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With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

The papermaking industry always causes many disastrous problems to humans, such as energy consumption, environmental pollution and destruction to the ecosystem. However, paper is not only one kind of life's necessities but also a necessity of the office. But the service life of paper is reduced easily because of the invasion of water. Here we creatively prepared one kind of multifunctional inner and outer uniform-superhydrophobic paper by the secondary use of waste paper inspired by nature and traditional papermaking knowledge, which not only has a great water proof ability for various liquids, but also has wonderful self-cleaning, anti-fouling and oil absorption abilities. It is worth mentioning that superhydrophobic recycled paper still has good writability, suppleness, foldability and tailorability to meet our daily needs. Unique outer and inner uniform-superhydrophobicity make this paper able to tolerate any degree of abrasion. This groundbreaking work will not only avoid harm by the water invasion and expand the usable range of paper, but also it will ease the energy and environment crisis.

The paper industry is the industry with the third highest energy consumption in the European Union. Using recycled paper instead of fresh fibers for papermaking is less energy consuming and saves resources. However, adhesive contaminants in recycled paper are particularly problematic since they reduce the quality of the resulting paper-product. To remove as many contaminants and at the same time obtain as many valuable fibres as possible, fine screening systems, consisting of multiple interconnected pressure screens, are used. Choosing the best configuration is a non-trivial task: The screens can be interconnected in several ways, and suitable screen designs as well as operational parameters have to be selected. Additionally, one has to face conflicting objectives. In this paper, we present an approach for the multi-criteria optimization of pressure screen systems based on Mixed-Integer Nonlinear Programming. We specifically focus on a clear representation of the trade-off between different objectives.

To improve energy efficiency of a Paper & Pulp industry, full capacity utilization of the processing equipment and diverse energy source utilities are promoted, while same time processing of diverse raw materials, meeting of strict processing time requirements, seamless material flow and obtaining required product quality are important concerns to be fulfilled. This is similar to a resources management problem while energy is considered as one of the measurable resources, wherein estimation of energy consumptions is crucial/complicated aspect. Discretized form of energy consumption (approximate energy consumed by unit amount of intermediate products at different intermediate processing stages) concept is utilized here with discrete event simulation for consumed energy estimation/analysis. The research deals with a real time working Paper & Pulp industry resource management problem, wherein overall energy consumption rate and production rate were analyzed for some commonly occurring situation of efficiency reduction of the processing station(s). Energy efficient solutions are evaluated with varying production flow (varying the percentage of raw material) and resources management options (turning on/off paper machines). Energy efficient solution which ensures minimum loss in production rate and minimizes disturbances in material flow is suggested for implementation.

Reversible computing is a version of computing in which the computational system to a degree is reversible. An essential circumstance for reversibility of a computational version is that the relation of the mapping states of transition features to their successors have to always be one-to-one. Reversible logic has emerged as an change design technique to the conventional logic, ensuing in decrease energy consumption and lesser circuit area. In this paper, a review of an efficient architecture of the reversible FIR filter structure is presented. For reaching low electricity, reversible good judgment mode of operation is implemented inside the design. Vicinity overhead is the tradeoff inside the proposed layout. From the synthesis consequences, the proposed low electricity FIR filter architecture offers power saving when as compared to the conventional layout. The vicinity overhead is for the proposed structure.

Wireless sensor network is vast area for research. Because of scarce resources, energy consumption and battery lifetime is crucial aspects in WSN. So with the help of routing protocol, improvement can be done. More energy can be conserved and network lifetime can be improved by modifying the existing protocols. Power Efficient GAthering in Sensor Information System (PEGASIS) is an improvement of hierarchical protocol. It mainly focuses on network lifetime. It uses chaining construction and data gathering approach to communicate. Aggregated data will pass on to the head node and it will pass the data to the base station. This paper introduces the PEGASIS, discusses the related previous work. New aspect of PEGASIS in heterogeneous environment is proposed. The new scheme is proposed in order to conserve energy.

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where providing electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs. In this work, we present a survey of power saving and energy optimization techniques for wireless sensor networks, which enhances the ones in existence and introduces the reader to the most well known available methods that can be used to save energy. They are analyzed from several points of view: Device hardware, transmission, MAC and routing protocols.

