Limited Energy Supply Research Articles

Hardware-aware neural architecture search for stochastic computing-based neural networks on tiny devices

Along with the progress of artificial intelligence (AI) democratization, there is an increasing potential for the deployment of deep neural networks (DNNs) to tiny devices, such as implantable cardioverter defibrillators (ICD). However, tiny devices with extremely limited energy supply (e.g., battery) have high demands on low-power execution, while guaranteeing the model accuracy. Stochastic computing (SC) as a new promising paradigm significantly reduces the power consumption of DNNs by simplifying arithmetic circuits, but often sacrifices the model accuracy. To make up for the accuracy loss, previous works mainly focus on either only-hardware (only-HW) circuit design or software-to-hardware (SW→HW) sequential workflow, which leads to unilateral optimization. Therefore, as the first attempt, aiming at both the hardware (HW) and software (SW) performance, we innovatively propose an HW↔SW co-exploration framework for SC-based NNs, namely SC-NAS, which is the first to couple SC with neural architecture search (NAS) for HW/SW co-optimization. We redefine the optimization problem and show a complete workflow to intelligently search for a set of configurations of NNs with hardware consumption as low as possible and accuracy as high as possible. We comprehensively explore the influencing factors, which have impacts on both the HW and SW performance for SC-based NNs, to build a search space for NAS. Furthermore, in order to improve the search efficiency of NAS, we contract the search space and set an energy constraint to early terminate unnecessary model inference. Experiments show that SC-NAS achieves up to 7.0 × energy saving than FP-NAS, and exceeds pure SC methods by over 3.8% in accuracy. Meanwhile, SC-NAS obtains around 8.6 × 1019 × search efficiency improvement than exhaustive search using VGGNet.

Efficient and optimized communication in biomedical sensor networks based on bioinspired particle swarm optimization for medical applications.

Healthcare, sports, the military, location monitoring and wireless body networks are emerging as technology of major relevance. As a result of the widespread usage of biomedical sensor networks in medical applications, it is essential that data packets containing vital signs be reliably and effectively supplied to the medical center. Because of its mobility, real-time monitoring, cheap cost, and real-time feedback, it may be used in a broad variety of applications. Effective data transport and a limited energy supply are the main challenges in WBAN. Uses genetic heuristics to enhance routing algorithm efficiency. Low-energy adaptive clustering hierarchy (LEACH) and distributed energy efficiency clustering (DEC) are two kinds of clustering algorithms (DEEC). A clustering-based routing protocol may be optimized using this study's optimization approach so that the network's lifetime can be extended.. The cluster heads (CHs) in sensor nodes are picked with the least amount of overhead grading possible. The cluster is being balanced. Passive clustering based on Bioinspired Particle Swarm Optimization (BPSO) should be used for clustering purposes. Routing messages efficiently means sending them quickly and efficiently without using a lot of bandwidth. Using constraints such as distance and residual power, the optimal path for the cluster may be determined with the help of iterative and heuristic chicken swarm optimization (IHCSO) for short. An evaluation of the packet distribution allocation, capacity, and average end-to-end latency illustrates the practicability of the proposed system in research concerning its efficiency. According to the findings of the research, following the technique that was proposed leads to much better outcomes.

An investigation of the impact of green methods and technologies on the environmental sustainability of supportive educational buildings, case study of UCT

As water and energy costs rise in South Africa due to the limited water and energy supply, green methods and technologies implemented in buildings will help alleviate the burden of this crisis. Many literatures studied green building technology; however, there is limited research on the barriers and drivers of using green methods and technology in supportive educational buildings at universities. It is essential to note the distinction between the different types of university buildings. Although the focus will be primarily based on supportive educational buildings, the overall study deals with the university as a system in a holistic view. This study makes use of a mixed-method approach, using an over-arching case study of supportive educational buildings at the University of Cape Town. A field survey of a supportive education building at UCT and semi-structured interviews were conducted with executive management at UCT. Findings reveal that across UCT’s various buildings, numerous green methods and technologies have been installed to help monitor and manage water and energy usage. Furthermore, it was indicated that the university had employed various strategies since the early 2000s to increase environmental sustainability to achieve sustainability. Additionally, there has been a shift to positively influence student behaviour through the phasing in of water and energy-saving campaigns around campus. The findings also reveal that UCT has recently prioritised green methods and technologies implementation, which inherently improves the resource efficiency and environmental sustainability of the supportive education buildings and, consequently, the overall university.

