Response Time Cost Research Articles

Swarm Intelligence to Face IoT Challenges.

The Internet of Things (IoT) paradigm denotes billions of physical entities connected to Internet that allow the collecting and sharing of big amounts of data. Everything may become a component of the IoT thanks to advancements in hardware, software, and wireless network availability. Devices get an advanced level of digital intelligence that enables them to transmit real-time data without applying for human support. However, IoT also comes with its own set of unique challenges. Heavy network traffic is generated in the IoT environment for transmitting data. Reducing network traffic by determining the shortest route from the source to the aim decreases overall system response time and energy consumption costs. This translates into the need to define efficient routing algorithms. Many IoT devices are powered by batteries with limited lifetime, so in order to ensure remote, continuous, distributed, and decentralized control and self-organization of these devices, power-aware techniques are highly desirable. Another requirement is to manage huge amounts of dynamically changing data. This paper reviews a set of swarm intelligence (SI) algorithms applied to the main challenges introduced by the IoT. SI algorithms try to determine the best path for insects by modeling the hunting behavior of the agent community. These algorithms are suitable for IoT needs because of their flexibility, resilience, dissemination degree, and extension.

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications.

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

VECTOR

Ambient temperature distribution monitoring is desired in a variety of real-life applications including indoors temperature control and building energy management. Traditional temperature sensors have their limitations in the aspects of single point/item based measurements, slow response time and huge cost for distribution estimation. In this paper, we introduce VECTOR, a temperature-field monitoring system that achieves high temperature sensing accuracy and fast response time using commercial sound playing/recording devices. First, our system uses a distributed ranging algorithm to measure the time-of-flight of multiple sound paths with microsecond resolution. We then propose a dRadon transform algorithm that reconstructs the temperature distribution from the measured speed of sound along different paths. Our experimental results show that we can measure the temperature with an error of 0.25°C from single sound path and reconstruct the temperature distribution at a decimeter-level spatial resolution.

Energy Consumption and Performance Optimized Task Scheduling in Distributed Data Centers

A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users’ expectations on both aspects. To tackle this challenge, this work first formulates the problem into a constrained biobjective optimization problem. A biobjective algorithm, named simulated-annealing-based adaptive differential evolution (SADE), is presented to simultaneously reduce both the response time of tasks and energy cost. Meanwhile, a method of minimal Manhattan distance is adopted to search for a final knee, for achieving a good balance between response time minimization and energy cost reduction. Experimental results on real-life datasets, i.e., the electricity prices and tasks collected from a Google cluster trace, have proved that SADE yields less task response time and lower energy cost compared with state-of-the-art algorithms.

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

Non-droop-control-based cascaded superconducting magnetic energy storage/battery hybrid energy storage system

Existing parallel-structured superconducting magnetic energy storage (SMES)/battery hybrid energy storage systems (HESSs) expose shortcomings, including transient switching instability, weak ability of continuous fault compensation, etc. Under continuous faults and long-term power fluctuations, SMES part in existing SMES/battery HESSs will run out its energy and fail to quickly respond to subsequent disturbances. This paper proposes a new cascaded topology of SMES/battery HESS. It remains the superiorities of conventional HESS schemes from aspects of fast response (~5 ms) and large energy capacity. Moreover, it avoids the use of droop control and then resolves the transient dynamic problem between the switchover of the battery and SMES. In the cascaded HESS, both the energy storages simultaneously activate when power variation is detected, where the SMES is just for compensating the initial power insufficiency before the full-power start of the battery. The stored energy in SMES can be sustainably utilized and available for multiple repeated power compensations. A simulation is established to discuss the performance of the HESS as an uninterruptable power source with comparisons of the other three cases in aspects of response time, energy capacity, and capital cost. Finally, a scaled-down experiment is tested, and performance comparisons are discussed to verify the technical advancement of cascaded HESS.

