenced participants’ judgments when it comes to a non-academic career track.

Dynamic task offloading for Internet of Things in mobile edge computing via deep reinforcement learning

SummaryWith the development of Internet of Things (IoT), more and more computation‐intensive tasks are generated by IoT devices. Due to the limitation of battery and computing capacity of IoT devices, these tasks can be offloaded to mobile edge computing (MEC) and cloud for processing. However, as the channel states and task generation process are dynamic, and the scales of task offloading problem and solution space size are increasing rapidly, the collaborative task offloading for MEC and cloud faces severe challenges. In this paper, we integrate the two conflicting offloading goals, which are maximizing the task finish ratio with tolerable delay and minimizing the power consumption of devices. We formulate the task offloading problem to balance the two conflicting goals. Then, we reformulate it as an MDP‐based dynamic task offloading problem. We design a deep reinforcement learning (DRL)‐based dynamic task offloading (DDTO) algorithm to solve this problem. Our DDTO algorithm can adapt to the dynamic and complex environment and adjust the task offloading strategies accordingly. Experiments are also carried out which show that our DDTO algorithm can converge quickly. The experiment results also validate the effectiveness and efficacy of our DDTO algorithm in balancing finish ratio and power.

Agent‐based time delay margin in consensus of multi‐agent systems by an event‐triggered control method: Concept and computation

AbstractThis paper deals with defining the concept of agent‐based time delay margin and computing its value in multi‐agent systems controlled by event‐triggered based controllers. The agent‐based time delay margin specifying the time delay tolerance of each agent for ensuring consensus in event‐triggered controlled multi‐agent systems can be considered as complementary for the concept of (network) time delay margin, which has been previously introduced in some literature. In this paper, an event‐triggered control method for achieving consensus in multi‐agent systems with time delay is considered. It is shown that the Zeno behavior is excluded by applying this method. Then, in a multi‐agent system controlled by the considered event‐triggered method, the concept of agent‐based time delay margin in the presence of a fixed network delay is defined. Moreover, an algorithm for computing the value of the time delay margin for each agent is proposed. Numerical simulation results are also provided to verify the obtained theoretical results.

A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices

The prominence of Machine-to-Machine (M2M) communications in the future wide area communication networks place various challenges to the cellular technologies such as the Long Term Evolution (LTE) standard, owing to the large number of M2M devices generating small bursts of infrequent data packets with a wide range of delay requirements. The channel structure and Quality of Service (QoS) framework of LTE networks fail to support M2M traffic with multiple burst sizes and QoS requirements while a bottleneck often arises from the limited control resources to communicate future uplink resource allocations to the M2M devices. Moreover, many of the M2M devices are battery-powered and require a low-power consuming wide area technology for wide-spread deployments. To alleviate these issues, in this article we propose an adaptive semi-persistent scheduling (SPS) scheme for the LTE uplink which caters for multi-service M2M traffic classes with variable burst sizes and delay tolerances. Instead of adhering to the rigid LTE QoS framework, the proposed algorithm supports variation of uplink allocation sizes based on queued data length yet does not require control signaling to inform those allocations to the respective devices. Both the eNodeB and the M2M devices can determine the precise uplink resource allocation related parameters based on their mutual knowledge, thus omitting the burden of regular control signaling exchanges. Based on a control parameter, the algorithm can offer different capacities and levels of QoS satisfaction to different traffic classes. We also introduce a pre-emptive feature by which the algorithm can prioritize new traffic with low delay tolerance over ongoing delay-tolerant traffic. We also build a model for incorporating the Discontinuous Reception (DRX) mechanism in synchronization with the adaptive SPS transmissions so that the UE power consumption can be significantly lowered, thereby extending their battery lives. The simulation and performance analysis of the proposed scheme shows significant improvement over the traditional LTE scheduler in terms of QoS satisfaction, channel utilization and low power requirements of multi-service M2M traffic.

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

A Novel Untethered Hand Wearable with Fine-Grained Cutaneous Haptic Feedback.