Design and Build a Vaname Shrimp Sorting System Based on Image Processing

Vannamei shrimp is a species of shrimp that has high economic value. In the process of trading vaname shrimp, there are different price classes. Determination of the price class of vaname shrimp is based on the size of the shrimp. But in the post-harvest process, the sorting of vaname shrimp is still done manually, namely by placing the white shrimp on a flat table and then separating it by size so that it takes a long time and the level of accuracy of the sorting process also becomes imprecise, as is done by cultivating in the coastal area of ​​Madura, this is due to the limitations of available shrimp post-harvest processing equipment. In addition, the limited supply of electrical energy for the coastal area of ​​Madura is also another factor that hinders the post-harvest process of vanname shrimp. The purpose of this study is to design and create a vannamei shrimp sorting system based on image processing. In processing this shrimp image using the Background Subtraction method. The Background Subtraction method is used as a separator between the object and the background. The sorting process is based on the size detection of shrimp by using the blob detection algorithm. BLOB (Binary Large Object) detection is an image segmentation method based on region growing. The goal is to analyze textures specifically and accurately. Because blob detection distinguishes colors that have thin gradations. Based on the tests that have been carried out, the average accuracy of the system in sorting vannamei shrimp is in the range of 90%.

Collaborative Energy Beamforming for Wireless Powered Fog Computing Networks

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\left({1 - \frac {1}{e}}\right)$ </tex-math></inline-formula> -approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {P} = \mathcal {NP}$ </tex-math></inline-formula> , and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks.

Construction of Landscape Ecological Planning Evaluation Model Based on Sensor Network

The application of wireless sensor network (WSN) technology promotes the modernization of forestry. WSN application technology in forest areas is an important research topic for the sustainable development of forestry in China and is also a research hotspot for forestry ecological monitoring at present. The application of wireless sensor networks in forest areas, first of all, solves the problem of limited energy supply and low-latency data transmission in the forest environment. Due to the large area of the forest environment and uneven tree density, dynamic changes in forest height, easy to block the signal, and other characteristics, the forest environment is prone to node energy depletion fast, the network life cycle is short, and data transmission delays large dilemma. Second, the application of wireless sensor networks is usually centered on maximum data acquisition, but the contradiction between high data acquisition rate and limited energy supply is inevitable, so it is necessary to construct a maximum data acquisition rate model with limited energy supply as a constraint to guarantee the optimal acquisition conditions for wireless sensor networks. In this paper, from the application of wireless sensor network technology in forest environment, the research on the application of wireless sensor network in forestry is carried out around the analysis of energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision-making fire danger warning process, so as to provide a solution to the overall problem of forest fire warning based on rechargeable wireless sensor network. In this paper, we analyze the dynamic replenishment of energy in rechargeable wireless sensor networks and propose an energy-based transmission control protocol that effectively improves data transmission efficiency. In the rechargeable wireless sensor network, the network E2E (end-to-end) average delay time is calculated based on the number of nodes on the data transmission link. The research idea of this paper starts from the application technology of wireless sensor network in the forest environment, and the design of the energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision fire danger warning process are realized vertically.

Reconciling concepts of black queen and tragedy of the commons in simulated bulk soil and rhizosphere prokaryote communities.

The Black Queen hypothesis describes the evolutionary strategy to lose costly functions in favour of improving growth efficiency. This results in mutants (cheaters) becoming obligately dependent upon a provider (black queen) to produce a necessary resource. Previous analyses demonstrate black queens and cheaters reach a state of equilibrium in pair-wise systems. However, in complex communities, accumulation of cheaters likely poses a serious burden on shared resources. This should result in a Tragedy of the Commons (ToC), whereby over-utilisation of public resources risks making them growth-limiting. With a collection of differential equations, microbial communities composed of twenty prokaryote ‘species’ either from rhizosphere, characterised by abundant carbon and energy sources, or bulk soil, with limited carbon and energy supply, were simulated. Functional trait groups differed based on combinations of cellulase and amino acid production, growth and resource uptake. Randomly generated communities were thus composed of species that acted as cellulolytic prototrophic black queens, groups that were either cellulolytic or prototrophic, or non-cellulolytic auxotrophic cheaters. Groups could evolve to lose functions over time. Biomass production and biodiversity were tracked in 8,000 Monte Carlo simulations over 500 generations. Bulk soil favoured oligotrophic co-operative communities where biodiversity was positively associated with growth. Rhizosphere favoured copiotrophic cheaters. The most successful functional group across both environments was neither black queens nor cheaters, but those that balanced providing an essential growth-limiting function at a relatively low maintenance cost. Accumulation of loss of function mutants in bulk soil risked resulting in loss of cumulative growth by ToC, while cumulative growth increased in the rhizosphere. In the bulk soil, oligotrophic adaptations assisted species in avoiding extinction. This demonstrated that loss of function by mutation is a successful evolutionary strategy in host-associated and/or resource-rich environments, but poses a risk to communities that must co-operate with each other for mutual co-existence. It was concluded that microbial communities must follow different evolutionary and community assembly strategies in bulk soil versus rhizosphere, with bulk soil communities more dependent on traits that promote co-operative interactions between microbial species.