Lagrangian Cooperation (LC): A Fog Node Cooperation Algorithm for Smart Cities

As a new paradigm, smart cities face many issues such as huge volumes of data and limited time for processing. To meet these challenges, fog computing via cooperation between servers from the network edge to the cloud is introduced. This can significantly reduce the response time, but also increase the amount of data in the fog layer and thus energy costs. Therefore, a convex problem is introduced to minimize the response time and energy costs. This problem is solved via a Lagrangian Cooperation (LC) algorithm. Results are presented which show that the proposed LC algorithm provides better performance than existing approaches.

A Bacterial Foraging Based Smart Offloading for IoT Sensors in Edge Computing

The main aim of edge computing is to facilitate the low power computational IoT sensors in processing heavy tasks. Sensors should be enough efficient to offload the required task and get back the computation results in the stipulated time. However, acquiring the desired quality of services (QoS) is a challenging task. Therefore, efficient resource management is inevitable to reduce the response time and communication cost. In this paper, a computation offloading algorithm based on nature-inspired multi-objective bacterial foraging optimization (MO-BFO) is proposed for load management over edge servers. The performance of the proposed MO-BFO algorithm is quantitatively evaluated against standard ant colony optimization (ACO), particle swarm optimization (PSO), and round-robin (RR) scheduler. The detailed results show that the proposed algorithm reduces the response time, communication cost, and ensures effective load management compared to its counterparts.

Reactive Hybrid Model for Fault Mitigation in Real-Time Cloud Computing

The Fourth Industrial Revolution is driven by cloud computing for increased productivity and efficiency. Despite the influence of cost-effectiveness, on-demand service and scalability, cloud computing is faced with many challenges such as security, performance, and fault tolerance. This research proposes a reactive mitigation model using checkpointing and replication techniques to overcome the challenges. The developed model has four layers: client/user, controller, fault tolerance (hybrid) and virtual machines layers. The functionalities of the developed model were compared to existing system using the same simulation conditions. The results showed a relative improvement over the existing system in terms of response time and computational cost and the developed system is able to react to faults by transferring requests to stable VMs and resume task execution at the last checkpoint. The developed model proved to work efficiently under various simulation conditions.

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

A full-set and self-powered ammonia leakage monitor system based on CNTs-PPy and triboelectric nanogenerator for zero-carbon vessels

With the further development of ammonia as a new energy fuel, it is urgent to develop a sustainable, accurate and low power consumption detection device for ammonia leakage. In this work, a self-powered full-set ammonia leakage monitoring device has been demonstrated, containing power generation system based on a triboelectric nanogenerator (TENG), ammonia sensing system based on carbon nanotube doped polypyrrole (CNTs-PPy) and signal collecting and transmitting system. This equipment has a good application prospect on NH3-fueled ships. The TENG can harvest mechanical energy of vibration during the engine operation to power the whole sensing system. When the external load resistance is 11 MΩ, the output power density can reach to 59.783 W/m3, powered by a five-layer honeycomb structure inspired triboelectric nanogenerator (V-TENG). The ammonia sensing system has the capability to detect ammonia with a low limit of detection (0.2 ppm), short response time (about 90 s), good selectivity, excellent stability and low cost, revealing the potential application of sensor for monitoring NH3 gas under different conditions. Finally, a bluetooth module is assembled into the device, realizing the wireless transmission from the detection module to the computer terminal. The self-powered and wireless full-set device can achieve the long-term and maintenance-free detection of ammonia leakage during the ocean voyage, which will play an important role in the further application of ammonia energy.