During open surgery, a surgeon relies not only on the detailed view of the organ being operated upon and on being able to feel the fine details of this organ but also heavily relies on the combination of these two senses. In laparoscopic surgery, haptic feedback provides surgeons information on interaction forces between instrument and tissue. There have been many studies to mimic the haptic feedback in laparoscopic-related telerobotics studies to date. However, cutaneous feedback is mostly restricted or limited in haptic feedback-based minimally invasive studies. We argue that fine-grained information is needed in laparoscopic surgeries to study the details of the instrument’s end and can convey via cutaneous feedback. We propose an exoskeleton haptic hand wearable which consists of five 4 × 4 miniaturized fingertip actuators, 80 in total, to convey cutaneous feedback. The wearable is described as modular, lightweight, Bluetooth, and WiFi-enabled, and has a maximum power consumption of 830 mW. Software is developed to demonstrate rapid tactile actuation of edges; this allows the user to feel the contours in cutaneous feedback. Moreover, to demonstrate the idea as an object displayed on a flat monitor, initial tests were carried out in 2D. In the second phase, the wearable exoskeleton glove is then further developed to feel 3D virtual objects by using a virtual reality (VR) headset demonstrated by a VR environment. Two-dimensional and 3D objects were tested by our novel untethered haptic hand wearable. Our results show that untethered humans understand actuation in cutaneous feedback just in a single tapping with 92.22% accuracy. Our wearable has an average latency of 46.5 ms, which is much less than the 600 ms tolerable delay acceptable by a surgeon in teleoperation. Therefore, we suggest our untethered hand wearable to enhance multimodal perception in minimally invasive surgeries to naturally feel the immediate environments of the instruments.

Enabling Delay Tolerance in IoT based Vehicular Networks

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.

Storage-assisted optical upstream transport scheme for task offloading in multi-access edge computing

Multi-access edge computing (MEC) applications are often implemented in the form of task offloading, which results in an unprecedented demand for data transfers among MEC servers. However, the combination of expensive and limited bandwidth, growing peak demand, and heterogeneous requirements of mixed traffic has posed a great challenge in terms of task offloading. In this study, we present a storage-assisted optical upstream transport scheme (SOUT) to overcome this challenge. Latency-critical (LC) tasks are given preemptive priority over delay-tolerant (DT) tasks. To reduce peak demand, the storage of an MEC server is introduced to temporarily store DT tasks. Resource partitioning is performed with an adjustable boundary based on traffic fluctuation. Analytic models are presented to investigate the interplay between SOUT and the performance of tasks. Our key findings reveal that there exist two trade-offs to be considered in SOUT. To balance the trade-offs, we formulate the spectrum partitioning and storage assignment problem as an optimization model and solve it using a heuristic approach. Studies show that SOUT provides lower blocking probability for both LC and DT tasks at the cost of slight preemption and limited storage usage when compared with two state-of-the-art optical transport schemes. We further show that 60% of network expenditures can be saved by trading cost-efficient storage for expensive link spectrum resources under a certain network scenario. Overall, this study aims to provide useful insights into task offloading over elastic optical links.

A model for pest control using integrated approach: impact of latent and gestation delays

In this article, we have established a mathematical model using impulsive differential equations for the dynamics of crop pest management in the presence of a pest with its predator and bio-pesticides. The pest population is divided into two subpopulations, namely, the susceptible pests and the infected pests. In this control process, bio-pesticides (generally virus) infect the susceptible pest through viral infection within the pest and make it infected so that predators can consume it quickly. We assume that pest controlling, using this integrated approach, is a delayed process and thus incorporated latent time of susceptible pest and gestation delay of predator in the model as time delay parameters. The system dynamics have been analyzed using qualitative theory: the existence of the equilibrium points and their stability properties has been derived. Hopf bifurcation of the coexisting equilibrium point is presented for both the delayed and non-delayed system. Detail numerical simulations are performed in support of analytical results and illustrate the different dynamical regimes observed in the system. We have observed that the system becomes free of infection when the latent time of the pest is large. Coexisting equilibrium exists for the lower value of latent delay, and it can change the stability properties from stable to unstable when it crosses its critical value. In contrast, gestation delay affects the stability switches of coexisting equilibrium only. The combined effect of the two delays on the system is shown numerically. Also, viral replication rate, infection rate (from virus to pest) is also significant from the pest management perspective. In summary, both the delay are essential for crop pest management, and pest control will be successful with tolerable delays.

