Limited Energy Resources Research Articles

Least-cost solutions to household energy supply decarbonisation in temperate and sub-tropical climates

Decarbonisation of building energy supply is key to achieving current targets of greenhouse gas emission reduction. However, the least-cost, net-zero supply of electrical and thermal loads in residential buildings is affected by building type, local climate and grid emissions intensity and is subject to uncertain future energy prices. This work investigated a typical detached house and an apartment in two climates – temperate and sub-tropical – in Australia. Least-cost optimisation of the technology mixes required to serve building energy loads was undertaken, including a range of appliances powered by electricity, natural gas and hydrogen and the use of distributed energy resourced, notably rooftop solar and residential battery energy storage. Solutions were investigated with increasingly stringent greenhouse gas emission abatement constraints and a range of future prices for electricity and hydrogen, demonstrating: i) progressive electrification is the likely cheapest pathway to net-zero emissions for the two building types studied; ii) should grid decarbonisation lag the abatement constraint applied to the homes, a net-zero dwelling is likely unachievable and the deepest achievable abatement is very expensive; and iii) network-delivered hydrogen may be optimal in a subset of buildings with limited distributed energy resources potential and more peaky heating and hot water demands. In such cases, bio-methane or even synthetic methane may also be preferred. Through significant adoption of distributed energy resources, results suggest an affordable route to deep (i.e. > 50 %) abatement for homeowners that relies less on grid emissions and, beyond the Australian case study, can be extended to a significant proportion of dwellings in other countries.

UAV-assisted fair communications for multi-pair users: A multi-agent deep reinforcement learning method

Unmanned aerial vehicle (UAV) plays an important role in scenarios like search and rescue, remote communication relay, battlefield mobile networks, etc. In this paper, we investigate multiple UAV relays providing real-time transmission for multi-pair ground users (GUs) in the absence of ground-based stations (GBSs). Due to the limited load capacities and energy resources on-board, fairness between multi-pair users, throughput maximization, as well as the connectivity maintenance between UAVs are jointly considered. We formulate the energy-efficient fair throughput objective function, which turns to be non-convex with hybrid variables. To solve this intractable problem, we utilize the power of neural networks in function approximation and propose a multi-agent deep reinforcement learning (MADRL)-based algorithm. Different from traditional MADRL algorithms, we utilize the method, named independent proximal policy optimization (IPPO), which allows agents to update according to their own observations memory and encourages more explorations to some extend, as the base of our solution. Simulation results demonstrate that our algorithm outperforms some baselines in terms of fairness, throughput as well as energy consumption.

Design and extensibility analysis of a variable buoyancy system for small autonomous underwater vehicles

PurposeA switching depth controller based on a variable buoyancy system (VBS) is proposed to improve the performance of small autonomous underwater vehicles (AUVs). First, the requirements of VBS for small AUVs are analyzed. Second, a modular VBS with high extensibility and easy integration is proposed based on the concepts of generality and interchangeability. Subsequently, a depth-switching controller is proposed based on the modular VBS, which combines the best features of the linear active disturbance rejection controller and the nonlinear active disturbance rejection controller.Design/methodology/approachThe controller design and endurance of tiny AUVs are challenging because of their low environmental adaptation, limited energy resources and nonlinear dynamics. Traditional and single linear controllers cannot solve these problems efficiently. Although the VBS can improve the endurance of AUVs, the current VBS is not extensible for small AUVs in terms of the differences in individuals and operating environments.FindingsThe switching controller’s performance was examined using simulation with water flow and external disturbances, and the controller’s performance was compared in pool experiments. The results show that switching controllers have greater effectiveness, disturbance rejection capability and robustness even in the face of various disturbances.Practical implicationsA high degree of standardization and integration of VBS significantly enhances the performance of small AUVs. This will help expand the market for small AUV applications.Originality/valueThis solution improves the extensibility of the VBS, making it easier to integrate into different models of small AUVs. The device enhances the endurance and maneuverability of the small AUVs by adjusting buoyancy and center of gravity for low-power hovering and pitch angle control.

Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing

Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage.

Energy capable protocol for heterogeneous blue brain network

The Blue Brain has a wide range of applications, which raises a number of challenging issues. Electronics may continuously monitor their surroundings depending on the real data that their Blue Brain nodes are acquiring by employing situational intelligence based on the Blue Brain environment. The Blue Brain does more than only monitor user behavior when utilizing this technology. Blue Brain is linked to a critical prerequisite for energy-efficient communication methodologies. Through the Blue Brain network, it utilizes the heterogeneity and variety of the interconnected components. Blue Brain nodes that are outsourced and have limited energy resources must utilize less energy. IoT nodes with differing energy levels are frequently dispersed across different geographic regions. The main goal of this work is to provide an energy-efficient Blue Brain framework capable of managing cluster head (CH) selection and Blue Brain node clustering. The appropriate CHs are selected, and an energetic cutoff concept is developed to guarantee that energy is shared equally among the CHs and participating Blue Brain nodes. The proposed concept envisions three different kinds of Blue Brain nodes for a Blue Brain infrastructure: expert, intermediary, and normal Blue Brain nodes. Level 1 Blue Brain nodes are regarded as normal nodes; level 2 nodes are regarded as intermediate Blue Brain nodes; and level 3 nodes are regarded as expert Blue Brain nodes. Level 1 Blue Brain nodes use the least amount of energy. The outcomes of the simulation demonstrate that the recommended strategy outperforms other existing methods.

Impact of Renewable Energy Resources on the Performance of DTN Networks in the Context of Hierarchical Routing Tree Topology (HRTT)

Delay-Tolerant Networks (DTN) are mobile networks specifically designed to operate in challenging wireless environments where connectivity is not guaranteed, and various issues such as frequent disconnections, long delivery delays, low delivery rates, and high error rates are common. One of the major problems of DTN networks is limited energy resources which impose strict constraints on the operational lifespan of DTN nodes. To solve these problems, we propose the adoption of a long-term strategy for energy management, based on the exploitation of renewable energies and the improvement of the HRTT topological architecture through the direct integration of renewable energy sources within DTN nodes. Indeed, DTN nodes powered by renewable energy resources, such as solar energy, have the potential to recharge themselves, extending their operational lifespan, increasing their storage and transmission capabilities, and reducing dependence on non-renewable energy resources. In this article, we assess the impact of limited energy resources and renewable energy resources like solar panels on the energy performance of DTN networks in the context of the Hierarchical Routing Tree Topology (HRTT), taking into account the average remaining energy as a performance metric. The evaluation of energy performance and the implementation of the HRTT topology are carried out using the ONE (Opportunistic Network Environment) simulator. According to the simulation results, the average remaining energy of nodes using renewable resources is significantly higher compared to the initial energy levels of nodes for all DTN protocols adopted by the nodes in the context of the HRTT topology. However, the average remaining energy of nodes using limited resources is lower compared to their initial energy levels for all DTN protocols adopted by the nodes in the context of the HRTT topology.

Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks

The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.

МЕТОДЫ ЗАЩИТЫ И КРИПТОГРАФИЧЕСКИЕ ПРОТОКОЛЫ ДЛЯ ВСТРОЕННЫХ ЭЛЕКТРОННЫХ СИСТЕМ

The article is devoted to the study of protection techniques and cryptographic protocols designed for embedded electronic systems, which are key components in modern devices and infrastructures. The main security threats faced by these systems are reviewed, including attacks through third-party channels and physical access. The paper analyzes lightweight cryptographic protocols suitable for limited computational and energy resources, authentication methods, key management, and hardware and software security approaches. Examples of successful implementations are given and the effectiveness of different methods is evaluated. Future directions in embedded system protection, including the impact of quantum computing, are also discussed.

