Limited Energy Supply Research Articles

An energy-balanced unequal clustering approach for circular wireless sensor networks

The network lifetime of the Wireless Sensor Networks (WSNs) is a critical factor for the relevant applications due to the limited energy supply. However, the imbalanced energy consumption of the whole network always leads to the “Hot Spot Problem” and the decline of network lifetime inevitably. To this end, the schemes of energy-balanced unequal clustering and energy-efficient cluster head rotation are considered in this paper. To be specific, we conduct detailed theoretical derivation and mathematical calculation based on the concept of gradient to obtain the optimal number of CHs, with the aim of balancing the energy consumption for the whole network topology. In addition, a fuzzy logic-based mechanism for CHs rotation is also proposed to balance the energy distribution among different cluster heads. Subsequently, a novel Energy-Balanced unequal Clustering Approach (EBCA) for circular wireless sensor networks is proposed and detailed. Finally, we conduct extensive simulations to verify its performance. The experimental results indicate that EBCA is able to balance the energy consumption of cluster heads in different gradient, reduce the energy consumption and prolong the network lifetime effectively compared with the classic and latest clustering algorithms.

An Investigation of Various Versions of AODV Protocol for Discovering Routing Path and Eliminating Packet Loss

A Mobile Ad hoc Network (MANET) is a network made up of mobile nodes that communicate through radio. It is a self-contained computer that can be individually programmed, ordered, and controlled. The nodes of an ad hoc network move at their own pace. Ad hoc network nodes are unable to communicate directly with one another due to their short propagation ranges. The topology of the network moves as nodes in a network switch, resulting in frequent route failures. The nodes of the ad hoc network are powered by a battery with a limited energy supply. When a node's battery dies, the network lifespan is shortened, resulting in network loss. Routing is a critical component of MANETs. As a result, Energy Efficient Routing Protocol is chosen to boost network efficiency. There are many routing protocols used to transfer data packets in wireless Ad hoc networks, the most important of which is the Ad hoc On-demand Distance Vector (AODV) Routing Protocol. The key issue with using AODV is that connection errors occur as a result of node movement that is unpredictable. As either the source node or an intermediate node shifts during data packet transfer, the energy in the nodes is lost again due to the additional path exploration process, which occurs several times. This paper provides a literature review of various implementations of the AODV protocol in the form of routing route exploration and packet loss reduction.

Trends in dark biohydrogen production strategy and linkages with transition towards low carbon economy: An outlook, cost-effectiveness, bottlenecks and future scope

The conventional fossil fuel showed a persistent and intense decline steadily over the past two decades have led to global deterioration of limited sustainable energy supplies. Furthermore, price fluctuations and its serious ecological consequences had piqued the interest of researchers mostly in domain of alternative renewable energy. Among all existing fuels, biohydrogen is documented because of its carbon-neutral, simple and sustainable output, low carbon emissions, and large energy density. Hydrogen (H2) generation from dark fermentation of biowaste is an enticing sustainable method which promotes in the creation of low carbon economy. This review details the overview of hydrogen yield form renewable feedstock through dark fermentation. It also detailed the recent trends such as pretreatment, addition of various additives, integrated options, etc., employed towards the enhancement of fermentation process to enrich the hydrogen production. Discussion about the inhibitory substances that affects the performance of fermentation process was incorporated. In addition, it elaborates the economic feasibility, challenges and limitation of the process along with future scope for the development of sustainable hydrogen economy.

Coordination of a Class of Underactuated Systems via Sampled-Data-Based Event-Triggered Schemes

This article investigates the coordination of a class of underactuated systems subject to limited energy supply and channel bandwidth and aims to stabilize system states and exclude Zeno behaviors simultaneously. First, by means of event-triggered (E-T) and quantized techniques, several novel quantized sampled-data-based E-T schemes are constructed, which only require discrete-time controller updates and partial quantized states, and thus efficiently mitigate the control and communication workloads. Then, in order to further lower the communication consumptions, several new triggered sampled-data-based communication rules under fixed and switched networks are established, where the communications are performed only at some specific instants and, thus, the ideally continuous-time signal transmission among neighbors can be avoided. Note that sufficient criteria for achieving the coordination of the underactuated systems are derived in terms of the Lyapunov–Krasovskii functional method. Finally, numerous simulations are carried out to demonstrate the effectiveness of the theoretical results.

