Limited Energy Supply Research Articles

Influence of the Power Generation Capacity of the Structural Parameters of a Piezoelectric Bimorph

With the popularization of integrated circuits, MEMS, and portable electronic devices, chemical batteries have many disadvantages as the main energy supply method, such as large size, high quality, and limited energy supply life, requiring regular replacement, resulting in waste of materials, environmental pollution, and other issues. From the above reasons, energy harvesting technology plays an important role in improving the efficiency and life of electronic equipment. In order to explore the influence of the bimorph piezoelectric vibrator’s structural parameters on the power generation capacity, this paper establishes a cantilever beam rectangular bimorph piezoelectric vibrator power generation model, derives the mathematical expression of the bimorph piezoelectric vibrator power generation, and determines the parameter factors that affect the power generation effect. Using MATLAB simulation analysis to obtain the influence relationship curve of system output voltage and structural parameters, the experiment tests the influence law of output voltage and thickness ratio, width-to-length ratio, and Young’s modulus ratio; the test results are consistent with the theoretical analysis, verifying the theory and the correctness of the analysis. The results show that when the thickness ratio is 0.58 and the width-to-length ratio is 1, the power generation effect of the piezoelectric vibrator is the best to reach 14.5V; the power generation capacity of the transducer is inversely proportional to Young’s modulus ratio. This research provides a new idea for the popularization of integrated circuits, MEMS, and portable electronic devices.

Energy-efficient collection of wearable sensor data through predictive sampling

Wearable sensors have emerged as viable and attractive solutions for monitoring the health of people under risk of major problems such as hypertension, heart attacks, and athletes overstressing their bodies. These devices would report on the status of certain body organs to a gateway node over wireless links. A major challenge for effective use of these miniaturized devices is sustaining their operation using a limited energy supply. Therefore, minimizing energy consumption is a crucial design goal. In this paper we propose a novel approach for reducing the volume and frequency of data transmission through data sample prediction. Our methodology is based on applying advanced machine learning techniques to determine when data transmissions are skipped, and by implicitly making the gateway aware of the omitted samples in order to achieve accurate signal reconstruction. The paper also presents a data quantization technique for increased throughput and reduced energy overhead while sustaining desired medical assessment accuracy. Furthermore, a packet formation algorithm is proposed to leverage the available buffering space to improve bandwidth and energy utilization subject to latency constraints. The effectiveness of our approach is validated using publicly available Electrocardiography and Electromyography datasets and is shown not only to outperform conventional data compression methods but can also be applied in conjunction with them.

Limited-energy output formation for multiagent systems with intermittent interactions

Limited-energy output formation design and analysis problems are addressed for multiagent systems with intermittent interactions. Firstly, a new dynamic output feedback formation control protocol with the limited energy supply is proposed, which contains two independent parts associated with the interactive interval and the non-interactive interval. Then, sufficient conditions for leaderless limited-energy output formation are proposed by a new two-step design approach, which can make two gain matrices of the formation control protocol be designed independently. Meanwhile, the output formation reference function is determined to describe the absolute motion of all agents as a whole. Moreover, by constructing two transformation matrices with specific structures, the main conclusions for leaderless multiagent systems are extended into leader-follower ones. Finally, two numerical simulations are shown to demonstrate theoretical results.

Formation tracking for singular swarm systems subjected to energy constraints and switching transmission topologies

AbstractThis article investigates admissible formation tracking for singular swarm systems under conditions of switching transmission topologies as well as the limited energy supply, where both switching transmission topologies containing a spanning tree and switching transmission topologies containing a joint spanning tree are addressed. First, a new formation control tracking protocol with switching transmission topologies is proposed to realize admissible formation tracking, where the total energy supply is predetermined. Afterwards, by a dual nonsingular transformation, the disagreement dynamics between followers and the leader is determined and the impacts of asymmetric structures of switching transmission topologies are eliminated. Sufficient conditions for admissible formation tracking design and analysis for singular swarm systems with switching transmission topologies containing a spanning tree and with switching transmission topologies containing a joint spanning tree are proposed, respectively, where the coupling relationship matrix between the total energy supply and the matrix variable is determined. Finally, two numerical simulations are presented to demonstrate the effectiveness of theoretical results.