Due to the extensive energy consumption and high population density in modern cities, the collection and use of scattered walking energy from the stream of people is crucial for the development of a green ecological city. Herein, a flexible undulated electrode-based triboelectric nanogenerator (u-TENG) was integrated to the floor to scavenge walking energy from pedestrians, promoting the ordered collection of disordered and scattered energy. Driven by the steps of human walking, the output of the as-fabricated u-TENG are an open-circuit voltage of 86 V and a short-circuit current of 6.2 μA, which are able to continuously light up 110 light-emitting diode bulbs. In addition, a self-powered location-tracking system was prepared for pedestrian volume counting and passenger tracing with the purpose of reducing energy consumption in public areas. The proposed walking energy harvesting device is flexible, feasible, and unaffected by season, climate, or location. This work not only proposes a strategy for mechanical energy harvesting in public areas, including subway stations, hospitals, shopping malls, and business streets, but also offers a novel solution for smart cities and low-carbon transportation alternatives.

As modern embedded systems are becoming more and more ubiquitous and interconnected, they attract a world-wide attention of attackers and the security aspect is more important than ever during the design of those systems. Moreover, given the ever-increasing complexity of the applications that run on these systems, it becomes increasingly difficult to meet all security criteria. While extra-functional design objectives such as performance and power/energy consumption are typically taken into account already during the very early stages of embedded systems design, system security is still mostly considered as an afterthought. That is, security is usually not regarded in the process of (early) design-space exploration of embedded systems, which is the critical process of multi-objective optimization that aims at optimizing the extra-functional behavior of a design. This position paper argues for the development of techniques for quantifying the ’degree of secureness’ of embedded system design instances such that these can be incorporated in a multi-objective optimization process. Such technology would allow for the optimization of security aspects of embedded systems during the earliest design phases as well as for studying the trade-offs between security and the other design objectives such as performance, power consumption and cost.

The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Background: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.

Energy consumption has become a first-class optimization goal in computing system design and implementation. Database systems, being a major consumer of computing resources (thus energy) in modern data centers, also face the challenges of going green. In this position paper, we describe our vision on this new direction of database system research, and report the results of our recent work on this topic. We describe our ideas on the key issues in designing a power-aware DBMS and sketch our solutions to such issues. Specifically, we believe that the ability for the DBMS to dynamically adjust various knobs to satisfy energy (and performance) goals is the main technical challenge in this paradigm. To address that challenge, we propose dynamic modeling and tuning techniques based on formal feedback control theory. Our preliminary data clearly show that the energy savings can be significant.

In order for wireless body area networks to meet widespread adoption, a number of security implications must be explored to promote and maintain fundamental medical ethical principles and social expectations. As a result, integration of security functionality to sensor nodes is required. Integrating security functionality to a wireless sensor node increases the size of the stored software program in program memory, the required time that the sensor's microprocessor needs to process the data and the wireless network traffic which is exchanged among sensors. This security overhead has dominant impact on the energy dissipation which is strongly related to the lifetime of the sensor, a critical aspect in wireless sensor network (WSN) technology. Strict definition of the security functionality, complete hardware model (microprocessor and radio), WBAN topology and the structure of the medium access control (MAC) frame are required for an accurate estimation of the energy that security introduces into the WBAN. In this work, we define a lightweight security scheme for WBAN, we estimate the additional energy consumption that the security scheme introduces to WBAN based on commercial available off-the-shelf hardware components (microprocessor and radio), the network topology and the MAC frame. Furthermore, we propose a new microcontroller design in order to reduce the energy consumption of the system. Experimental results and comparisons with other works are given. © 2011 Springer Science+Business Media, LLC.

Motivation MinION is a portable nanopore sequencing device that can be easily operated in the field with features including monitoring of run progress and selective sequencing. To fully exploit these features, real-time base calling is required. Up to date, this has only been achieved at the cost of high computing requirements that pose limitations in terms of hardware availability in common laptops and energy consumption. Results We developed a new base caller DeepNano-coral for nanopore sequencing, which is optimized to run on the Coral Edge Tensor Processing Unit, a small USB-attached hardware accelerator. To achieve this goal, we have designed new versions of two key components used in convolutional neural networks for speech recognition and base calling. In our components, we propose a new way of factorization of a full convolution into smaller operations, which decreases memory access operations, memory access being a bottleneck on this device. DeepNano-coral achieves real-time base calling during sequencing with the accuracy slightly better than the fast mode of the Guppy base caller and is extremely energy efficient, using only 10 W of power. Availability and implementation https://github.com/fmfi-compbio/coral-basecaller Supplementary information Supplementary data are available at Bioinformatics online.

