Energy Consumption Constraints Research Articles

Machine learning applications for photovoltaic system optimization in zero green energy buildings

In this paper, the energy supply of a zero-energy building with 220 square meters is considered using optimized nanocomposite solar panels with respect to maximum efficiency. An optimized hybrid machine learning method plays a key role in presenting solar panel modeling with over 0.99% accuracy. Predicting the properties of the nanomaterial solar cell in four different seasons is performed by efficient support vector machines (SVM), and k-nearest neighbors (KNN) machine learning algorithms. In addition, the KNN algorithm is optimized by the Particle Swarm Optimization (PSO) method to improve the capabilities of KNN and reveal the best performance criteria for the photovoltaic modeling characteristics. The parameters of the nanocomposite cells are optimized using the proposed novel Multidisciplinary Optimization Method (MDO) to increase the efficiency of the solar panel by up to 170%. Optimization of solar cell performance with nanocomposite material under energy consumption constraints is carried out to propose the best construction of cells with 3 layers. The presented approach as a solution and indicator for the next generation of commercial and residential buildings can increase the potential values of solar cells to at least 70%.

User Scheduling and Task Offloading in Multi-Tier Computing 6G Vehicular Network

Many real-time application scenarios are developed in 6G communications. Driven by the low-latency data processing requirements, multi-tier computing has become an important technology to improve user experience and reduce network overhead. In this paper, we consider a multi-tier computation offloading network structure for 6G applications, in which the cloud computing center and the nearby vehicle edge server (VES) are able to partially calculate the tasks offloaded from the user equipment (UE), and the remaining task is processed locally in the UE. By jointly optimizing user scheduling, cloud offloading ratio, VES offloading ratio, and VES mobility, the objective function is to minimize the delay of the system transmission and computation under the constraints of discrete variables and energy consumption. To solve the problem, a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm based on multi-tier computation offloading is proposed. Simultaneously, compared with baseline algorithms, PD-DDPG algorithm has an obvious advantage in both the speed of convergence and the system delay.

Optimization Design of Pressure Hull for Long-Range Underwater Glider Based on Energy Consumption Constraints

Underwater gliders are a class of ocean observation equipment driven by buoyancy, and their energy consumption source is mainly generated by the active regulation of buoyancy. The periodic elastic deformation of the pressure hull during the upward and downward movement of the underwater glider can have a large impact on its driving buoyancy. This paper relates the optimization problem of the pressure hull with the energy consumption of underwater glider, and the energy improvement factor is taken as the optimization objective. Based on the mechanical theory, the theoretical optimization model and constraint model are derived. A hybrid genetic simulated annealing algorithm (HGSAA) is adopted to optimize the pressure hull of the underwater glider developed by Huazhong University of Science and Technology (HUST). Additionally, the effectiveness of the optimized mathematical model and optimization results were verified by the tests. The sea trial results show that after the pressure hull optimization, the energy consumption of the buoyancy regulation unit decreased by 21.9%, and the total energy carried increased by 12.4%.

Measuring capacity utilization under the constraints of energy consumption and CO2 emissions using meta-frontier DEA: A case of China's non-ferrous metal industries

Currently, China's non-ferrous metal industry (NMI) still has the problem of overcapacity, measuring its capacity utilization (CU) and revealing its determinants is an important way to resolve the overcapacity problem of China's NMI. Using the panel data of China's 29 NMIs, this paper constructs a meta-frontier data envelopment analysis (DEA) to measure CU by taking into account the energy consumption and CO2 emissions constraints, and decompose the CU into four parts: technical efficiency, technology gap, scale efficiency, and equipment utilization. The main findings indicate that during the period of 2004–2017, the CU of China's NMI is generally low. Non-ferrous metal alloy manufacturing and rolling processing has the highest CU, while non-ferrous metals mining and dressing shows the lowest CU. From the dynamic perspective, mining and dressing experienced CU decrease, while other two sub-industries achieved CU increase. Technical inefficiency and technology gap enlargement are two main barriers of China's NMIs' CU. Besides, among 29 NMIs, there are only ten industries witnessed a CU improvement, while the remaining nineteen industries all experienced decrease of CU. CU and its determinants across China's 29 NMIs are quite different. Thus, the policies to improve CU should be made tailored to each industry's features.