Evidence for object–place binding in pigeons in a sequence-learning procedure

We studied object–location binding in pigeons using a sequence learning procedure. A sequence of four objects was presented, one at a time at one of four locations on a touchscreen. A single peck at the object ended the trial, and food reinforcement was delivered intermittently. In Experiment 1, a between-subjects design was used to present objects, locations, or both in a regular sequence or randomly. Response time costs on nonreinforced probe tests on which object order, location order, or both were disrupted revealed sequence learning effects. Pigeons encoded location order when it was consistent, but not object order when it alone was consistent. When both were consistent, pigeons encoded both, and showed evidence of object–location binding. In Experiment 2, two groups of pigeons received training on sequences where the same object always appeared at the same location. For some pigeons a consistent sequence was used while for others sequence order was randomized. Only when sequence order was consistent was object–location binding found. These experiments are the first demonstrations of strong and lasting feature binding in pigeons and are consistent with a functional account of learning.

Cloud Computing Load Balancing Techniques: Retrospect and Recommendations

Load balancing is a research area that seeks to improve the quality of services provided to various clients in cloud computing environments. As cloud users increase around the world, cloud service providers are challenged to develop strategies for distributing tasks to machines for processing at cloud data centers. This work collected and undertook a thorough review of various load balancing techniques, uncovering the key limitations of existing strategies. The publications were chosen from peer-reviewed papers on Google Scholar. Cloud computing, cloud load balancing techniques, approaches to cloud load balancing, and big-data cloud computing systems were among the terms used in the search. Out of 201 studies, 39 met the criteria for inclusion. 5 of the research focused on cloud computing, 6 on cloud load balancing, 7 on resource scheduling in cloud, 16 on techniques for balancing cloud load, and 5 on big-data cloud computing environments. The study identified some research gaps and recommended a throughput-maximization based central-distributive load balancing architecture as a solution to maximize throughput, minimize response time and processing cost, and optimize load balancing architecture. Keywords— Centralized, cloud-computing, distributive, load-balancing.

Benchmarking study between capacitive and electronic load technic to track I-V and P-V of a solar panel

<p>To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods.</p>

Optimal PID Tuning of PLL for PV Inverter Based on Aquila Optimizer

Phase-locked loop (PLL) is a fundamental and crucial component of a photovoltaic (PV) connected inverter, which plays a significant role in high-quality grid connection by fast and precise phase detection and lock. Several novel critical structure improvements and proportional-integral (PI) parameter optimization techniques of PLL were proposed to reduce shock current and promote the quality of grid connection at present. However, the present techniques ignored the differential element of PLL and did not acquire ideal results. Thus, this paper adopts Aquila optimizer algorithm to regulate the proportional-integral-differential (PID) parameters of PLL for smoothing power fluctuation and improving grid connection quality. Three regulation strategies (i.e., PLL regulation, global regulation, and step regulation) are carefully designed to systematically and comprehensively evaluate the performance of the proposed method based on a simulation model in MATLAB/Simulink, namely, “250-kW Grid-Connected PV Array”. Simulation results indicate that PLL regulation strategy can effectively decrease power fluctuation and overshoot with a short response time, low complexity, and time cost. Particularly, the Error(P) and the maximum deviation of output power under optimal parameters obtained by PLL strategy are decreased by 418 W and 12.5 kW compared with those under initial parameters, respectively.

Enhancing replica management in a cloud environment using data mining based dynamic replication algorithm

Cloud computing has recently gained a lot of popularity and attention from the research community. One of the many on-demand services that large-scale applications provide to cloud customers is storage, which accumulates more generated data and subsequently leads to the need for storage. Despite the fact that users can use the cloud to store and provide the type of storage that desire, it still takes a significant amount of time to store and retrieve data due to the large accumulation of data. Due to the need to improve data availability, response time, reliability, and migration costs, the current storage engine needs to be replicated across multiple sites. When copies are properly distributed, data replication speeds up execution. The biggest challenges in data replication are choosing which data to replicate, where to put it, how to manage replication, and how many replicas it needs. Therefore, various studies have been carried out on some data mining-based data replication systems to evaluate replication issues and manage cloud storage. In most cases, data replication in a data mining environment is done using data mining along with a replication algorithm and a data grid policy. In addition, this paper addresses replica management issues and proposes affordable data replication in the cloud that satisfies all Quality of Service (QoS) requirements.