Development of approaches to emergency management in conditions of limited energy supply of the zone of their localization

The solution of the problem of developing approaches to the management of the emergency prevention process in the conditions of limited energy supply of emergency response measures is considered. Within the framework of solving the problem, the process of emergency management in the conditions of limited energy supply is analyzed and the scheme of managerial influence on the process of spreading the emergency situation in the conditions of limited energy supply is developed. A structural and logical model of emergency management in conditions of limited energy supply has been developed. Developed a structural and logical model of emergency management in conditions of limited energy supply, which consists of twelve units. These are monitoring of the situation, identification of risks, notification and evacuation of people, search and identification of the danger of emergence and spread of emergency in conditions of limited energy supply, decision-making on the use of special equipment for electricity generation, preparatory measures, information control measures. measures in case of spread of danger, localization of sources of spread of danger, decision-making on recovery without quarantine regime in the region of hostilities, impact on the situation, analysis of the effectiveness of preventive measures and emergency prevention measures. The location of the units on six hierarchical levels, connected by logical connections, provides information on the state of the region in conditions of limited energy supply through the elements of the monitoring system and impact on the region through the system of performers, providing a continuous management process to minimize the consequences of emergency. The obtained results allow, using innovative information and technical methods of emergency prevention of such nature, to further form a modern method of emergency response in conditions of limited energy supply of emergency response measures, supplementing the above methods with an effective crisis management system. Key words: emergency situation, structural-logical model, conditions of limited energy supply.

Environment-aware communication channel quality prediction for underwater acoustic transmissions: A machine learning method

Due to the limited energy supply of sensor nodes in underwater acoustic communication networks (UACNs), energy optimization for underwater acoustic transmissions is critical to prolong the network lifetime and improve network performance. Machine learning is a powerful and promising method that can be used to optimize energy consumption of UACNs. In this paper, we propose a machine learning based environment-aware communication channel quality prediction (ML-ECQP) method for UACNs. In ML-ECQP, the logistic regression (LR) algorithm is used to predict the communication channel quality (which is measured according to the bit error rate) between a transmitter and a receiver based on the perceived underwater acoustic channel environmental parameters (such as signal-to-noise ratio, underwater temperature, wind speed, etc.). Based on the predicted communication quality, each transmitter can optimize the acoustic data transmissions in order to minimize the energy waste caused by retransmissions, thus significantly reducing the energy consumption of UACNs. Extensive experiments are conducted in the Furong Lake at Xiamen University to demonstrate the performance (in terms of the feasibility, channel condition predication accuracy, and energy consumption reduction) of the proposed ML-ECQP method.

Application-Oriented Traffic Modeling of WiFi-Based Internet of Things Gateways

Many Internet of Things (IoT) devices generate relatively small-sized data and have limited energy supply. These two factors limit their ability to connect directly to cloud servers through a wireless backbone network without imposing a burden on this network in providing efficient data transfer. In this article, we consider an IoT network architecture where a number of different IoT devices send their data wirelessly to an IoT gateway (or a fog node) via a WiFi network. We focus on characterizing incoming traffic patterns to the gateway for three typical IoT applications with real-time and nonreal-time data transfer requirements, such as video surveillance, smart city, and e-healthcare. Our study is based on generating real IoT traffic traces in a lab environment from various sensors and devices for the aforementioned applications and employing these traces to emulate a network of IoT nodes connected to a gateway via WiFi. In the conducted experiments, different homogenous and nonhomogeneous traffic patterns of the selected applications are examined for synchronized and unsynchronized data sources. Based on our empirical data, the experimental results reveal that the packet interarrival time distribution at the gateway is close to generalized Pareto distribution for homogeneous eHealth and smart city traffic, whereas the Weibull distribution is the nearest to model the empirical packet interarrival time for the rest of the examined traffic patterns. Moreover, we show that employing the experimental findings to analyze the delay performance of connecting the gateway to the cloud, given certain backbone network resources, leads to accurate results.

Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization

In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we investigate energy minimization for UAV-aided communication networks by jointly optimizing data-transmission scheduling and UAV hovering time. The formulated problem is combinatorial and non-convex with bilinear constraints. To tackle the problem, firstly, we provide an optimal algorithm (OPT) and a golden section search heuristic algorithm (GSS-HEU). Both solutions are served as offline performance benchmarks which might not be suitable for online operations. Towards this end, from a deep reinforcement learning (DRL) perspective, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm and develop a set of approaches to confine the action space. Compared to conventional RL/DRL, the novelty of AC-DSOS lies in handling two major issues, i.e., exponentially-increased action space and infeasible actions. Numerical results show that AC-DSOS is able to provide feasible solutions, and save around 25-30% energy compared to two conventional deep AC-DRL algorithms. Compared to the developed GSS-HEU, AC-DSOS consumes around 10% higher energy but reduces the computational time from second-level to millisecond-level.

Delay-Sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware (DREAM) Routing Protocol for WSNs

A majority of Wireless Sensor Network (WSN) research at present is focussed on the problems of limited energy supply and its impact on network lifetime. Nevertheless, the plethora of applications conceivable with the help of WSNs often demand for MOO (Multi-Objective Optimization) formulations, where several design goals contend together for the best trade-off solution among them. Therefore, research investigators must also regard other miscellaneous issues in addition to energy efficiency for applicability of WSNs in practical scenarios like Internet of Things. DREAM (Delay-sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware) routing protocol is proposed in the present work, that ameliorates network lifetime (in terms of First Node Death and Last Node Death), throughput (in terms of number of packets sent to Base Station) and latency (average end-to-end delay in seconds) in the network along with enhancing the reliability (in terms of percentage packet loss) of delivered data. The proposed protocol also integrates mobility and heterogeneity of the nodes to cater to the needs of an application-independent general purpose WSN routing protocol, which can be used commercially. Comparative analysis with existing protocols establishes the superiority of the proposed protocol, which is capable of improving the network lifetime by about 3.54% and simultaneously lowering the delay by 35.5%, along with the amelioration of other parameters.

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.

A novel energy harvesting with middle-order weighted probability (EHMoWP) for performance improvement in wireless sensor network (WSN)

Wireless sensor network (WSN) has been a promising technology and widely involved in environmental monitoring. The limited energy supply associated with WSNs impacts the lifetime of the network and the energy constraints make it difficult for sensor nodes to harvest environmental energy. This drawback on the performance of WSN is improved through routing and clustering techniques for environmental energy harvesting and termed as harvesting wireless sensor network (EH-WSN). As a result of the unique characteristics of EH-WSN, the performance of typical clustering and routing protocols of WSN are ineffective in EH-WSN. A novel energy harvesting middle-order weighted probability (EHMoWP) is proposed in this paper for the static and dynamic operation of WSN nodes. The proposed EHMoWP approach is based on the integration of cluster head selection and routing, with the utilization of a distributed–centralized approach which cogitates the energy of nodes, harvested energy, clustering process, and neighbor nodes. Simulation outcomes showed that this proposed EHMoWP outperforms significantly the conventional techniques concerning residual energy and the number of alive and dead nodes. The proposed EHMoWP exhibits almost 10% higher residual energy with improved network lifetime which is measured in terms of alive node and dead node count. Based on the analysis of dead node and alive node estimation, it was observed that the number of alive nodes was 10% higher and the number of dead nodes was 10% fewer than in the conventional technique. This is because the proposed EHMoWP exhibits 2 dead nodes less and 2 alive nodes higher than the existing technique.

