Minimize Resource Consumption Research Articles

Effective-Capacity-Based Resource Allocation for End-to-End Multi-Connectivity in 5G IAB Networks

In a 5G integrated backhaul and access (IAB) network, an IAB-donor and multiple IAB-nodes form a multi-hop wireless backhaul network. When a terminal is connected to an IAB-node, the traffic flows generated by the terminal can be backhauled to the IAB-donor. Due to the mobility of terminals, access links are error-prone and cause difficulty in ensuring end-to-end quality of service (QoS) of traffic flows. To improve access link reliability, multi-connectivity is considered in IAB, i.e., a terminal is connected to multiple IAB-nodes. However, to ensure end-to-end QoS, multi-connectivity must be considered together with multi-hop backhauling. Thus, an effective-capacity based resource allocation (eReal) scheme is developed to establish end-to-end multi-connectivity. The objective of the scheme is to guarantee end-to-end QoS and ensure outage probability is below the given threshold. Since traffic flows are classified into the guaranteed bit rate (GBR) and the non-GBR types, the scheme is formulated as two joint route selection and resource allocation problems to provide differentiated services with minimum resource consumption. Since both problems are NP-hard, they are solved using column generation. Performance results show that eReal serves various traffic flows with over 95% QoS guarantees and also significantly outperforms existing schemes.

IMPACT OF THE PRECISION BEEKEEPING ON THE LIVING ENVIRONMENT

Bees are insects of great importance to humanity and the living environment. Bees are the main pollinators and plants are dependent on them. Consequently, global food production is dependent on bees and their work. In order to have a healthy living environment, it is of great importance for humans to help bees to be as healthy and productive as possible. The relationship between humans and bees is more than 9000 years old, but beekeeping principles and tools invented more than 200 years ago are still in use. The new principle that taking momentum in the beekeeping industry is Precision Beekeeping (PB). This is an apiary management strategy based on the monitoring of individual honeybee colonies to minimize resource consumption, maximize the productivity of bees and improve the quality of the living environment. In this research, we analyze the state-of-the-art systems for Precision Beekeeping. We make a comparative analysis of commercial solutions for precision beekeeping, with the goal to understand and determine the impact of device features on honeybees and the living environment.

An FPGA Accelerator for High-Speed Moving Objects Detection and Tracking With a Spike Camera.

Ultra-high-speed object detection and tracking are crucial in fields such as fault detection and scientific observation. Existing solutions to this task have deficiencies in processing speeds. To deal with this difficulty, we propose a neural-inspired ultra-high-speed moving object filtering, detection, and tracking scheme, as well as a corresponding accelerator based on a high-speed spike camera. We parallelize the filtering module and divide the detection module to accelerate the algorithm and balance latency among modules for the benefit of the task-level pipeline. To be specific, a block-based parallel computation model is proposed to accelerate the filtering module, and the detection module is accelerated by a parallel connected component labeling algorithm modeling spike sparsity and spatial connectivity of moving objects with a searching tree. The hardware optimizations include processing the LIF layer with a group of multiplexers to reduce ADD operations and replacing expensive exponential operations with multiplications of preprocessed fixed-point values to increase processing speed and minimize resource consumption. We design an accelerator with the above techniques, achieving 19 times acceleration over the serial version after 25-way parallelization. A processing system for the accelerator is also implemented on the Xilinx ZCU-102 board to validate its functionality and performance. Our accelerator can process more than 20,000 spike images with 250 × 400 resolution per second with 1.618 W dynamic power consumption.

Highly Scalable and Secure Kubernetes Multi Tenancy Architecture for Fintech

The study also presents a Kubernetes multi-tenancy design that is highly scalable and secure for FinTech applications. Kubernetes’ ability to manage the containers well makes it perfect for the financial institutions that need strong infrastructure solutions. Some of the main issues like scalability, security and compliance are targeted in the architecture. For the purpose of tenant isolation it uses mechanisms such as namespaces, network policies, and role-based access control while for dynamic resource allocation and the improved security depth it uses mechanisms like pod security policies. Performance and security analysis shows satisfactory results, which proves that the chosen architecture helps to solve multifaceted FinTech problems with minimal consumption of resources and low expenses

Strategies to Reduce the Impact of Resource Consumption in the Ghanaian Construction Industry