Simultaneous Wireless Information and Power Transfer Assisted Federated Learning via Nonorthogonal Multiple Access

Federated learning (FL) has been considered as a promising approach for enabling distributed learning without sacrificing edge-devices’ (EDs’) data privacy. However, training machine learning (ML) model distributively is challenging to the EDs with limited energy supply. In this work, we consider that the central parameter-server (which is co-located with the cellular base station, BS) exploits simultaneous wireless information and power transfer (SWIPT) to simultaneously send the aggregated model to all EDs and also transfer energy to them. With the collected energy from the BS, each ED firstly trains its local model, and then all EDs form a non-orthogonal multiple access (NOMA) cluster for sending their local models to the central parameter-server (i.e., the BS). To minimize the total energy consumption, we formulate a joint optimization of the BS’s SWIPT duration, each ED’s power-splitting ratio, the EDs’ NOMA uploading duration as well as the EDs’ and BS’s processing-rates. To address the non-convexity of the joint optimization problem, we decompose it into two subproblems, both of which are efficiently solved. We then propose an enhanced block coordinate descent (EBCD) algorithm, which iteratively solves the two subproblems in sequence and exploits the idea of simulated annealing to avoid being trapped by some local optimum, to approach to the optimal solution of the original joint optimization problem. By using our EBCD-Algorithm, we further investigate how to properly select the EDs to participate in the FL process, with the objective of minimizing all participants’ total energy consumption. A cross-entropy based algorithm, which exploits our EBCD-Algorithm as a subroutine, is proposed to determine the optimal ED-selection. Numerical results are provided to validate our proposed algorithms and demonstrate the performance advantage of the proposed SWIPT assisted FL via NOMA in comparison with some conventional FL schemes.

SPEED-UP, and energy-efficient GPSR protocol for WSNs using IOT

The development of new applications for intelligent cities, medicine, agriculture and energy harvesting would be a promising concept using wireless sensor networks based on the Internet of Things (IoT-WSNs). At the moment, the research was aimed at reducing the limited energy supply of IoT devices, which has a serious impact on their architecture. To ensure rapid and stable connection, new and efficient methods of energy systems must be designed. To handle data transfer in WSNs and to handle the problem of detector energy supply, the authors presented an improved Speed Up-Greedy Perimeter Stateless Routing Protocol for Wireless Sensor Networks (SU-GPSR) and Energy Harvest Greedy Perimeters Stateless Routing Protocol (EH-GPSR). SU-GPSR, unlike other variations of GPSR algorithms, provides complete energy consumption while assessing the next jump for local nodes and mobile nodes in an integrated framework. Additionally, established measurements are recorded using the SU-GPSR skip selection method. To calculate the amount of energy collected, EH-GPSR uses an EH rate that is generated by Mobilized Minimum Path Recovery Time (MMRT) method, as it improves the method of greedy routing. To incrementally choose the next hop, it particularly uses a weighted method that merges the node's data cost with the EH rate. In both packed and systems, the simulation study infers that SU-GPSR performs better than EH-GPSR and FA-GPSR, whereas EH-GPSR increases the node life with a speed by 20% when contrasted to the other GPSR method.