Deep Reinforcement Learning‐Based UAV Path Planning for Energy‐Efficient Multitier Cooperative Computing in Wireless Sensor Networks

Benefiting from the progress of microelectromechanical system (MEMS) technology, wireless sensor networks (WSNs) can run a large number of complex applications. One of the most critical challenges for complex WSN applications is the huge computing demands and limited battery energy without any replenishment. The recent development of UAV‐assisted cooperative computing technology provides a promising solution to overcome these shortcomings. This paper addresses a three‐tier WSN model for UAV‐assisted cooperative computing, which includes several sensor nodes, a moving UAV equipped with computing resources, and a sink node (SN). Computation tasks arrive randomly at each sensor node, and the UAV moves around above the sensor nodes and provides computing services. The sensor nodes can process the computation tasks locally or cooperate with the UAV or SN for computing. In a life cycle of the UAV, we aim to maximize the energy efficiency of cooperative computing by optimizing the UAV path planning on the constraints of node energy consumption and task deadline. To adapt to the time‐varying indeterminate environment, a deep Q network‐ (DQN‐) based path planning algorithm is proposed. Simulation studies show that the performance of the proposed algorithm is better than the competitive algorithms, significantly improves the energy efficiency of cooperative computing, and achieves energy consumption balance.

Formal security analysis of an IoT mutual authentication protocol

Wireless sensor networks (WSNs) are widely used in day to day activities in order to provide users with multiple services such as smart grids, smart homes, industrial internet of things (IoT), agriculture and health-care. These services are provided by collecting and transmitting the sensing data to the gateway node over an unsafe channel, having constraints of security, energy consumption and connectivity. In 2022, Fariss et al. proposed an ECC-based mutual authentication and key agreement protocol for WSNs. They provided its informal security and showed that it’s secure against many security threats. They also formally analyzed the scheme’s security using AVISPA Tool. In this article, we analyze the security of Fariss et. Al protocol using GNY logic, an advanced version of BAN logic.

Aggregate Flexibility Capacity of TCLs With Cycling Constraints

Thermostatically Controlled Loads (TCLs), e.g., an air conditioner, typically maintain their temperature within a preset range using on/off actuation. These types of loads are inherently flexible: many different power consumption trajectories exist that can keep the temperature within range. Grid operators need tools for quantifying demand flexibility of a collection of TCLs in order to use them as a resource. However, computationally tractable characterization of flexibility capacity obtained so far has considered temperature constraints alone, ignoring their cycling/lock-out constraints: the length of time a TCL must stay in “on” stage before switching to “off,” or vice versa. In this work, we present a tractable characterization of the flexibility capacity of a collection of TCLs that incorporates not only temperature but also cycling and total energy consumption constraints. Unlike prior attempts at capacity characterizations incorporating cycling constraints, our results are independent of the algorithm used to coordinate the TCLs. The characterization leads to a set of convex constraints. A grid operator can use this characterization to compute a power consumption trajectory for an ensemble of TCLs that comes closest to what the operator needs to maintain demand-supply balance. Numerical results are provided to showcase the effectiveness of the proposed characterization.

Research on an Intelligent Computing Offloading Model for the Internet of Vehicles Based on Blockchain

Aiming at the problems of computing power, reliability and cost when intelligent vehicles deal with computationally intensive and delay-sensitive emerging applications in multiple business scenarios in the Internet of Vehicles, an intelligent computing offloading model is proposed. This can minimize the total system cost under the constraint of time delay and energy consumption. Considering the cost of blockchain and the cost of intelligent vehicles, the DDPG algorithm is used to solve the proposed model. Simulation results show that the method proposed in this paper can effectively reduce the total cost of computing offload and further improve the success rate of computational offloading under the premise of computational offloading safety.