In recent years, fast computation, low power, and small footprint are the key motivations for building SNN hardware. The unique features of SNN hardware have not been fully exploited, where the computation speed and energy efficiency of the SNN hardware can be improved according to the sparse spiking and non-uniform traffic of SNN. In this paper, we propose a 5.28-mm² 4096-neuron 1M-synapse energy-efficient digital SNN hardware that can achieve ultra-low energy per synaptic operation of 4.5 pJ. The proposed neuron computing unit is implemented in pipeline architecture to achieve high synaptic processing speed. The proposed spike processing unit can significantly increase the processing speed of the neuron core by 1.9x and 9.4x when the spike injection rate is 50% and 10%, respectively. Besides, the increase in the processing speed of the neuron core leads to a reduction in energy consumption of up to 81.5%. An event-driven clock gating circuit that can reduce the power consumption of the proposed neuron block by more than 70% is proposed in this paper. This paper proposes a supervised STDP+ algorithm for SNN training, and the classification accuracy of the MNIST digits is 89.6% with 73.6% weight sparsity of the output layer.

DRAM devices require periodic refresh operations to preserve data integrity. Slowing down the refresh rate can reduce the energy consumption; however, it may cause a loss of data stored in the DRAM cell. This paper proposes a new memory architecture of soft approximation for deep learning applications, which reduces the refresh energy consumption while maintaining accuracy and high performance. Utilizing the error-tolerant property of deep learning applications, the proposed memory architecture avoids the accuracy drop caused by data loss by flexibly controlling the refresh operation for different bits, depending on their criticality. For data storage, the approximate DRAM architecture reorganizes the data so that these data are sorted according to their bit significance. Critical bits are stored in more frequently refreshed devices while non-critical bits are stored in less frequently refreshed devices. In addition, for further reduction of the DRAM energy consumption, this paper combines hard approximation, which reduces the number of accesses to DRAM, with soft approximation. Simulation results show that the refresh energy consumption is reduced by 69.71%, and the total energy consumption is reduced by 26.0 % for the hybrid memory with a negligible drop in both training and testing phases on state-of-the-art deep networks.

This paper presents an energy-efficient spectral analysis method for the Internet of Things (IoT). The objective of this paper is to reduce the energy consumption of edge devices. The proposed method uses an autoregressive (AR) model for spectral analysis instead of the discrete Fourier transform, and its calculation process is distributed to the edge device and a base station by considering the energy consumption tradeoff of the data processing and the data communication. In this paper, the Yule-Walker method is employed for the AR coefficient calculation. The calculation process of Yule-Walker method can be divided into two parts: an autocorrelation calculation and an AR coefficient calculation. The autocorrelation calculation is implemented in the edge devices, and its dedicated hardware is designed using Verilog HDL. Meanwhile, the AR coefficient is calculated in the base station and is used for the spectral analysis. According to this distributed processing approach, the energy consumption of the edge device can be reduced compared with conventional DFT approaches using the fast Fourier transform (FFT). The system level energy consumption is evaluated assuming the IoT edge device, which has a wireless transceiver using Bluetooth low energy. The evaluation results show that the proposed method can reduce 79% of the edge device energy consumption for spectral analysis in a practical application.

Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low memory killer. These memory management methods are fully functioning. However, Android phones cannot swap memory into solid-state drives, thus slowing the processor and reducing storage lifetime. In addition, the compression and decompression processes consume additional energy and latency. Therefore, the CompCache requires an extension. An extended Android CompCache using a graphics processing unit to compress and decompress memory pages on demand and reduce the latency is introduced in this paper. This paper characterizes each data compression and decompression utility by measuring compression ratio, compression and decompression throughput, and energy efficiency to validate the process. Experimental results prove that data compression and decompression utilities can be beneficial to reduce the latency and perform faster compression and decompression compared with existing approaches

The control cost is an important and inevitable problem for network control. In existing work on finite-time (FnT) synchronization on stochastic multilayer networks (MNs), the pth moment synchronization based on energy consumption estimation has not been involved. The paper firstly proposes two new FnT pth moment quasi-stability theorems of stochastic systems, which expand the existing value range of p. Second, novel sufficient criteria are obtained for FnT pth moment quasi-synchronization of stochastic MNs. Compared with some existing FnT synchronization of stochastic MNs, the paper not only gives the range of the number of layers about MNs under the minimum convergence time, but also gives the estimation of energy consumption. Finally, numerical simulation shows that the theoretical analysis is effective.