Energy-efficient location-based routing algorithms for three dimensional mobile ad hoc networks

Wireless ad hoc network nodes suffer from limited energy supply. In the current location-based topology control algorithms a node discovers its neighbors by exchanging periodic hello messages between neighbors using the maximum transmission power of each node. This could be a source of extreme power consumption. This paper suggests several new location-based routing algorithms for mobile ad hoc networks that try to increase a network lifetime by decreasing the average power consumption of the network nodes. In the newly proposed algorithms, if the node is not moving it uses less frequent hello messages. During the movement of the node, the hello messages frequency increases. The new algorithms use adjusted transmission power for routing messages as well. We compare by simulation the power consumption of the new algorithm with the current location-based routing algorithms to show a substantial improvement in the overall network lifetime.

A Novel Concept of Reducing Carbon Footprint through Green Energy Recovery at NRL (Huge Power Generation at No Extra Cost)

Energy conservation has emerged as one of the major issues in the recent years due to the increasing gap between the ever rising demand & the limited supply of energy. On one hand, Energy requirement in our country is increasing in a very rapid rate. On the other hand due to this, the available Energy resources are depleting very fast. So, conservation and efficient utilization of energy resources is the first and most important step towards narrowing down this gap. The need of the hour is to utilize the available energy resources efficiently, wisely & judiciously and at the same time minimize the wastage and unnecessary utilization of the resources. We need to discover the hidden and untapped areas which have good potential of energy and thus reduce the rate of consumption of available non-renewable sources and fossil fuels. The other very important and undeniable fact of the today’s world is that the rate of emission of Greenhouse gases is increasing day-by-day leading to Global Warming and various undesirable changes in the environment we are living in today. If we do not take necessary corrective actions today then it would be too late for tomorrow and our Future generation would suffer Keeping in view the importance of the future generation and with an objective of Energy conservation and to reduce some part of the Global Warming by reduction in harmful Green-house gas emission; an innovative and novel concept was devised in our Numaligarh Refinery Plant (NRL) in it’s post expansion scenario.

Energetic and exergetic evaluation of methanol synthesis process in a hybridized system of methane cracking, chemical looping combustion, thermal desalination and photovoltaic panels

Due to limited energy supply sources and environmental issues, the use of renewable energy to replace fossil fuels and reduce pollution has increased. One of the easiest, safest, and most portable ways to store renewable energy for a long time is to convert it to liquid methanol. In this paper, a novel integrated system is developed for cogeneration of liquid methanol and freshwater using methane cracking unit integrated with chemical looping combustion, methanol production cycle, multi-effect desalination, and photovoltaic panels. The thermal integration of new structures for cogeneration leads to a reduction in the number of equipment used and an increase in efficiency. This integrated structure produces 23.97 kmol h−1 liquid methanol, 204.3 kmol h−1 desalinated water, 42.49 kmol h−1 solid carbon, 58.52 kmol h−1 nitrogen, and 668.9 kmol h−1 hot water. The waste heat of the chemical looping combustion is used to supply methane cracking unit, which produces 84.99 kmol h−1 hydrogen, 7.582 kmol h−1 carbon dioxide, and 24.74 kW power. These products and the excess carbon dioxide supplied from outside are used as input feed for the liquid methanol production cycle. Waste heat from the liquid methanol production cycle is used to supply heat to the thermal desalination cycle and produce hot water. The thermal energy and exergy efficiencies of the integrated structure are 48.64% and 71.68%, respectively. In the hybrid structure, the largest share of exergy destruction belongs to reactors (65.61%), photovoltaic panels (17.73%), and heat exchangers (10.97%), respectively. Sensitivity analysis in different operating conditions is used to investigate the sensitivity and changes in the output and important parameters of the process.