Location privacy protection of maritime mobile terminals

Mobile Edge Computing (MEC) can support various high-reliability and low-delay applications in Maritime Networks (MNs). However, security risks in computing task offloading exist. In this study, the location privacy leakage risk of Maritime Mobile Terminals (MMTs) is quantified during task offloading and relevant Location Privacy Protection (LPP) schemes of MMT are considered under two kinds of task offloading scenarios. In single-MMT and single-time offloading scenario, a dynamic cache and spatial cloaking-based LPP (DS-CLP) algorithm is proposed; and under the multi-MMTs and multi-time offloading scenario, a pseudonym and alterable silent period-based LPP (PA-SLP) strategy is proposed. Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy. Meanwhile, extending the alterable silent period, increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP. Furthermore, the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.

Comparison of the Sustainability Effects of High-Intensity Interval Exercise and Moderate-Intensity Continuous Exercise on Cognitive Flexibility.

This study examined the immediate and sustained effects of high-intensity interval exercise (HIIE) and moderate-intensity continuous exercise (MICE) on cognitive flexibility in young adults. Participants (n = 56) engaged in (1) a session of HIIE, involving 10 sets of one-minute treadmill running at an intensity targeting 90% heart rate reserve (HRR) interspersed with self-paced walking at 50% HRR; (2) a session of MICE, involving a 20 min treadmill running at an intensity of 40–59% HRR; and (3) a control session, involving 24 min of resting on separate days in a counterbalanced order. Using a more-odd shifting task, cognitive flexibility was assessed before the intervention (t0), immediately after the session (t1), and then at 30 min (t2) after the session. During the more-odd shifting task, the switch cost of response time (RT) immediately after the HIIE was significantly reduced compared to that before exercise, suggesting beneficial effects on cognitive flexibility. Additionally, the impacts of HIIE were maintained for 30 min post-exercise. However, improved cognitive flexibility was not observed until 30 min after the MICE intervention. HIIE might represent a time-efficient approach for enhancing cognitive flexibility.

Contextual cueing is not flexible

Target detection is faster when search displays repeat, but properties of the memory representations that give rise to this contextual cueing effect remain uncertain. We adapted the contextual cueing task using an ABA design and recorded the eye movements of healthy young adults to determine whether the memory representations are flexible. Targets moved to a new location during the B phase and then returned to their original locations (second A phase). Contextual cueing effects in the first A phase were reinstated immediately in the second A phase, and response time costs eventually gave way to a repeated search advantage in the B phase, suggesting that two target-context associations were learned. However, this apparent flexibility disappeared when eye tracking data were used to subdivide repeated displays based on B-phase viewing of the original target quadrant. Therefore, memory representations acquired in the contextual cueing task resist change and are not flexible.

Capillary Electrophoresis Instruments for Medical Applications and Falsified Drug Analysis/Quality Control in Developing Countries.

The implementation of integrated analytical techniques to meet stringent requirements in the life sciences requires a well-developed analytical capacity. New technology in analytical equipment for the analysis of large and small molecules is continuously being developed. However, developing countries frequently struggle to keep pace with technological advancements. Hence, it is of utmost importance to better invest in optimizing existing and proven methodologies to tackle life-saving challenges in developing countries. In this regard, capillary electrophoresis is a promising candidate for solving multiple analytical problems compared to its chromatographic and spectroscopic counterparts due to its fast analytical response time and notable cost efficiency. In the following, we summarize various issues and opportunities for capillary electrophoresis to be the technique of choice for the unresolved bottlenecks in analytical equipment in developing countries for drug quality control. This perspective demonstrates that the ongoing quest for the design of new, impactful analytical techniques is a dynamic and rapidly developing research area and mentions some directions and opportunities that have arisen during the recent pandemic.