Distributed detection of sparse signals with censoring sensors in clustered sensor networks

In this paper, we explore the distributed detection of sparse signals in energy-limited clustered sensor networks (CSNs). For this problem, the centralized detector based on locally most powerful test (LMPT) methodology that uses the analog data transmitted by all the sensor nodes in CSNs can be easily realized according to the prior work. However, for the centralized LMPT detector, the energy consumption caused by data transmission is excessively high, which makes its implementation in CSNs with limited energy supply impractical. To address this issue, we propose a new detector by combining the advantages of censoring and LMPT strategies, in which both the cluster head (CLH) nodes and the ordinary (ORD) nodes only send data deemed to be informative enough and the fusion center (FC) fuses the received data based on LMPT methodology. The detection performance of the proposed detector, characterized by Fisher Information, is analyzed in the asymptotic regime. Also, we analytically derive the relationship between the detection performance of the proposed censoring-based LMPT (cens-LMPT) detector and the communication rates, both of which are controlled by the censoring thresholds. We present an illustrative example by considering the detection problem with 2-CSNs, i.e., CSNs in which each cluster contains two nodes, and provide corresponding theoretical analysis and simulation results.

Compression of Deep Neural Networks based on quantized tensor decomposition to implement on reconfigurable hardware platforms

Deep Neural Networks (DNNs) have been vastly and successfully employed in various artificial intelligence and machine learning applications (e.g., image processing and natural language processing). As DNNs become deeper and enclose more filters per layer, they incur high computational costs and large memory consumption to preserve their large number of parameters. Moreover, present processing platforms (e.g., CPU, GPU, and FPGA) have not enough internal memory, and hence external memory storage is needed. Hence deploying DNNs on mobile applications is difficult, considering the limited storage space, computation power, energy supply, and real-time processing requirements. In this work, using a method based on tensor decomposition, network parameters were compressed, thereby reducing access to external memory. This compression method decomposes the network layers’ weight tensor into a limited number of principal vectors such that (i) almost all the initial parameters can be retrieved, (ii) the network structure did not change, and (iii) the network quality after reproducing the parameters was almost similar to the original network in terms of detection accuracy. To optimize the realization of this method on FPGA, the tensor decomposition algorithm was modified while its convergence was not affected, and the reproduction of network parameters on FPGA was straightforward. The proposed algorithm reduced the parameters of ResNet50, VGG16, and VGG19 networks trained with Cifar10 and Cifar100 by almost 10 times.

Energy efficient convolutional neural networks for arrhythmia detection

Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimization steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive. • We generate energy-efficient 1D-CNN architectures for the detection of heart arrhythmias. • Accuracy and energy are optimized during learning for an accurate light-weight design. • Our model uses 99.4% less trainable parameters in comparison to previous studies. • The method can be applied on every type of continuous time series signals.

Stochastic Optimization-Aided Energy-Efficient Information Collection in Internet of Underwater Things Networks

In the face of deeply exploring and exploiting marine resources, the Internet of Underwater Things (IoUT) networks have drawn great attention considering its widely distributed low-cost and easy-deployment smart sensing nodes. However, given the hostile underwater environment, it is critical to conceive energy-efficient information collection because of limited underwater energy supply and inefficient artificial recharge methods. Characterized by high flexibility and maneuverability, autonomous underwater vehicles (AUVs) are regarded as a promising solution for information collection in the IoUT relying upon delicate AUVs’ trajectory and information collection strategy design with the spirit of balancing their energy consumption and information processing capability. In this article, we propose a heterogeneous AUV-aided information collection system with the aim of maximizing the energy efficiency of IoUT nodes taking into account AUV trajectory, resource allocation, and the Age of Information (AoI). Moreover, based on the particle swarm optimization (PSO), we obtain the trajectory of AUVs with low time complexity. Additionally, a two-stage joint optimization algorithm based on the Lyapunov optimization is constructed to strike a tradeoff between energy efficiency and system queue backlog iteratively. Finally, simulation results validate the effectiveness and superiority of our proposed strategy.

Efficient UAV-Based MEC Using GPU-Based PSO and Voronoi Diagrams

Mobile-Edge Computing (MEC) displaces cloud services as closely as possible to the end user. This enables the edge servers to execute the offloaded tasks that are requested by the users, which in turn decreases the energy consumption and the turnaround time delay. However, as a result of a hostile environment or in catastrophic zones with no network, it could be difficult to deploy such edge servers. Unmanned Aerial Vehicles (UAVs) can be employed in such scenarios. The edge servers mounted on these UAVs assist with task offloading. For the majority of IoT applications, the execution times of tasks are often crucial. Therefore, UAVs tend to have a limited energy supply. This study presents an approach to offload IoT user applications based on the usage of Voronoi diagrams to determine task delays and cluster IoT devices dynamically as a first step. Second, the UAV flies over each cluster to perform the offloading process. In addition, we propose a Graphics Processing Unit (GPU)-based parallelization of particle swarm optimization to balance the cluster sizes and identify the shortest path along these clusters while minimizing the UAV flying time and energy consumption. Some evaluation results are given to demonstrate the effectiveness of the presented offloading strategy.