Edge Intelligent Joint Optimization for Lifetime and Latency in Large-Scale Cyber–Physical Systems

In recent years, the exploration on large-scale cyber–physical systems (CPSs) has become a fertile research field of significant impact. Large-scale CPS applications cover not only manufacturing and production areas but also daily living domains. Traditional solutions dedicated for large-scale CPSs mainly concentrate on the service latency or reliability optimization, but neglect the resultant negative impact on system lifetime. In this article, we conduct the first study on jointly optimizing the service latency and system lifetime subject to the constraints of reliability, energy consumption, and schedulability for large-scale CPSs. We propose an edge intelligent solution composed of offline and online phases. At the offline phase, the long short-term memory (LSTM) technique is leveraged to predict task offloading rates at individual user groups. Afterward, the multiobjective evolutionary algorithm with dual local search (DLS-MOEA) is exploited to determine optimal system static settings of computation offloading mapping and task replication number. At the online phase, an affinity-driven scheme incurring minimal system dynamic overheads is designed to deal with the inherent mobility of terminal users. We also build an algorithm validation platform upon which extensive simulation experiments are carried out. Experimental results show that our offline and online schemes outperform the state-of-the-art benchmarking methods by 27.1% and 43.5%, respectively.

High-Resolution Tap-Based IoT System for Flow Data Collection and Water End-Use Analysis

Knowledge on water-use patterns in residential settings can help policymakers formulate well-targeted water conservation measures and evaluate the efficacy of such measures. Prior studies have applied machine learning techniques to disaggregate household-level water consumption data, collected by smart meters, into specific end-use categories, such as showering, basin use, kitchen use, and use by washing machine. However, the analysis of the effect of the sampling interval on the classification accuracy level remains an underinvestigated issue. This article seeks to fill this knowledge gap by identifying an optimal sampling interval that can achieve a high level of classification accuracy while overcoming constraints of on-device data storage, data transmission, and energy consumption. To understand the benefits of collecting fine-grained data for machine learning, we have built a high-resolution tap-based Internet of Things (IoT) system comprising a set of Wi-Fi-based tap sensors, gateway infrastructure, and a secure data processing pipeline. Based on empirical tap-based data collected over an eight-month period, we concluded that when the sampling interval decreases slightly from 5 to 1 s, the accuracy level of the end-use classification model increases significantly from 66.6% to 76.1 %. This article also highlights the challenges of deploying IoT sensors to collect water consumption data in a domestic setting. In order to collect sufficient ground-truth data for the training and verification of a generalizable water end-use disaggregation model, it is necessary to sophisticate the flow data collection system by adopting low-power wide-area network technologies and reducing the level of energy consumption of the flow sensing components.

Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework

Deep neural networks (DNNs) are widely used in various artificial intelligence applications and platforms, such as sensors in internet of things (IoT) devices, speech and image recognition in mobile systems, and web searching in data centers. While DNNs achieve remarkable prediction accuracy, they introduce major computational and memory bandwidth challenges due to the increasing model complexity and the growing amount of data used for training and inference. These challenges introduce major difficulties not only due to the constraints of system cost, performance, and energy consumption, but also due to limitations in currently available memory bandwidth. The recent advances in semiconductor technologies have further intensified the gap between computational hardware performance and memory systems bandwidth. Consequently, memory systems are, today, a major performance bottleneck for DNN applications. In this paper, we present DRAMA, a deep neural network memory simulator. DRAMA extends the SCALE-Sim simulator for DNN inference on systolic arrays with a detailed, accurate, and extensive modeling and simulation environment of the memory system. DRAMA can simulate in detail the hierarchical main memory components—such as memory channels, modules, ranks, and banks—and related timing parameters. In addition, DRAMA can explore tradeoffs for memory system performance and identify bottlenecks for different DNNs and memory architectures. We demonstrate DRAMA’s capabilities through a set of experimental simulations based on several use cases.