ECO-CPP: Energy constrained online coverage path planning

Some robotic platforms are expected to perform coverage path planning (CPP) on a daily basis. However, at times they may be expected to perform CPP in large unknown spaces, which are not fully coverable with the onboard limited energy supply. In these cases the platforms need to revisit the charging station multiple times, increasing mission times and the total energy needed, reducing the availability of the robots. This paper proposes a novel solution to this energy constrained online coverage path planning problem, showing effectiveness of this solution in various settings. The proposed solution is based on contour following and outperforms existing work in the literature. Additionally, this work presents a new lower bound for the minimum number of charges and energy consumed for both, offline and online, energy constrained coverage path planning problems.

Spiking Neural Networks and Their Applications: A Review.

The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.

Joint task offloading and resource allocation in mobile edge computing with energy harvesting

Mobile edge computing (MEC) is considered to be a promising technique to enhance the computation capability and reduce the energy consumption of smart mobile devices (SMDs) in the sixth-generation (6G) networks. With the huge increase of SMDs, many applications of SMDs can be interrupted due to the limited energy supply. Combining MEC and energy harvesting (EH) can help solve this issue, where computation-intensive tasks can be offloaded to edge servers and the SMDs can also be charged during the offloading. In this work, we aim to minimize the total energy consumption subject to the service latency requirement by jointly optimizing the task offloading ratio and resource allocation (including time switching (TS) factor, uplink transmission power of SMDs, downlink transmission power of eNodeB, computation resources of SMDs and MEC server). Compared with the previous studies, the task uplink transmission time, MEC computation time and the computation results downloading time are all considered in this problem. Since the problem is non-convex, we first reformulate it, and then decompose it into two subproblems, i.e., joint uplink and downlink transmission time optimization subproblem (JUDTT-OP) and joint task offloading ratio and TS factor optimization subproblem (JTORTSF-OP). By solving the two subproblems, a joint task offloading and resource allocation with EH (JTORAEH) algorithm is proposed to solve the considered problem. Simulation results show that compared with other benchmark methods, the proposed JTORAEH algorithm can achieve a better performance in terms of the total energy consumption.

A new static path planning strategy for drones

Unmanned Aerial Vehicles (UAVs) have been progressively used in environmental monitoring and disaster sensing. The drones fly over a set of waypoints in the area to achieve their surveillance task. Despite their potential advantages, UAVs have several drawbacks, including a limited energy supply and a short operational duration which make all attempts at planning ineffective. In this paper, we propose a novel UAV path planning for the monitoring task that considerably reduces energy consumption and minimizes the number of turns with minimum overlapping. In addition, the proposed path gives the whole area the same level of importance. The proposed path was compared with four static paths: back and forth, spiral, LMAT, and Zamboni. Results show that the proposed path allows a better coverage with less energy consumption than the state‐of‐the‐art methods.

Achieving Longevity in Wireless Body Area Network by Efficient Transmission Power Control for IoMT Applications

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT). IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithmto extend the network lifetime in IoMT networks for healthcare applicationsby eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%.

Blockchain-Based Privacy-Preserving Networking Strategy for Dynamic Wireless Charging of EVs

Dynamic wireless charging of electric vehicles (EVs) enables the exchange of power between a mobile EV and the electricity grid via a set of charging pads (CPs) deployed along the road. Accordingly, dynamic charging coordination can be introduced for a group of mobile EVs to specify where each EV can charge (i.e., from which CPs). This coordination mechanism maximizes the satisfied charging requests given the limited available energy supply. Upon specifying the optimal set of pads for a given EV, a fast authentication mechanism is required between the EV and the CPs to start the charging process. However, both the coordination and authentication mechanisms require exchanging private information, e.g., EV identities and locations. Hence, there is a need for a strategy that enables privacy-preservation in dynamic charging via supporting: (i) user anonymity and (ii) data unlinkability. In this paper, we propose a decentralized and scalable networking strategy based on a specially designed private blockchain that can support the privacy requirements of dynamic charging coordination, authentication, and billing. The proposed networking strategy relies on group signature and distributed random number generators to support the desirable features. Simulation results demonstrate the efficiency and low complexity of the proposed blockchain-based networking strategy.

