Energy Consumption Constraints Research Articles

Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

In satellite-terrestrial integrated networks, it is a common practice to schedule real-time tasks from low Earth orbit (LEO) satellites to ground stations (GSs) for data processing. However, the joint task scheduling and resource allocation under unknown environment dynamics (e.g., transmission latency) remains to be a challenging problem. First, the tradeoff between task latencies and energy consumption should be carefully considered when making decisions to minimize task latencies under time-averaged energy consumption constraints. Second, to learn the environment uncertainties and minimize the system performance loss (i.e., regret) in terms of task latencies, both online feedback and offline history should be leveraged efficiently, and the accompanying exploration-exploitation tradeoff should be dealt with in a proper way. In this article, we formulate the joint task scheduling and resource allocation problem as a constrained combinatorial multi-armed bandit (CMAB) problem. To solve the problem, by integrating online learning, online control, and offline historical information, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task scheduling and Resource allocation scheme with Data-driven Bandit Learning</i> called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRDBL</i> . Our theoretical and numerical results show that TRDBL achieves a sublinear time-averaged regret while satisfying the time-averaged energy consumption constraints.

Multi-AUV dynamic trajectory optimization and collaborative search combined with task urgency and energy consumption scheduling in 3-D underwater environment with random ocean currents and uncertain obstacles

A double layer bio-inspired self-organizing map (DLBSOM) algorithm for multiple autonomous underwater vehicle (multi-AUV) dynamic task planning to search for multi-targets in the 3-D underwater environments affected by random ocean currents and dynamic uncertainty obstacles is proposed. Combined with a task urgency bio-inspired neural network (TUBNN) model that is applied to construct the neuron activity network to complete initial task assignment and path planning with the constraint of energy consumption. The decoupling of the task assignment and path planning in the initial task planning is solved through the corrected redistribution mechanism and end-to-end trajectory optimization method. The unknown obstacles information is fused with the diamond-shaped local neuronal network constructed by the diamond closed curve function. Through the function curve, the local neuronal network can be precisely adjusted according to AUV operation information and equipment performance to achieve the saving of computing resources and quick processing speed. Subsequently, the distance-speed comprehensive evaluation (DSCE) strategy is used to complete dynamic avoidance decision. By the General Bathymetric Chart of the Oceans (GBCO) and the Integrated Ocean Observing System (IOOS), the environment is generated to complete multiple sets of simulations that demonstrate the efficiency and adaptability of the DLBSOM algorithm in multi-AUV collaborative search.

Energy-Constrained Indoor Air Quality Optimization for HVAC System in Smart Building

The heating, ventilation, air conditioning (HVAC) system is a common ventilation system applied to an indoor environment based on Internet of Things (IoT) technology. In HVAC, providing a comfortable temperature under the constraint of energy is a major challenge. In this article, we propose an air quality optimization strategy to control the air supply and energy consumption, and build several dynamic models to capture the stochastic processes in HVAC. Besides, a system utility maximization problem is formulated. We provide a solution framework based on the Lyapunov optimization method. Based on this framework, we propose a utility-optimal air quality optimization algorithm to solve the subproblem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the upper bound of the indoor temperature is derived, and the optimality of the algorithm is analyzed. Simulation results show the impact of the system parameter on the HVAC system and the indoor temperature, and verify that the proposed strategy and methods can maintain the comfortable temperature range and supply more fresh air effectively under the constraints of energy consumption.

Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

Energy-Efficient Joint Computation Offloading and Resource Allocation Strategy for ISAC-Aided 6G V2X Networks

Integrated sensing and communications (ISAC) is a pillar technology of 6G to enable intelligent environment awareness and hardware cost efficiencies. In ISAC-aided 6G vehicle-to-everything (V2X) networks, the perception data fusion from multiple sources can make a reliable and efficient data sharing to guarantee driving safety. However, the contradiction between task delay requirement and energy consumption becomes more and more prominent with the growth of computing task amount. Therefore, this paper proposes a joint computation offloading and resource allocation strategy to build greener V2X networks with mobile-edge computing (MEC) and ISAC technologies. A data fusion architecture for cooperative perception is first introduced to support fusing massive perception data from wireless infrastructures and vehicles. Then, a minimization problem of queuing latency is formulated with long-term latency and energy consumption constraints for data fusion computing tasks. The problem is further reformulated by the Lyapunov optimization method to transfer delay and energy constraints into queue stability problems. Finally, a joint computation offloading and resource allocation (JCORA) scheme is proposed to obtain the optimal computation offloading and resource allocation decision, achieving the balance between task delay and energy consumption. Extensive simulations validate the effectiveness of the proposed strategy compared with other baseline schemes.

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints.

Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

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.