Robust Energy-Efficient Hybrid Beamforming Design for Massive MIMO LEO Satellite Communication Systems

In low earth orbit (LEO) satellite communication systems, the limited energy supply capacity and the difficulty in obtaining channel state information (CSI) are the practical challenges. Motivated by this, we focus on the design of robust hybrid beamforming to maximize the energy efficiency of LEO satellite communication systems. Assuming that the LEO satellite transmitter adopts the massive multi-input multi-output (MIMO) technology, considering the CSI errors caused by propagation delay and Doppler shift, under the constraints of transmit power and quality of service (QoS), a robust energy-efficient hybrid beamforming scheme is proposed. Since that there are no explicit expressions for the ergodic user rate and the ergodic signal-to-interference-plus-noise ratio, the approximate values with closed-form expressions are adopted. Then, we invoke the semidefinite programming (SDP) algorithm to transform the nonconvex quadratic constrained quadratic programming (QCQP) problem equivalently, and an inner and outer nested iterative algorithm combining quadratic transformation fractional programming (QTFP) and concave convex process (CCCP) is utilized to transfer a nonconvex problem into a convex problem. Meanwhile, we adopt a penalty function algorithm to solve the rank-one constraint in semidefinite programming algorithm. Finally, we invoke the normal form distance minimization (NFDM) algorithm and the alternating optimization (AltOpt) algorithm to jointly solve the digital beamforming matrix and analog beamforming matrix in hybrid beamformer. Numerical results validate that our proposed robust approach significantly outperforms the conventional one.

GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems

In the scenario of limited energy supply, Unmanned Aerial Vehicles (UAVs) enabled communication systems must make efficient use of energy in order to provide long-term service. In this paper, we propose a global energy efficiency maximization (GREEN) strategy for multi-UAV enabled communication systems. In such systems, a group of UAVs communicates with their associated ground terminals (GTs) by using a UAV-enabled interference channel (UAV-IC). In particular, we optimize the UAVs' trajectory control by jointly considering both the communication throughput and the total energy consumption of the whole system. We aim to maximize the global energy efficiency (GEE) of a task for multi-UAV communications, in which the problem is challenging to optimally solve due to its non-convex nature and strongly coupled variables. To tackle this problem, first, we investigate and propose a global energy-efficient optimization problem based on the fly-hover-communicate protocol. Second, we extend our proposed solution from the single UAV-enabled system to multiple UAV-GT pairs cases. In addition, we consider the general scenario in which the UAVs also communicate while flying. Based on the successive convex approximation technique and the path discretization method, the GREEN strategy is designed for optimizing UAV trajectories in this scenario. The simulation results show that the proposed strategy can achieve significantly higher GEE than the benchmark schemes for multi-UAV enabled communications.

Energy limitation or sensitive predators? Trophic and non-trophic impacts of wastewater pollution on stream food webs.

Impacts of environmental stressors on food webs are often difficult to predict because trophic levels can respond in divergent ways, and biotic interactions may dampen or amplify responses. Here we studied food-web-level impacts of urban wastewater pollution, a widespread source of degradation that can alter stream food webs via top-down and bottom-up processes. Wastewater may (1) subsidize primary producers by decreasing nutrient limitation, inducing a wide-bottomed trophic pyramid. However, (2) wastewater may also reduce the quality and diversity of resources, which could decrease energy transfer efficiency by reducing consumer fitness, leading to predator starvation. Additionally, (3) if higher trophic levels are particularly sensitive to pollution, primary consumers could be released from predation pressure. We tested these hypotheses in 10 pairs of stream sites located upstream and downstream of urban wastewater effluents with different pollutant levels. We found that wastewater pollution reduced predator richness by ∼34%. Community size spectra (CSS) slopes were steeper downstream than upstream of wastewater effluents in all except one impact site where predators became locally extinct. Further, variation in downstream CSS slopes were correlated with pollution loads: the more polluted the stream, the steeper the CSS. We estimate that wastewater pollution decreased energy transfer efficiencies to primary consumers by ∼70%, limiting energy supply to predators. Additionally, traits increasing vulnerability to chemical pollution were overrepresented among predators, which presented compressed trophic niches (δ15 N-δ13 C) downstream of effluents. Our results show that wastewater pollution can impact stream food webs via a combination of energy limitation to consumers and extirpation of pollution-sensitive top predators. Understanding the indirect (biotically mediated) vs. direct (abiotic) mechanisms controlling responses to stress may help anticipating impacts of altered water quantity and quality, key signatures of global change.