Real-time traffic sign detection and recognition using Raspberry Pi

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

Revenue Maximization Through Cell Switching and Spectrum Leasing in 5G HetNets

Even though dynamic cell switching is a prominent approach for energy optimization in heterogeneous wireless communication networks, it results in spectrum under-utilization as the spectrum originally occupied by the base stations that are turned off remain dormant. In order to make the businesses of primary network (PN) operators, who hold the spectrum license, more profitable and sustainable as well as to avoid spectrum under-utilization, this dormant spectrum can be leased to the secondary network (SN) operators who cannot afford to purchase the spectrum license. In this study, first, the cell switching problem, which solely focuses on the amount of energy saved, is translated to a problem of revenue maximization by including the spectrum leasing concept and converting the energy saving to monetary saving from reduced electricity bills. In this regard, two spectrum demand scenarios are considered for the SN operator: delay tolerant (DT), for non-real time applications, and non-delay tolerant (NDT), for real time applications. Then, a cell switching and spectrum leasing framework based on simulated annealing algorithm is developed to maximize the revenue of the PN while respecting the quality-of-service constraints. The simulation results reveal that the DT spectrum demand is more beneficial to both PN and SN operators as it results in greater revenue for the former while the latter is able to access more spectrum to meet higher service demands. This finding suggests that if the application can tolerate delays, then it makes more sense for both PN and SN to adopt the DT scenario. In addition, it is observed that the performance of the proposed framework is very close to that of the optimal solution with a significant reduction in the computation complexity.

Green Multistage Upgrade of Switches and Controllers for Bundled-Links SDNs

The switches in legacy networks perform both control and data forwarding operations. In contrast, in a software defined network (SDN), a controller is responsible for network control and each SDN-switch (<i>s</i>-switch) only forwards data packets. Advantageously, controllers can be upgraded easily to run state-of-the-art network configuration and management solutions. In this context, this paper addresses the novel problem of upgrading a legacy network into an SDN over multiple stages that span months or years. More specifically, it aims to minimize energy consumption by optimizing switch upgrades, controller placement, and traffic routing subject to an operator&#x2019;s budget, traffic delay tolerance, and controller capacity. We formulate the problem as a mixed integer program (MIP), and develop a heuristic solution that ensures a single path is used between switches and up to two link-disjoint paths are used between an s-switch and its controller. Our simulation results show that increasing an operator&#x2019;s budget and the number of upgrade stages reduce the energy consumption of tested networks by 68.42%. Further, our heuristic solution yields energy saving that is within 5% away from the optimal value. In addition, deploying controllers at strategic locations saves more energy than placing them at arbitrary locations. Lastly, our heuristic solution guarantees the delay requirement of traffic demands and runs up to 307 times faster than a prior solution.

Global Input Delay Tolerance of MIMO Nonlinear Systems under Nonsmooth Feedback: A Homogeneous Perspective

In this paper, we tackle the problem of when a globally asymptotically stabilizable (GAS) nonlinear system by either state or output feedback is globally tolerable with respect to input delay. We focus on the case when the continuous system is not stabilizable by linear or smooth feedback, and thus local exponential stabilizability (LES) is not fulfilled. With the aid of the Lyapunov-Krasovskii functional method and weighted homogeneity, we prove that for a multi-input-multi-output (MIMO) nonlinear system with homogeneous degree zero, global asymptotic stabilizability by nonsmooth but H&#x00F6;lder continuous state or output feedback implies global asymptotic stability (GAS) of the closed-loop system with input delay, i.e., the property of global input delay tolerance (GIDT). We then illustrate applications of the obtained GIDT results to significant classes of time-delay nonlinear systems with uncontrollable/unobservable linearization, including but not limited to a chain of nonlinear integrators, higher-order systems with a lower-triangular or upper-triangular structure, and beyond.

A Review on Detection of Malicious Node in Delay Tolerance Network

TODAY, The use of a wireless network enables enterprises to avoid the costly process of introducing cables into buildings or as a connection between different equipment locations. The basis of wireless systems are radio waves, an implementation that takes place at the physical level of network structure. Wireless network is installations a method by which homes, telecommunications networks and enterprise (business) avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. Delay tolerant networks (DTNs), such as sensor networks with scheduled intermittent connectivity, vehicular DTNs that disseminate location-dependent information, and pocket-switched networks that allow humans to communicate without network infrastructure, are highly partitioned networks that may suffer from frequent disconnectivity. In DTNs, the in-transit messages, also named bundles, can be sent over an existing link and buffered at the next hop until the next link in the path appears. This message propagation process is usually referred to as the “store-carry-and-forward” strategy, and the routing is decided in an “opportunistic” fashion. We aim to evaluate the added effect of the presence of malicious nodes on ad hoc network performance, and determine appropriate measures to detect malicious nodes. A malicious node advertising itself as having a valid route to the destination. With this intension the attacker consumes or intercepts the packet without any forwarding. An attacker can completely modify the packet and generate fake information, this cause the network traffic diverted or dropped. Let H be a malicious node. When H receives a Route Request, it sends back a Route Reply immediately, which constructs the data and can be transmitted by itself with the shortest path. So S receives Route Reply and it is replaced by H-&gt;S. then H receives all the data from S. In this research we propose a new assesment based scheme for detection of Malicious Nodes in DTN. And examine different strategies for prevention to malicious nodes as well as Compare out come proposed scheme with the earliest established schemes.