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

Multiagent Learning for Secure Wireless Access From UAVs With Limited Energy Resources

Multiagent Learning for Secure Wireless Access From UAVs With Limited Energy Resources

UAV Relay Energy Consumption Minimization in an MEC-Assisted Marine Data Collection System

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

LELBC: A low energy lightweight block cipher for smart agriculture

The massive collection and transmission of various crop and livestock data in smart agriculture leads to serious security concerns. Furthermore, many Internet of Things (IoT) devices in smart agriculture are battery-powered, with limited energy resources. Therefore, a low energy lightweight block cipher (LELBC) is proposed to overcome the data leakage problem during sensor data transmission in smart agriculture. Firstly, a new permutation substitution permutation (PSP) structure is proposed, taking into account the energy resource constraints of unified encryption and decryption (ED) circuits. It has highly consistent encryption and decryption and a good diffusion effect. Secondly, a 4-bit low energy involutive S-box is obtained based on a genetic algorithm. The proposed S-box has lower area and latency compared to the existing S-boxes. The experimental data show that LELBC consumes 1864 gate equivalents (GE) in area and 6.99 μJ/bit in energy (encryption + decryption) under the UMC 0.18μm 1P6M process library. LELBC decreases energy and area consumption by 24.02% and 24.04%, respectively, compared to Midori. Finally, a temperature collection and encryption transmission platform is established. LELBC is deployed on the platform to encrypt the collected data, establishing the first line of defense for the secure transmission of smart agriculture sensor data.

Machine learning based cascaded ANN MPPT controller for erratic PV shading circumstances

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

Node depth Representation-based Evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running time across a wide range of data instances. Specifically, our algorithm provides 49.54% better solution quality on average across all data types compared to the previous state-of-the-art method.

A high-scalability and low-latency cluster-based routing protocol in time-sensitive WSNs using genetic algorithm

Wireless Sensor Networks (WSNs) play a critical role in data transmission and reception across a multitude of applications. A primary challenge in WSNs is managing scalability and latency due to the deployment of numerous sensor nodes, each with limited memory and energy resources. Moreover, understanding the randomness in node distribution across the WSN is complex. A commonly employed technique to address this issue is clustering, which groups distributed nodes within the network. A node with high energy, preferably located near the cluster's center, is selected as the cluster head to enhance latency. This paper primarily focuses on developing an efficient routing protocol to improve the scalability of WSNs. An ideal routing protocol should adapt to network topology changes. In this proposal, a Genetic Algorithm-based routing mechanism, the Cluster-based Genetic Routing Protocol (CGRP), is established. CGRP is particularly suitable for highly distributed and rapidly expanding networks, performing well even with an increase in workload. A mathematical evaluation is conducted to elucidate the scalability of the network. To demonstrate CGRP's efficiency, it was compared with various conventional state-of-the-art routing algorithms. Experimental results indicate that CGRP outperforms these algorithms, achieving lower latency and proving robust in highly scalable WSNs.

Investigating Pathways to Minimize Sensor Power Usage for the Internet of Remote Things.

The Internet of Remote Things (IoRT) offers an exciting landscape for the development and deployment of remote wireless sensing nodes (WSNs) which can gather useful environmental data. Low Power Wide Area Networks (LPWANs) provide an ideal network topology for enabling the IoRT, but due to the remote location of these WSNs, the power and energy requirements for such systems must be accurately determined before deployment, as devices will be running on limited energy resources, such as long-life batteries or energy harvesting. Various sensor modules that are available on the consumer market are suitable for these applications; however, the exact power requirements and characteristics of the sensor are often not stated in datasheets, nor verified experimentally. This study details an experimental procedure where the energy requirements are measured for various sensor modules that are available for Arduino and other microcontroller units (MCUs). First, the static power consumption of continually powered sensors was measured. The impact of sensor warm-up time, associated with powering on the sensor and waiting for reliable measurements, is also explored. Finally, the opportunity to reduce power for sensors which have multiple outputs was investigated to see if there is any significant reduction in power consumption when obtaining readings from fewer outputs than all that are available. It was found that, generally, CO2 and soil moisture sensors have a large power requirement when compared with temperature, humidity and pressure sensors. Limiting multiple sensor outputs was shown not to reduce power consumption. The warm-up time for analog sensors and digital sensors was generally negligible and in the order of 10-50 ms. However, one CO2 sensor had a large overhead warm-up time of several seconds which added a significant energy burden. It was found that more, or as much, power could be consumed during warm-up as during the actual measurement phase. Finally, this study found disparity between power consumption values in datasheets and experimental measurements, which could have significant consequences in terms of battery life in the field.

Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach

Wireless Sensor Networks (WSNs) are widely studied for their data collection and monitoring capabilities across diverse applications. However, the limited energy resources of sensor nodes present a significant challenge in extending the network's lifespan. To overcome this, we introduce a Deep Learning based Grouping Model Approach (DL-GMA) that optimizes energy usage in WSNs. DL-GMA employs advanced deep learning techniques, particularly Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation, Cluster Head (CH) selection, and CH maintenance. Evaluation using key metrics—Energy Efficiency (88.7 %), Network Stability (90.8 %), Network Scalability (87.1 %), Congestion Level (18.3 %), and Quality of Service (QoS) (93.4 %)—demonstrates the effectiveness of DL-GMA in energy utilization optimization and overall network performance. Incorporating deep learning and intelligent grouping, our approach extends WSN lifespan and improves data transmission efficiency. DL-GMA represents a significant advancement in energy optimization for WSNs, addressing the challenges of limited energy resources and maximizing the network's potential while improving data transmission efficiency.

Energy auditing of a soy‐paneer (tofu) processing unit: A case study

AbstractNowadays, limited energy resources and higher energy prices have increased the importance of energy management. Energy use has also grown over time due to the increased production capacity of food industries in response to the growing demand for high‐quality processed foods. The present investigation studies a small‐scale soy‐paneer processing unit's energy consumption methods and patterns. Energy (both direct and indirect) involved in each unit operation was evaluated, and the scope for increasing energy efficiency was highlighted. The specific energy expenditure in the soy‐paneer‐making process was 10.77 MJ/kg. The unit operation that demands the highest energy requirement was steaming (92.43% of the total energy input), as it was produced through a less efficient burner‐based boiler. As a result, it was advised to switch out the present burner‐based steam‐generating boiler system to a more efficient electrical steam generation system, which would also result in decreased cost and better energy utilization.Practical applicationEnergy auditing of the processing unit aids in assessing energy consumption and proposing measures to reduce energy use and cost. The soy processing sector holds a vital position in producing soy‐derived products. Notably, soy‐paneer stands out as a prominently consumed product within this industry. Energy auditing can play a crucial role in optimizing energy consumption and efficiency in soy processing units.

A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks.

Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods.

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

In complex two-dimensional monitoring environments, how to enhance network efficiency and network lifespan while utilizing limited energy resources, and ensuring that wireless sensor networks achieve the required partial coverage of the monitoring area, are the challenges of optimizing coverage in wireless sensor networks.With the premise of ensuring connectivity in the target network area, an enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) is proposed in this paper, by jointly optimizing network coverage, node utilization, and network energy balance objectives. Firstly, the logistic chaotic mapping is used to maintain the diversity of the initial salp swarm population. Secondly, to balance global and local search capabilities, a new dynamic convergence factor is introduced. Finally, to escape local optima more effectively, a follower updating strategy is implemented to reduce the blind following of followers while retaining superior individual information. The effectiveness of the strategy is validated through comparative experiments on ZDT and DTLZ test functions, and the proposed algorithm is applied to coverage optimization in WSNs in complex environments. The results demonstrate that the algorithm can adjust coverage thresholds according to different application requirements, providing various effective coverage optimization configurations. With the same preset requirements for partial coverage achieved, both network efficiency and lifespan have been significantly improved.