Impact of direct links on Intelligent Reflect Surface-aided MEC networks

This paper studies an intelligent reflect surface (IRS) aided mobile edge computing (MEC) network, where the direct link exists in the network can assist the task transmission for computing with the help of multiple elements in the IRS. We perform the performance evaluation by instigating the impact of direct link on the outage probability. Specifically, Firstly, we analyze the system outage probability (SOP) with a different number of reflecting elements and energy consumption constraints. Moreover, we propose two selection methods for the case of multiple reflecting elements. In particular, Method I maximizes the first-hop reflecting channel while Method II maximizes the dual-hop product channel. In further, for the two different methods, we estimate the outage probability of the system by considering the reflecting channel information and providing the analytic expression of the outage probability, respectively. Finally, the numerical results verify the correctness of our results. The results show that increasing the number of reflecting elements can effectively reduce the SOP.

Distributed Edge Event-Triggered Control of Nonlinear Fuzzy Multiagent Systems With Saturation Constraint Hybrid Impulsive Protocols

In this article, we devote to solve the consensus problem for nonlinear multiagent system under energy consumption constraint. A novel control protocol which combines impulse control with event-triggered mechanism is proposed. By setting the event decision time at pulse time, it can not only reduce triggered times but also avoid the occurrence of “Zeno behavior.” In order to reduce the conservatism of control protocol, input saturation constraint and state saturation constraint are both discussed, for the limited receiving channel of the agent and limited sampling range of the sampler, respectively. Further, the dynamic model of multiagent systems with fuzzy information is proposed for the uncertain factors in actual environment. Finally, for the sake of proving the effectiveness about the control protocols, some simulation examples are presented.

Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network

In this paper, we consider an mmWave-based train-ground communication system in the high-speed railway (HSR) scenario, where the computation tasks of users can be partially offloaded to the rail-side base station (BS) or the mobile relays (MRs) deployed on the roof of the train. The MRs operate in the full-duplex (FD) mode to achieve high spectrum utilization. We formulate the problem of minimizing the average task execution latency of all users, under local device and MRs energy consumption constraints. We propose a joint resource allocation and computation offloading scheme (JRACO) to solve the problem. It consists of a resource allocation and computation offloading (RACO) algorithm and an MR Energy constraint algorithm. RACO utilizes the matching game theory to iterate between two subproblems, i.e., data segmentation and user association and sub-channel allocation. With the RACO results, the MR energy constraint algorithm ensures that the MR energy consumption constraint is satisfied. Extensive simulations validate that JRACO can effectively reduce the average latency and increase the number of served users compared with three baseline schemes.

Using energy consumption constraints to control the freight transportation structure in China (2021–2030)

China has formulated a series of energy-saving policies to combat climate change, including energy consumption limit to 6 billion tonnes of standard coal by 2030, thus controlling the energy consumption of freight transportation is crucial for achieving energy-saving goals. In this study, the annual control model for freight transportation structure adjustment (FTSA-AC model) was established from the perspectives of the target year prediction and intermediate year controlling to control energy consumption in the freight sector. Innovatively, an efficient algorithm was designed to solve the model and obtain the annual control plan of freight transportation structure adjustment in China (2021–2030). Accordingly, the national freight transportation structure will be continuously adjusted to the optimal state until 2027, and the freight turnover will be achieved at a steady growth rate of 1.50% from 2027 to 2030. The freight turnover will reach 22888.040 billion tonne-kilometres by 2030, in which railways, highways, waterways, and aviation would account for 18.92%, 30.20%, 50.76%, and 0.12%, respectively. According to the results, freight energy consumption will reach its peak in 2029, with 145 million tonnes of standard coal. Lastly, a series of reasonable policy recommendations were presented to provide a reference for energy-efficient freight transport development in China.

Design of a Multimodal Oculometric Sensor Contact Lens.

Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field.