A Scalable Hardware Accelerator for Mobile DNA Sequencing

DNA sequencers are being miniaturized and increasingly targeted toward mobile applications. However, the intense bioinformatic computing needs of sequencers present a challenge for remote use with limited energy supply. This article presents a step toward realizing a low-power and high-speed bioinformatic engine, a hardware-accelerated basecaller, for mobile sequencing applications. The design is targeted for nanopore-based sequencers and is architected to easily scale to the complexity of this sensor. In addition to accelerating the CPU in real time with a custom field-programmable gate array (FPGA) through a high-speed serial link, the proposed framework envisages the challenging memory requirement of high-order nanopore sensors. The framework proposes a memory management scheme, which provisions the memory requirement problem in three dimensions: the basecalling speed, the circuit's area, and power consumption. The implementation results demonstrate a $142\times $ basecalling speed improvement over a 12-core CPU-only reference, as well as significant speedup compared with other existing solutions. Also, an energy efficiency improvement of three orders of magnitude is measured.

An Adaptive Asynchronous Wake-Up Scheme for Underwater Acoustic Sensor Networks Using Deep Reinforcement Learning

Underwater acoustic sensor networks (UWSNs), acting as a reliable and efficient infrastructure of the Internet of underwater things (IoUT), have attracted much research interest in recent years due to the wide range of their potential marine applications. The limited energy supply of underwater sensor nodes is a significant challenge that can be mitigated by the cyclic difference set (CDS)-based coordination asynchronous wake-up scheme. However, the CDS-based asynchronous wake-up scheme also introduces long delays in the neighbor discovery that deteriorates packet delay as well as the network lifetime. In this paper, we formulate the problem of policy selection for idle listening as a Markov decision process and exploit the framework of deep reinforcement learning to obtain the optimal policies of underwater sensor nodes. Furthermore, the long short-term memory (LSTM) networks are utilized to estimate the network traffic feature, which can improve the performance of the proposed adaptive asynchronous wake-up scheme. To verify the performance of the proposed scheme, simulations in different network scenarios are conducted with the comparison of random, fixed metric policies, and original CDS-based asynchronous wake-up schemes.

Security challenges of Internet of Underwater Things: A systematic literature review

AbstractWater covers approximately 71% of the earth surface, yet much of the underwater world remains unexplored due to technology limitations. Internet of Underwater Things (IoUT) is a network of underwater objects that enables monitoring subsea environment remotely. Underwater Wireless Sensor Network (UWSN) is the main enabling technology for IoUT. UWSNs are characterised by the limitations of the underlying acoustic communication medium, high energy consumption, lack of hardware resources to implement computationally intensive tasks and dynamic network topology due to node mobility. These characteristics render UNWSNs vulnerable to different attacks, such as Wormhole, Sybil, flooding, jamming, spoofing, and Denial of Service attacks. This article reviews peer‐reviewed literature that addresses the security challenges and attacks on UWSNs as well as possible mitigative solutions. Findings show that the biggest contributing factors to security threats in UWSNs are the limited energy supply, the limited communication medium and the harsh underwater communication conditions. Researchers in this field agree that the security measures of terrestrial wireless sensor networks are not directly applicable to UWSNs due to the unique nature of the underwater environment where resource management becomes a significant challenge. This article also outlines future research directions on security and privacy challenges of IoUT and UWSN.

Design and Operation Optimization of Distributed Solar Energy System Based on Dynamic Operation Strategy