With a growing priority on resolving environmental issues and managing resources more effectively in line with Sustainable Development Goal 12, this research aims to assess solutions to minimize resource consumption in the Ghanaian construction sector. In the Ghanaian cities of Accra and Kumasi, questionnaires were utilized to solicit responses from construction stakeholders. Mean score ranking was used to rank these techniques. A reliability analysis using Cronbach's alpha coefficient found a high level of internal consistency. A high level of agreement was found after testing using Kendall's concordance. A one-sample t-test was also employed to examine the relative importance of the variables. Properties should be built to be disassembled; sections of existing building structures should be reused; prefabricated components should be utilized for on-site assembly; demolition components should be re-used or recycled, and existing buildings should be renovated to prevent destruction. This research is valuable because it adds to a checklist of measures for minimizing the effect of resource consumption in the Ghanaian construction sector, as well as to the achievement of the SDG goal. Project managers, architects, engineers, subcontractors, and other key stakeholders are encouraged to use innovative approaches to decrease resource consumption.

Regenerative urbanism: a causal layered analysis

PurposeThis paper aims to analyse both traditional and regenerative fields across four layers, litany, systems, worldviews and myth/metaphor. It aims to provide in-depth insight into the beliefs, values epistemologies and assumptions that scaffold thinking and practice. As a result of this analysis, future implications for regenerative urban practice are also considered.Design/methodology/approachPrevailing sustainability approaches seek to mitigate further harm in urban centres by increasing efficiency and minimising resource consumption and impact. They are primarily underpinned by a reductionist worldview that separates human objectives from those of the natural world. In contrast, regenerative approaches to urban sustainability have emerged out of an ecological worldview and aim to achieve net positive outcomes as a result of co-evolutionary relationships between social and ecological systems. This paper explores both approaches in urban communities through futures thinking tool, causal layered analysis.FindingsAs a result of the causal layered analysis undertaken, this paper provides insights into regenerative thinking and practice in urban settings. These insights cover four main thematic categories: purpose, place, practice and progress. Moving to the deeper layers of worldview and myth metaphor analysis, in particular, has significant implications for ongoing practice, including facilitating processes by which communities can reflect upon, unpack and reconstruct their concepts of future “success”.Originality/valueAnthropogenic climate change continues to deliver worsening ecological, social and economic impacts globally. Urban centres are particularly central to this crisis given their massive resource consumption and rapid population growth. This paper provides an alternative, deep analysis to consider thinking and practice required for urban regeneration. It reveals the need for a shift in purpose and a deeper understanding of place, illustrating the roles that futures tools may place in this transition.

Dynamic Scheduler for WSN Applications in Precision Agriculture

Sensors may be configured to collect the various parameters those do not change over a long interval (i.e. water level, soil attributes/ fertilizer quantity of agriculture land/ growth of crops). In case, if all sensors start transmission after a lengthy intermission, it may cause packet drop over network and excessive resources may be exhausted due to retransmission. To manage the bulk data forwarding with minimum resource consumption, in this paper, an application scheduler is introduced to enforce the discipline bulk data transmission. Its performance is analyzed using LEACH protocol under the constraints of various parameters (Throughput/remaining energy/number of dead/alive sensors) with sensor density (100-300).

Smart Bus Terminus: Sustainable Urban Public Transport Solution Through BIM and GIS Integrated Approach

Smart city / digital city is engaging in Big Data which will be utilized in monitoring and optimizing the urban systems to minimize resource consumption and maximize the quality of life. To implement the concept of “smart cities”, smart city components, design innovation and prototypes must be studied in detail which will be beneficial in understanding the features. India is moving towards becoming one of the developed nations, the source of revenue is highly dependent on public contributions, especially through the transportation sector. The population of India is majorly comprised of the middle-class income category. Their livelihood mainly depends on the regional level jobs and for commute, they prefer public transportation especially Buses. For lodging these buses that are huge in number, Bus Terminus become an integral infrastructure component. In Indian cities, the existing bus terminus appears to be poorly designed and highly congested irrespective of their sizes. Interventions of modern methodologies to improve the conditions in the existing bus terminus in various Indian cities and reconstructing them is of utmost priority for urban planners and policymakers to reach sustainable cities. The objective of adopting smart innovative solutions in Bus Terminus is to reduce pollution, congestion of traffic, improve safety, time optimization and cost reduction. Modern bus terminus must accommodate some key features such as green buildings and utilization of eco-friendly materials for construction, incorporating real-time response, installing solar panels for energy efficiency and power generation, waste segregation and recycling, the hybrid modal form of transport i.e., both traditional and electric vehicles, etc. In the present study, the attempt has been made to develop an artistic view of a “Smart Bus Terminus” prototype that can be adopted at regional and global levels. Autodesk REVIT Architecture and AutoCAD have been used to design and create 3D models; Geographical Information System tools have been adopted for the recommendation of site selection.