The legacy of river channel modification in wadeable, lowland rivers: Exploring overdeep rivers in England

AbstractMany lowland fluvial systems are suspected to possess a morphological legacy from a long history of channel modifications as a consequence of limited energy and sediment supply to facilitate recovery. We explore the extent of such modifications using a regionally extensive dataset of physical habitat surveys compiled by non‐specialist surveyors. Representative photographs for each surveyed site were used to quality check channel width, depth and bed grain size information derived from Modular River Physical (MoRPh) surveys. Following checking, 1659 surveys were retained for analysis from alluvial sites, almost entirely in England. The photographs were also inspected for evidence of clear ‘overdeepening’ that would preclude frequent overtopping of the lower bank top. Results indicated that almost one‐third of the sites were overdeepened, that width‐to‐depth ratios defined using the active bed width showed stronger discrimination of overdeepening than bankfull width, that highly statistically significant identification of overdeepened channels was possible in channels up to 10 m wide and with only minimal differences attributable to channel bed materials. Stepwise regression analysis estimated relationships between channel width‐to‐depth ratios and channel size for overdeepened and non‐overdeepened channels. We demonstrate that large data sets collected by numerous non‐specialist surveyors can, with careful filtering, generate statistically robust results of geomorphological value over areas larger than is otherwise practicable. Furthermore, we reveal a notable legacy of overdeepening in the analysed lowland rivers, which presents a significant ‘hydromorphological’ management challenge.

Network Model With Reduced Metabolic Rate Predicts Spatial Synchrony of Neuronal Activity.

In a cerebral hypometabolic state, cortical neurons exhibit slow synchronous oscillatory activity with sparse firing. How such a synchronization spatially organizes as the cerebral metabolic rate decreases have not been systemically investigated. We developed a network model of leaky integrate-and-fire neurons with an additional dependency on ATP dynamics. Neurons were scattered in a 2D space, and their population activity patterns at varying ATP levels were simulated. The model predicted a decrease in firing activity as the ATP production rate was lowered. Under hypometabolic conditions, an oscillatory firing pattern, that is, an ON-OFF cycle arose through a failure of sustainable firing due to reduced excitatory positive feedback and rebound firing after the slow recovery of ATP concentration. The firing rate oscillation of distant neurons developed at first asynchronously that changed into burst suppression and global synchronization as ATP production further decreased. These changes resembled the experimental data obtained from anesthetized rats, as an example of a metabolically suppressed brain. Together, this study substantiates a novel biophysical mechanism of neuronal network synchronization under limited energy supply conditions.

MCTS-Enhanced Hybrid Offloading for Aerial Multi-Access Edge Computing

Computation offloading is a main issue in multi-access edge computing (MEC). In particular, in aerial MEC (AMEC) relying on UAVs/ drones, due to limited resources of single devices in providing services to end users, we always have to offload tasks to different devices within the network. However, in wireless communications, there are obstacles in transmission distance between devices sending out offloading requests and others with idle resources. Furthermore, UAVs in AMEC also have to face the challenges of limited energy supply from batteries. As a result, in this article, we design a Monte Carlo tree search enhanced hybrid strategy in achieving time- and energy-aware computation offloading for AMEC. The simulation results show that the proposed solutions can greatly reduce time cost and energy consumption while using UAVs as flying offloaders.

Terminal area control rules and eVTOL adaptive scheduling model for multi-vertiport system in urban air Mobility

On the one hand, vertiports in economically developed areas will present the operation mode of multi-vertiport, and the contradiction between the development of urban air mobility and limited operating resources is very serious. On the other hand, unlike fixed-wing aircraft or helicopters, the electric vertical take-off and landing (eVTOL) aircraft have different operating processes and limited battery energy supply. Long flight times in the terminal area pose a risk of exhaustion and great safety challenges. Given the terminal area of the multi-vertiport system (MVS-TA), this paper investigates the adaptive control system (ACS), which includes the design of the operating environment and the integrated scheduling model. The design of the operating environment is the basis of adaptive management, which consists of the concept of multi-ring structure and the junction control rules based on backpressure policy. Considering the power constraint and dynamic priority, an integrated model including path planning model and distributed sequencing model is constructed to solve the cooperative scheduling problem of approach-departure flights. Results from numerical analyses reveal that: First, in the case of large flight flow, ACS can achieve the efficiency and safety of MVS-TA operation better than the traditional control system. Secondly, the flight density into the terminal area has a greater impact on ACS performance relative to flight volume. With a density of less than 120 flights/hour, the operating efficiency of MVS-TA under ACS varies within 10.3% with the number of flights or density.