Real-Time Controller Reconfiguration for Delay-Resilient Cyber-Physical Systems

Networks on the cyber-physical systems (CPSs) configure feedback control loops between physical systems in the real world and control software in the cyber world. Malicious behaviors on the networks can increase network delays by exhausting limited network resources and security vulnerabilities to destabilize CPSs, which are entitled the network delay attack. In this paper, we focus on the problem of how to guarantee the stability of CPS under the network delay attack. We propose a real-time controller reconfiguration to ensure the resiliency of the physical systems against the network delay attack. Our controller reconfiguration consists of two algorithms: controller gain tuning and access point (AP) handover, which give a delay tolerance and an attack avoidance, respectively. Depending on the network delays, the computing system adopts one of these two algorithms and mitigates the physical impacts of the network delay attack. We validate that the proposed controller reconfiguration can ensure the resiliency of CPS against the network delay attack by implementing a testbed with wireless networks.

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

The edge computing paradigm has become extremely popular over the past years, as a means of offloading computationally intensive tasks by users of resource and battery-constrained devices. Nevertheless, the edge networks’ overexploitation by the ever-increasing number of task-offloading users, gradually leads to their performance degradation. In this paper, we leverage on the different levels of available computing capabilities across the network, and we design an incentive mechanism that aims to shift the selfish users’ preference from the edge to the upper fog computing layer, accounting for their level of delay tolerance. To deal with the users’ heterogeneity in terms of their applications’ multi-dimensional distinctive features (including their delay tolerance/sensitivity), a multi-dimensional contract theory modeling is adopted, according to which the edge server determines the bundles of the users’ provided efforts and corresponding offered rewards. In this respect, each user’s effort represents the amount of its initially offloaded task at the edge that is allowed to be further forwarded and processed at the fog. Considering that the users-to-edge server offloading is performed under Non-Orthogonal Multiple Access (NOMA), the problem of joint computation task offloading and uplink transmission power allocation is subsequently addressed via a Stackelberg game, where the edge server and the users are treated as leader and followers, respectively. The aim of the game is to minimize the end-to-end network’s energy consumption and increase its resource utilization efficiency. The incentive mechanism and resource allocation framework is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

Virtual reality intervention effects on future self-continuity and delayed reward preference in substance use disorder recovery: pilot study results

Sustained remission from substance use disorder (SUD) is challenged by high relapse rates, which provides opportunities for novel clinical interventions. Immersive virtual reality (VR) permits delivering synthetic experiences that feel real and actualizes otherwise impossible scenarios for therapeutic benefit. We report on the feasibility of an immersive VR intervention designed to increase valuation of the future by enhancing future self-continuity and leveraging future self-discrepancy with personalized future selves as SUD recovery support. Twenty-one adults in early SUD recovery (< 1 year) interacted with versions of themselves age-progressed fifteen years from two different behavioral trajectories: an SUD Future Self and a Recovery Future Self. The future selves’ interactive monologs include personalized details and voice for a lifelike interaction within a time travel vignette. Before and following the intervention, participants rated future self-continuity and performed delay discounting. Following the intervention, daily images of the Recovery Future Self were sent to participants’ smartphones for thirty days. The VR intervention generated no adverse events, was well tolerated (presence, liking, and comfort), and significantly increased future self-continuity and delayed reward preference (doubling delay tolerance). The intervention also reduced craving, ps < 0.05. Thirty days later, n = 18 remained abstinent; importantly, increased future self-similarity persisted. Abstainers’ future self-similarity increased following VR. All individual participants showing increased future self-similarity post-VR remained abstinent, and all participants who relapsed showed either reduced or zero effect on future self-similarity. Post-intervention semi-structured interviews revealed emotional engagement with the experience. VR simulation of imagined realities reifies novel clinical interventions that are practicable and personalized. The current study demonstrates an implementation readily applied in the clinic and shows promise for facilitating SUD recovery. Creative collaboration between researchers, clinicians, and VR developers has great potential to revolutionize mental health interventions and expand the range of tools for clinicians targeting SUD and other disorders.