A recursive time aggregation-disaggregation heuristic for the multidimensional and multiperiod precedence-constrained knapsack problem: An application to the open-pit mine block sequencing problem

A recursive time aggregation-disaggregation (RAD) heuristic is proposed to solve large-scale multidimensional and multiperiod precedence-constrained knapsack problems (MMPKP) in which a profit is maximized by filling the knapsack in multiple periods while satisfying minimum and maximum resource consumption constraints per period as well as precedence constraints between items. An important strategic planning application of the MMPKP in the mining industry is the well-known open-pit mine block sequencing problem (BSP). In the BSP, a mine is modeled as a three-dimensional grid of blocks to determine a block extraction sequence that maximizes the net present value while satisfying constraints on the shape of the mine and resource consumption over time. Large real-life instances of this problem are difficult to solve, particularly with lower bounds on resource consumption. The advantage of the time aggregation-disaggregation heuristic over a rolling-horizon-based time decomposition is twofold: first, the entire horizon is considered for the resource consumption from the first aggregation; and second, only two-period subproblems have to be solved. This method is applied to a well-known integer programming model and a variant thereof in which blocks can be extracted in parts over multiple periods. Tests on benchmark instances show that near-optimal solutions for both of the models can be obtained for extremely large instances with up to 2,340,142 blocks and 10 periods.

Genome-Scale Metabolic Model's multi-objective solving algorithm based on the inflexion point of Pareto front including maximum energy utilization and its application in Aspergillus niger DS03043.

The solution of Genome-Scale Metabolic Model (GSMM) directly affects the simulation accuracy of the metabolic process in digital cells. Single-objective optimization methods, such as flux balance analysis (FBA), which is widely used in solving GSMM, have limitations when simulating actual biological processes, which leads to unrealistic results due to other biological constraints being ignored. A novel multi-objective differential evolution algorithm based on general FBA (i.e., differential evolution FBA [DEFBA]) is hence proposed to solve GSMM. First, in accordance with the assumption that cells minimize resource consumption and maximize resource utilization, the maximum specific growth rate and the minimum cellular production rate of ATP, NADPH, and NADH are defined as the multi-objective functions of DEFBA. Second, FBA is used to produce the initial individuals of DEFBA by changing the upper bound of biomass reaction in GSMM. Third, mutation and selection operations help in generating new individuals in the solution space to search the Pareto front. Finally, the optimal solution is selected by analyzing the inflexion point of the Pareto front. In DEFBA, multi-objective technology and optimal solution judging technology can introduce the biological constraints into the GSMM solving method, such that the solution can be more consistent with the essential biological mechanism. DEFBA is applied to solve Aspergillus niger's GSMM. The improved results show that DEFBA can be an effective general solving algorithm for GSMM.