The rapid urbanization in Northwest China highlights the mismatch of increasing energy demand and limited local energy supply. Nevertheless, the remote areas in Northwest China are abundant with rich solar energy resources and land space resource. Therefore, establishing a distributed solar energy system (DSES) is a feasible solution to the energy supply problem in remote Northwest China. Due to the strong fluctuations in the availability of solar energy, operation strategies based on fixed parameters may not ensure optimal operation of DSESs. In this study, dynamic operation strategies that allocate surplus power from photovoltaic panels according to variable ratios were developed in both grid-connected and off-grid scenarios, a joint optimization model for optimizing the design and operation of a DSES was established based on the dynamic operation strategies, and a DSES of a residential building in Shaanxi Province was used as a case study. The analysis results indicate that: (1) The dynamic operation strategy can effectively reduce the operating cost of the DSES in both the grid-connected and off-grid scenarios, and the efficiency of the proposed strategy can be further enhanced by increasing the difference between peak and valley time-of-use electricity prices in the grid-connected scenario; (2) the difference between peak and valley time-of-use electricity prices has a significant impact on the optimal capacity of the batteries in the grid-connected scenario when the dynamic operation strategy is implemented. The greater the difference between peak and valley time-of-use electricity prices, the greater the configured capacity of the batteries; (3) in terms of abandoned photovoltaic power in the off-grid scenario, the three operation strategies considered in this study can be arranged in an ascending order (i.e., strategy B, strategy A, and the dynamic operation strategy). The dynamic operation strategy achieves a reduction of 12.4% in abandoned photovoltaic power compared with strategy A and a reduction of 45.4% compared with strategy B.

The nicotinamide phosphoribosyltransferase antagonist FK866 inhibits growth of prostate tumour spheroids and increases doxorubicin retention without changes in drug transporter and cancer stem cell protein expression.

Nicotinamide phosphoribosyltransferase (NAMPT) is a rate-limiting enzyme for nicotinamide adenine dinucleotide (NAD) synthesis and is involved in cancercell proliferation through regulation of energy production pathways. Therefore, NAMPT inhibitors are promising drugs for cancer therapy by limiting energy supply of tumours. Herein, we demonstrated that the NAMPT inhibitor FK866 ((E)-N-(4-(1-Benzoylpiperidin-4-yl)butyl)-3-(pyridin-3-yl)acrylamide) dose-dependently inhibited growth and cell motility of DU-145 prostate tumour spheroids and decreased the intracellular ATP concentration. The apoptosis marker cleaved caspase-3 remained unchanged, but the autophagy marker microtubule-associated protein 1A/1B-light chain 3 (LC3) was upregulated. Growth inhibition was reversed upon co-administration of NAD to the cell culture medium. FK866 decreased calcein as well as pheophorbide A efflux from tumour spheroids and increased doxorubicin toxicity, indicating interference with function of drug efflux transporters. DU-145 multicellular tumour spheroids expressed the stem cell associated markers CD133, CD44, Oct4, Nanog, Sox2, and drug transporters ABCB1, ABCG2, and ABCC1 which are associated with stem cell properties in cancer cells. The ABCB1 inhibitor zosuquidar, the ABCG2 inhibitor Ko143, and the ABCC1 inhibitor MK571 increased calcein retention. Neither protein expression of stem cell markers, nor drug transporters was significantly changed upon FK866 treatment. In conclusion, our data suggest that FK866 inhibits prostate cancer cell proliferation by interference with the energy metabolism, and function of drug efflux transporters.

Metabolic Gatekeepers of Pathological B Cell Activation.

Unlike other cell types, B cells undergo multiple rounds of V(D)J recombination and hypermutation to evolve high-affinity antibodies. Reflecting high frequencies of DNA double-strand breaks, adaptive immune protection by B cells comes with an increased risk of malignant transformation. In addition, the vast majority of newly generated B cells express an autoreactive B cell receptor (BCR). Thus, B cells are under intense selective pressure to remove autoreactive and premalignant clones. Despite stringent negative selection, B cells frequently give rise to autoimmune disease and B cell malignancies. In this review, we discuss mechanisms that we term metabolic gatekeepers to eliminate pathogenic B cell clones on the basis of energy depletion. Chronic activation signals from autoreactive BCRs or transforming oncogenes increase energy demands in autoreactive and premalignant B cells. Thus, metabolic gatekeepers limit energy supply to levels that are insufficient to fuel either a transforming oncogene or hyperactive signaling from an autoreactive BCR.