In this paper, we address the challenges of frequent backup and recovery operations associated with an unstable energy supply in energy-harvesting nonvolatile processors (NVPs). To address this problem, this paper adopts a retention state, which can allow the system to retain data in a volatile cache to wait for power recovery instead of immediately backing up the data. In addition, in an energy-harvesting NVP, important issues include how to back up and recover data, how to perform cache replacement, and how to transmit data to different parts of the cache in such a way that the performance and energy consumption can be improved and a successful backup can be guaranteed. Therefore, we propose a performance-oriented cache management scheme based on a retention state to generate near-optimal data migration, replacement and backup solutions for applications in polynomial time. This scheme uses two energy thresholds to pre-back up and back up data to reduce the backup and recovery operations according to the retention state. We evaluate the performance and energy consumption of our proposed algorithms in comparison with those of the dual-threshold SLC STT-RAM scheme and morphable cache scheme. The evaluation shows that the proposed scheme can achieve 18.58% and 4.54% lower average energy consumption than the SLC STT-RAM and morphable cache schemes, respectively, with comparable performance. The experimental results also show that compared with the SLC STT-RAM and morphable cache schemes, the proposed algorithm can achieve 17.90% and 8.85% performance improvement, respectively, on average.

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy. The work presented in this paper has been carried out within the framework of the project ROUTE56 (PID2019-104945GB-I00/ AEI/10.13039/501100011033) funded by the Agencia Estatal de Investigación (Spanish Ministry of Science and Innovation); the FPI grant BES-2017-079994, funded by the Spanish Ministry of Science, Innovation and Universities; and the grant 2017 SGR 578, funded by the Catalan Government (AGAUR, Departament de Rercerca i Universitats, Generalitat de Catalunya).

Devices that populate the Internet of Things (IoT) are typically constrained with respect to energy consumption. When the data that are processed, stored and/or communicated by these devices need to be secured, low-energy security mechanisms have to be designed and implemented. Related work mainly concentrates either on low-energy security algorithms and protocols, or on low-energy wireless communication. However, it is important for system developers to take into account the overall energy consumption of the IoT system when making design choices. Therefore, this work presents an in-depth analysis of the energy consumption of IoT devices that provide end-to-end secure communication and digital signatures. The paper follows a granular approach, profiling and measuring each individual contribution to the overall energy consumption, including the computation of cryptographic operations as well as the wireless transmission of messages in cryptographic protocols. The paper also calculates the minimal time period of a secure communication session in order to minimize the energy impact of the session’s setup phase and thus minimize the overall average power consumption. The goal of this work is to provide assistance in the selection of a suitable wireless communication standard and cryptographic cipher suite for building end-to-end secure IoT applications.

This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day‐ahead and real‐time basis. The main objective of the paper is to help balance the load through ON‐peak hours and OFF‐peak hours. Three price signals, namely: real‐time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real‐time does not significantly impact the cost of electricity and the peak to average ratio.

Aiming at the problems of extensive management and energy waste in the operation of shopping malls, this paper proposed a shopping mall building energy-saving diagnosis model based on an improved PSO-SVM neural network. Firstly, the operation data with good energy-saving characteristics were selected from the collected historical data of energy consumption by clustering algorithm as the data basis for the establishment of the model. Secondly, the Adaboost algorithm was used to optimize the PSO-SVM neural network to complete the model construction, and the energy-saving data was used to complete the training of the model. Finally, the judgment criteria for abnormal energy consumption were established, and the energy consumption situation at the current moment was diagnosed by using the trained energy-saving diagnosis model. The abnormal judgment criteria of energy consumption were used to analyze the diagnostic results, and the energy-saving diagnosis process was completed, which was verified by case analysis. Through case analysis and verification, the energy-saving rate of the energy-saving diagnosis model established in this paper has been increased from -1.1% to 11.9%, and the problem moments generated during the operation of air conditioning in shopping malls have been successfully diagnosed. The model established in this paper can not only find out the unreasonable conditions in the operation of the air-conditioning system, but also provide a reference for building energy-saving management and operation.