Intelligent Tracking and Control of Resource Consumption in Manufacturing

The article examines the problems of learning and adaptation in control of production resource efficiency. The minimum consumption of resources leads to the greatest efficiency. The possibilities of decreasing resource consumption depend on random factors. In the face of such uncertainty, learning and adaptation can be used to control to reduce resource consumption. In this case, it is necessary to take into account the activity of the elements of manufacturing system associated with the presence of their own goals, which do not necessarily coincide with the goal of the system as a whole. These elements can influence adaptation and learning procedures to achieve their goals. The research is carried out on a three-level model of manufacturing. At its top level is a boss who evaluates the foreman who is at the middle level of the system. At the bottom of the system is the worker. The boss must to control the efficiency of the foreman in such a way as to save resources to a minimum. But the foreman knows own capabilities better than the boss. In turn, the worker knows his own resource-saving potential better than the foreman. So both the foreman and the worker can manipulate their resources consumption to gain more stimuli. To avoid such manipulation under conditions of uncertainty, mechanism of intelligent tracking and decision-making for resource efficiency of manufacturing is proposed. This mechanism includes procedure for digital self-adaptation of boss and procedure for assessing of the foreman. Sufficient conditions of the optimality of this mechanism are found, in which random opportunities of decreasing resource consumption are fully used. With such a mechanism, the foreman is interested in improving the worker's efficiency. It is proved that for this it is sufficient to use linear procedures of adaptive planning and stimulation of worker. The proposed approach to intelligent tracking and decision-making for resource efficiency is illustrated by the example of water consumption at the refit of locomotives in the depot of the Russian Railways company.

Serverless Geospatial Data Processing Workflow System Design

Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.

Methodology for Ensuring a Comfortable Microclimate State in a Smart Home Using an Ensemble of Fuzzy Artificial Neural Networks

Analysis of the application of smart home technology indicates an insufficient level of controllability of its infrastructure, which leads to excessive consumption of energy and information resources. The problem of managing the digital infrastructure of human living space, is associated with a large number of highly specialized solutions for home automation, which complicate the management process. Smart home is considered as a set of independent cyber-physical devices aimed at achieving its goal. For coordinated work of cyber-physical devices it is proposed to provide their joint work through a single information center. Simulation of device operation modes in a digital environment preserves the resource of physical devices by making a virtual calculation for all possible variants of interaction of devices between themselves and the physical environment. A methodology for controlling the microclimate of a smart home using an ensemble of fuzzy artificial neural networks is developed, with the example of joint use of air conditioning, ventilation and heating. The neural network algorithm allows you to monitor the parameters of the physical environment, predict the modes of cyber-physical devices and generate control signals for each of them, ensuring the joint operation of devices with minimal resource consumption and information traffic. A variant of practical implementation of a smart home climate control system on the example of a multifunctional educational computer class is proposed. Hybrid neural networks of air conditioning, ventilation and heating systems were developed. The testing of the microclimate control system of a multifunctional university classroom using hybrid neural networks was carried out, a programmable logic controller of domestic production was used as a control device. The goal of management based on cooperating cyber-physical devices is to achieve a minimum of power and information traffic when they work together.

Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements

Introspection mechanisms are employed in agent architectures toimprove agent performance. However, there is currently no approach tointrospection that makes automatic adjustments at multiple levels inthe implemented agent system. We introduce our novel multi-levelintrospection framework that can be used to automatically adjustarchitectural configurations based on the introspection results at theagent, infrastructure and component level. We demonstrate the utilityof such adjustments in a concrete implementation on a robot where thehigh-level goal of the robot is used to automatically configure thevision system in a way that minimizes resource consumption whileimproving overall task performance.

COVID-19 and industrial waste mitigation via thermochemical technologies towards a circular economy: A state-of-the-art review

The increasing awareness of waste circular economy has motivated valorization strategies for minimizing resource consumption and waste production in the private sector. With the rise of various industrial wastes and with the emergence of COVID-19 wastes, a sustainable approach is needed to mitigate the growing concern about wastes. Thermochemical treatment technologies in the form of direct combustion, torrefaction, pyrolysis, and gasification have been identified to have vital roles in the value-creation of various waste streams. Moreover, the alignment of thermochemical processes for waste mitigation concerning the circular economy framework needs to be established. Accordingly, a comprehensive review of the different thermochemical treatment options for industrial and the novel COVID-19 medical wastes streams is conducted in this study. This review focuses on highlighting the instrumental role of thermochemical conversion platforms in achieving a circular economy in the industrial sector. Various strategies in waste mitigation through various thermochemical processes such as management, recovery, reduction, and treatment are discussed. The results show that thermochemical technologies are beneficial in addressing the sustainability concerns on mitigating wastes from the industrial sector and wastes brought by the COVID-19 pandemic. This also includes the current issues faced as well as future perspectives of the thermochemical conversion technologies.