CDMR: Effective Computing-Dependent Multi-Path Routing Strategies in Satellite and Terrestrial Integrated Networks

With the development of science and technology, Satellite and Terrestrial Integrated Network (STIN) can effectively make up for the defects in the terrestrial network coverage and provide robust end-to-end transmission and computing supports for users in many communication scenarios such as emergency rescue, space exploration, and so on. However, many researchers only studied transmission or computing, ignoring that the task can be computed in task transmission. And limited by the satellite's battery resources and high dynamics, it is challenging to realize the fusion of communication and computing with the constraint of energy consumption in the high dynamic satellite network. Therefore, this paper proposes a Computing-Dependent Multi-path Routing (CDMR) paradigm to address such a challenge. In CDMR, the task is mapped to the multi-path route in STIN, and the energy consumption is taken as the optimization goal. Furthermore, the processing latency is the constraint to ensure that the optimal CDMR strategy is under latency constraint. Finally, the simulation results show that the CDMR proposed in this paper can effectively compute the task during the end-to-end transmission. Compared with other computing models, CDMR has more evident advantages in energy saving.

Vehicle Selection and Resource Optimization for Federated Learning in Vehicular Edge Computing

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles’ precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system’s overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.

Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs

Load balance in vehicular ad hoc networks (VANETs) is a challenge in vehicle-to-vehicle computation offloading, due to stochastic requests of users, heterogeneous service capabilities and high mobility of vehicles, etc. This paper aims to fill this gap by formulating a problem for load balance in a VANET, with the objective of minimizing the maximum load under transmit power, storage capacity, per task completion time and energy consumption constraints. The formulated problem is proved to be NP-hard, then it is investigated by decomposing it into two subproblems, i.e., how to offload tasks for the case of fixed transmit power and how to adjust transmit power for the given offloading decision. For the first subproblem, an approximation algorithm is proposed by offloading the tasks in the vehicle with the maximum load to the vehicle with minimum load. Meanwhile, a deep reinforcement learning algorithm is proposed, in order to focus on the network dynamics. A coalition based algorithm, a distributed coalition based algorithm, as well as an incentive algorithm based on deep reinforcement learning, are proposed to maximize the total payoff for the selfishness of vehicles. For the second subproblem, an adjustment strategy for transmit power is customized to further reduce the computing load. The algorithms are evaluated on an integrated simulation platform with open street map, SUMO, NS-3 and dataset of Google cluster-usage traces. Simulation results show that, the proposed algorithms outperform three state-of-the-art works for most cases, in terms of the maximum load. The proposed distributed algorithm can significantly accelerate the proposed centralized algorithm with acceptable increase in maximum load. Besides, the load can be further reduced by the proposed adjustment strategy.

Coordination optimisation of energy and manufacturing flow for industry integrated energy system

With the breaking down of information barriers between energy flow with manufacturing flow, the coordination between them is an effective way to relieve the dual pressure from energy and environment industrial integrated energy system. The authors develop a scenario-based coordination model for energy flow and manufacturing flow to make full use of the flexibilities of energy supply and production process to reduce energy cost. To capture the flexibility in the energy flow, the authors enhance an electricity-steam-product gas–gas storage coupling energy flow model considering multi-uncertainties. The authors also develop a batch process model to formulate the flexibility in the production process. Based on the batch energy consumption constraints, the energy flow model and the batch process model are integrated as a coordination model. To ensure the feasibility of hard budget constraints under all possible random realisations, we add all-scenario-feasibility robust constraints which are infinite-dimensional constraints into the model. To solve the model, a vertex scenario set based on the characteristics of convex optimisation is constructed to equivalently convert infinite-dimensional constraints to finite-dimensional constraints. In this way, the coordination model is transformed to a mixed integer linear programming and can be solved using CPLEX. Finally, numerical test based on a real iron and steel plant is analysed. The results show that coordination between energy with manufacturing flow is effective to reduce the energy cost and carbon emission. Compare with only optimising energy flow, the coordination model can reduce total cost about 221.6 thousand RMB and 304.44t coal every day.