Nowadays, the reduction of CO2 emissions by moving from fossil to renewable energy sources is on the policy of many governments. At the same time, these governments are forcing the reduction of energy consumption. Since large industries have been in the focus for the last decade, today also small and medium enterprises with production lot size one are increasingly being obliged to reduce their energy requirements in production. Energy-efficient CNC machine tools contribute to this goal. In machining processes, the machining strategy also has a significant influence on energy demand. For manufacturing of lot size one, the prediction of the energy demand of a machining strategy, before a part is manufactured plays a decisive role. In numerous previous studies, analytical models between the energy demand and the machining strategy have been developed. However, their accuracy depends largely on the parameterization of these models by dedicated experiments. In this paper, different machine learning algorithms, especially variations of the decision tree (’DecisionTree’, ’RandomForest’, boosted ’RandomForest’) are investigated for their ability to predict the energy demand of CNC machining operations based on real production data, without the need for dedicated experiments. As shown in this paper, the most accurate energy demand predictions can be achieved with the ’RandomForest’ algorithm.

This paper proposes a penalty strategy with differentiated service rate based on the free riding phenomenon in P2P networks, and establishes an M/M/c+d queueing model. Based on this model, a sleep/wakeup mechanism is introduced for the peers at the service end, and a single asynchronous vacation strategy is adopted to reduce the energy consumption of the system. In addition, the energy consumption of peers in each state is quantified, and the relationship between the energy consumption and parameters of the system is analyzed. In order to avoid excessive requests for unnecessary services from requesting nodes and increasing energy consumption of the system, this paper analyzes the Nash equilibrium between the arrival rate and the net profit of a single node, and then studies the optimization of social profit. The stationary distribution of queueing model is obtained by the method of matrix geometric solution, the performance indicators of the system are constructed, and the system performance is analyzed by numerical experiments. Experimental results show that the model developed in this paper has a significant penalty effect on free riding behavior, and that the single asynchronous vacation strategy not only saves more than 10% of the total energy consumption compared with the single synchronous vacation strategy, but also makes the hybrid P2P networks more flexible and efficient.

Based on the fusion of wireless sensor modules and UHF RFID readers, this paper develops a UHF RFID reading and writing system based on wireless sensing technology and designs a communication interface circuit that integrates wireless sensing and RFID technology. The financial supervision mechanism of the Internet of Things compatible with these two technologies improves the communication distance limitation of the RFID system and improves the flexibility of the RFID reading and writing system. Secondly, in view of the security issues of WSN small and medium data distribution, this paper proposes a lightweight small data supply chain financial supervision program. The solution adopts two-way authentication technology based on the improvement of the Internet of Things supply chain, the negotiation of the Internet of Things supply chain, and a lightweight encryption algorithm, which realizes the secure communication between ordinary nodes and gateway nodes, and can more effectively solve the problem of small and medium data distribution in WSN to resist the deficiencies of the financial supervision mechanism; compared with existing solutions of the same magnitude, it has higher security and efficiency. Experimental simulation shows that an important problem in the converged network is the imbalance of node energy consumption. In order to reduce the negative impact of the imbalance of energy consumption on the network, this article compares the two for direct transmission protocol and minimum energy multihop routing protocol. From the perspective of supply chain process performance, based on the proposed technical impact framework, it analyzes how companies actually carry out ID application work, making the research from general to individual, from theory to practice. The network energy consumption calculation method under this protocol improves an improved network architecture model that increases the communication scale. The test results show that the technical impact analysis framework established in this article based on the theory of the Internet of Things and the advantages of RFID technology can analyze the automation benefits, information benefits, and transformative benefits brought by automatic identification technology to the supply chain and introduce complementary resources and potential factors. It provides a theoretical basis and operation method for measuring the impact of RFID on the performance of the financial supervision process based on the Internet of Things supply chain in practical applications.

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.

Blockchain distributed ledger technology (DLT) has widespread applications in society 5.0 because it improves service efficiency and significantly reduces labor costs. However, employing blockchain DLT entails considerable energy consumption in the mining process. This paper proposes a blockchain accelerator (BCA) with ultralow power consumption and a high processing rate to address the problem. The BCA focuses on accelerating the double secure hash algorithm (SHA) 256 function required in the mining process at a system-on-chip (SoC) level. We propose three ideas, namely, multiple local memories (multimem), double-cell processing element (D-PE), and nonce autoupdate (NAU), to reduce the external data transfer time and improve the BCA hardware efficiency. We propose a cascaded multiple BCA chip model to enhance the system throughput by several-fold. Our experiments on an ASIC and FPGA prove that the proposed BCA successfully performs the mining process for multiple blockchain networks with much lower power consumption than that of the state-of-the-art CPUs and GPUs. The BCA is laid out with Renesas 65 nm technology with a chip area of $25~mm^{2}$ and consumes $530~mW$ at 100 MHz . The power efficiency of the layout chip is improved by 2428 and 143 times compared with that of the fastest CPU Intel i9-10940X and GPU RTX 3090, respectively.