Heterogeneous Stream Scheduling for Cross-Modal Transmission

Cross-modal communication is playing an increasingly important role in improving receivers' immersive experience. The main challenge lies in ensuring the heterogeneous requirements of the cross-modal stream. Especially, the discontinuity of the received haptic signals caused by the delay should be eliminated. Unfortunately, existing schemes study the cross-modal stream separately, which leads to that the haptic signal is distorted, or the audio-visual quality is reduced. To solve this problem fundamentally, we propose a joint transmission framework combining prediction and device-to-device (D2D) communication by taking advantage of the correlation of the haptic signals and the proximity feature of the receivers. Specifically, on the theoretical end, to completely eliminate the discontinuity, we propose a prediction mechanism by predicting and sending the future signals in advance. To compensate for the reliability loss brought by prediction, D2D links are efficiently established on the receivers' side. On the technical end, we first design a minimum resource (e.g., power) consumption search algorithm based on the binary search to obtain the optimal prediction horizon. Moreover, we develop a simple but efficient transmission mode selection algorithm based on the Hungarian algorithm. Experimental results demonstrate the advantages of our proposed scheme in saving the power consumption.

Navigating the shifting sands of filtering facepiece respirator provision during the COVID-19 pandemic: a system response for maximising staff safety

IntroductionPersonal protective equipment is essential to protect health workers and patients and to ensure confidence when dealing with aerosolised disease transmission. We describe the process for ensuring adequate filtering facepiece...

RESFIT: A Reputation and Security Monitoring Platform for IoT Applications

The fast-paced adoption of smart devices has changed the Internet of Things (IoT) landscape, leading to the growth of smart environments and inclusion in many aspects of our society. In IoT applications, data collected from sensors and mobile devices are aggregated, processed, and analyzed to extract useful information and develop intelligent services. If the collected data is not trustworthy due to the damage or malicious input of some sensors, the quality of the service will be impacted. For reliable data collection and mining, it is mandatory to define robust security and trust models, suitable for the IoT application context. In this paper, we propose RESFIT, a platform that implements a reputation-based trust mechanism and an advanced application level firewall to cope with the above mentioned issues. Having a gateway-centric architecture, the proposed platform ensures minimal resource consumption at the node layer, and an integrated overview and control of the system state, through the cloud component and smartphone management application.

Effect of Process Parameters on Energy Consumption, Physical, and Mechanical Properties of Fused Deposition Modeling.

The application of the fused deposition modeling (FDM) additive manufacturing process has increased in the production of functional parts across all industries. FDM is also being introduced for industrial tooling and fixture applications due to its capabilities in building free-form and complex shapes that are otherwise challenging to manufacture by conventional methods. However, there is not yet a comprehensive understanding of how the FDM process parameters impact the mechanical behavior of engineered products, energy consumption, and other physical properties for different material stocks. Acquiring this information is quite a complex task, given the large variety of possible combinations of materials–additive manufacturing machines–slicing software process parameters. In this study, the knowledge gap is filled by using the Taguchi L27 orthogonal array design of experiments to evaluate the impact of five notable FDM process parameters: infill density, infill pattern, layer thickness, print speed, and shell thickness on energy consumption, production time, part weight, dimensional accuracy, hardness, and tensile strength. Signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) were performed on the experimental data to quantify the parameters’ main effects on the responses and establish an optimal combination for the FDM process. The novelty of this work is the simultaneous evaluation of the effects of the FDM process parameters on the quality performances because most studies have considered one or two of the performances alone. The study opens an opportunity for multiobjective function optimization of the FDM process that can be used to effectively minimize resource consumption and production time while maximizing the mechanical and physical characteristics to fit the design requirements of FDM-manufactured products.

Training Accelerator for Two Means Decision Tree

Decision trees (DTs) are profusely used in machine learning (ML) applications on account of their fast execution and high interpretability. As DT training is time-consuming, in this brief, we proposed a hardware training accelerator to speedup the training process. The proposed training accelerator is implemented on the field-programmable gate array (FPGA) having a maximum operating frequency of 62 MHz. The proposed architecture uses a combination of parallel execution for training time reduction and pipelined execution to minimize resource consumption. For a given design, the proposed hardware implementation is found to be at least $14\times $ faster than the C-based software implementation. Moreover, the proposed architecture can be easily retrained for the next set of data using a single RESET signal. This on-the-go training makes the hardware versatile for any kind of application.