Exergy Efficiency Promotion for the System of CO2 Hydrogenation to Methanol in Habitable Confined Space

Excess hydrogen and carbon dioxide will be produced during the operation of life support systems in the habitable confined space (HCS), and to eliminate the two excess gases by converting them into methanol is of great significance for maintenance of atmospheric balance and protection of crew’s life safety. Due to the limited energy supply ability within the HCS, it is important for the system of carbon dioxide hydrogenation to methanol (CDHM) to operate with high energy efficiency to reduce unnecessary external energy consumption and internal energy loss. In this paper, the exergy analysis method is adopted for exergy efficiency improvement. Specifically, a parametric study on the exergetic performance of the CDHM system is conducted based on the three key working condition parameters that have a huge impact on the reaction process and energy utilization quality, which is used to find the favorable working condition with low external energy consumption and exergy destruction per unit gas elimination and high exergy efficiency. Within the chosen three reaction parameters which are reaction pressure, temperature, and space velocity ranging from 5 to 8 MPa, from 483.15 to 543.15 K, and from 2,800 to 4000 h−1, respectively, the gas elimination of carbon dioxide and hydrogen increases by 13.3, 19.58, and 30.58%, respectively. Moreover, the input power, cold energy consumption, and exergy destruction per molar synthetic methanol all grow to some extent, leading to a 0.06% decline, a 0.46% promotion, and a 0.15% decrease, respectively, in the exergy efficiency. The results show that the high exergy efficiency can be realized with relatively low pressure, high temperature, and low space velocity in the working condition. Besides, the exergy destructions of each component in the CDHM system are also presented in this paper. The exergy destructions in the methanol synthesis reactor, heater, and heat exchanger hot end are found to be the three biggest, whose summation accounts for more than 90% of the total system exergy destruction. Thus, the exergy efficiency also can be improved by reducing the three biggest exergy destructions.

Reliability-Constrained Throughput Optimization of Industrial Wireless Sensor Networks With Energy Harvesting Relay

In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.

The Efficient Implementation of Hybrid Power Plants in Indonesia

The energy crisis is a serious issue that all countries around the world are dealing with, particularly the energy sources used to generate electricity. There are numerous alternative energy sources available in nature, such as solar energy, wind energy, and so on. Wind and solar energy as hybrid energy sources are thought to be promising in electric generation technology. Hybrid Power Plants can also be used to address the issue of limited electrical energy supply in Indonesia's remote areas. The purpose of this study is to describe the effectiveness of the hybrid power plants implementation in Indonesia. The study was conducted using the narrative literature review method. It can be inferred that the purpose of developing this hybrid technology, among others, is to obtain more efficient electrical energy generation by combining the advantages of two or more types of power generation systems that work in an integrated manner as a compact system. The produced electric energy can be stored in the battery. In terms of use, the produced direct current (DC) is converted into alternating current (AC) using the inverter system prior to distribution to consumers. Therefore, this hybrid power plant is suitable to be implemented in Indonesia.

When Crowdsourcing Meets Unmanned Vehicles: Toward Cost-Effective Collaborative Urban Sensing via Deep Reinforcement Learning

Mobile crowdsensing (MCS) and unmanned vehicle sensing (UVS) provide two complementary paradigms for large-scale urban sensing. Generally, MCS has a lower cost but often confronts sensing imbalance and even blind areas due to the limitation of human mobility, whereas UVS is often capable of completing more demanding tasks at the expense of limited energy supply and hardware cost. Thus, it is significant to investigate whether we could integrate the two paradigms for high-quality urban sensing in a cost-effective collaborative way. However, it is nontrivial due to complex and long-term optimization objectives, uncontrolled dynamics, and a large number of heterogeneous agents. To address the collaborative sensing problem, we propose an actor-critic-based heterogeneous collaborative reinforcement learning (HCRL) algorithm, which leverages several key ideas: local observation to handle expanded state space and extract the states of neighbor nodes, generalized model to avoid environment nonstationarity and ensure the scalability and stability of network, and proximal policy optimization to prevent the destructively large policy updates. Extensive simulations based on a mobility model and a realistic trace data set are conducted to confirm that HCRL outperforms the state-of-the-art baselines.