This paper discusses finite/fixed-time (FNT/FXT) synchronization of multi-layer networks based on energy consumption estimation. Firstly, a novel FXT stability theorem of dynamical system is presented. Secondly, sufficient criteria for FNT/FXT synchronization in multi-layer networks are given, and the estimation of the energy consumption are also gained. Especially, the range of the number of layers of network is gained based on the least convergence time and the estimation of the least energy consumption. The results show that shorter synchronization time and less energy consumption does not mean less network layers. Finally, the validity of the theoretical method is verified by numerical simulation.

The wireless sensor network integrates sensor technology, microelectromechanical technology, distributed information processing technology, and wireless communication technology. In order to solve this problem, this paper designs and proposes an anchor node self-location algorithm. Aiming at the positioning accuracy of wireless sensor network nodes, this paper proposes an improved algorithm for sensor network node positioning that uses error correction methods to reduce accumulated distance errors and positioning errors. In this paper, the designed routing algorithm is simulated and implemented, and the performance of the routing algorithm is evaluated based on different network topologies. From the analysis results, compared with the existing typical routing algorithms, the routing algorithms designed in this paper can effectively reduce the energy consumption of the network and prolong the lifetime of the network. The core of the algorithm is to integrate the known and available information of the system to locate unknown anchor nodes. This greatly reduces the number of anchor nodes whose initial position information is required by the system, and under the condition of less impact on the positioning accuracy of the system, the cost of the system is reduced and the scope of application of the system is improved. This paper has deeply studied the positioning and tracking problems in wireless sensor networks, including node positioning, biochemical gas source positioning, and target tracking, and designed and developed a platform for positioning and tracking application research to lay the foundation for further application research. In the study of the above problems, new methods of positioning and tracking with theoretical and practical value are proposed for different practical application scenarios, and the performance is verified and evaluated through computer simulation.

The complexity of railway vehicle structures has been part of an evolutionary process for almost two hundred years. Challenges such as increased weight, increased maintenance, higher costs and energy consumption have become common. The vision for future railway vehicles is to reduce complexity, hence enable simpler structures and reduce maintenance and cost, and of course various research challenges arise from this. In fact, a number of papers in the railway engineering literature have presented practical ways to control steering of railway vehicles to improve performance. The model of the railway wheelset is highly nonlinear, mainly due to the nature of the wheelset structure and the related wheel-rail contact forces involved during operation. In this paper, the simplest design in terms of retrofitting, the actuated solid-axle wheelset is considered, we investigate actively controlled wheelsets from a Linear Parameter Varying (LPV) control aspect. We use the grid-based LPV approach to synthesize the H∞ / LPV controller, which is self-scheduled by the forward velocity, as well as the longitudinal and lateral creep coefficients. The aim of the controller is to reduce the lateral displacement and yaw angle of the wheelset. Simulation results show that the proposed controller ensures the achievement of the above targets in the considered frequency domain up to 100 rad/s.

Facade systems have recently been transformed from simple passive elements to more advanced and complex active systems that are able to act in an adaptive manner and in response to the surrounding environment through changing buildings facades behaviour, in terms of spatial configurations or characteristics of its external skin. R esponsive facades can improve building’s energy efficiency through improving buildings energy savings by their capability to change their behaviour and/or characteristics through exploiting stimulus-responsive materials and intelligent control systems that can make a significant and valuable contribution to the success of the buildings system as a whole. This paper intends to display a brief overview of the existing different climate responsive facades strategies through displaying a number of different environmental responsive facades behaviours as well as goals that can be achieved by the applications of such responsive facades. Based on the analysis of the illustrated case studies considered in this paper, responsive behaviour of kinetic facades is categorized under two main categories in response to two main types of stimuli that usually buildings are subjected to which are solar radiation and natural air current. Each one of these types is categorized into other sub categories which vary according to a number of different factors such as the design strategy, desired function or applied technology. the applications of kinetic solar responsive facades in this research is divided into three main types, which are responding through architectonic building movement, transformable shading systems and integrated kinetic shading systems with PV cells. While natural air current and wind driven kinetic responsive facades are divided in to two main types, which are air current responsive facades and integrated air current responsive facades with wind energy collectors. Adaptive architecture generally and responsive facades strategies specifically with its wide range of applications is considered as an emergent and significant research topic, based on its capability of reducing buildings energy consumption rates drastically as well as improving the indoor living environments.