A dual-hop topology-aware routing protocol for underwater optical wireless sensor networks

Underwater optical wireless communication (UOWC) is an emerging technology for underwater wireless sensor networks (UWSNs). Compared with acoustic waves and radio frequency waves, optical waves can provide higher data rates and lower latency. However, given the limited transmission range and energy supply, as well as the existence of communication void regions in UOWC which may further deteriorate its transmission performances (e.g. packet deliver rate), effective multi-hop routing schemes are highly desirable for UOWC to expand its transmission range with satisfactory performance. In this paper, we propose a dual-hop topology-aware (DHTA) routing algorithm for UOWC. By adopting dual-hop routing strategy, the proposed algorithm reduces the packet entry possibility into void regions, and thus improves the packet deliver rate. And by selecting the next relay node considering both the available distance of transmission and the deviation of transmission direction, the proposed algorithm can effectively reduce the average energy consumption of the network. Besides, a threshold is employed by the proposed algorithm for energy balance to prevent excessive energy consumption of some nodes. The simulation results verify that the proposed DHTA routing algorithm effectively alleviates the effluence of void regions on forwarding packet and increases the packet delivery rate (PDR), with reduced average energy consumption and energy standard deviation.

SNF1-Related Protein Kinase 1 Activity Represses the Canonical Translational Machinery.

Protein biosynthesis is achieved through translation, which consumes enormous energy. Therefore, under conditions of limited energy supply, translation progress should be strictly coordinated. Sucrose non-fermenting kinase1 (SNF1)-related protein kinase 1 (SnRK1) is an evolutionarily conserved master regulator of cellular energy stress signaling in plants. Rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) SnRK1 enhance hypoxia tolerance and induce the expression of stress-related genes. However, whether SnRK1 modulates protein synthesis in plants is unknown. In this study, using translational reporter constructs transfected in Arabidopsis protoplasts we showed that the expression of OsSnRK1A and AtSnRK1.1 decreases the abundance of canonical proteins without affecting their encoding transcript levels and protein stability. Moreover, the loading of total mRNAs and GFP mRNAs into the heavy polysome fraction which is normally translated was attenuated in transgenic Arabidopsis lines constitutively expressing OsSnRK1A or AtSnRK1.1. Taken together, these results suggest that OsSnRK1A and AtSnRK1.1 suppress protein translation to maintain energy homeostasis.

A Node Deployment Optimization Algorithm of WSNs Based on Improved Moth Flame Search

Wireless sensor networks, one of the basic technologies of remote environmental monitoring, can provide efficient sensing and communication services under limited energy supply. Coverage control is an important method to ensure efficient communication and reliable data transmission. Given the complex physical environments, which impede the energy supplement and recovery of sensor nodes, the motivation of our research is to repair the coverage holes and reduce the energy consumption during the deployment of sensor nodes. Firstly, the variable spiral position update and the adaptive inertia weight strategy are adopted to improve local development and global search ability of the moth flame algorithm. Secondly, we analyze the virtual force of nodes, including the attractive force of uncovered grid points, the virtual force between adjacent sensor nodes and the repulsive force of boundary. The node resultant force is used as the disturbance factor of moth position updating to optimize the path, which effectively avoids the “premature” problem of the algorithm and accelerates global convergence. Finally, moth search is used to guide nodes to move to the area with coverage holes to achieve coverage optimization. In addition, we limit the random walking range of moths to reduce the moving distance. The simulation results show that compared with VFPSO, VFA and MFO algorithms, the coverage rate of VF-IMFO algorithm is increased by 7.16%, 3.85% and 22.2%, and the average moving distance of nodes is reduced by 9.01 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {m}$ </tex-math></inline-formula> , 0.51 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {m}$ </tex-math></inline-formula> and 32.46 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {m}$ </tex-math></inline-formula> respectively. Moreover, under different deployment environments, the VF-IMFO algorithm still maintains remarkable performance advantages.

Engineering Acetogenic Bacteria for Efficient One-Carbon Utilization.

C1 gases, including carbon dioxide (CO2) and carbon monoxide (CO), are major contributors to climate crisis. Numerous studies have been conducted to fix and recycle C1 gases in order to solve this problem. Among them, the use of microorganisms as biocatalysts to convert C1 gases to value-added chemicals is a promising solution. Acetogenic bacteria (acetogens) have received attention as high-potential biocatalysts owing to their conserved Wood–Ljungdahl (WL) pathway, which fixes not only CO2 but also CO. Although some metabolites have been produced via C1 gas fermentation on an industrial scale, the conversion of C1 gases to produce various biochemicals by engineering acetogens has been limited. The energy limitation of acetogens is one of the challenges to overcome, as their metabolism operates at a thermodynamic limit, and the low solubility of gaseous substrates results in a limited supply of cellular energy. This review provides strategies for developing efficient platform strains for C1 gas conversion, focusing on engineering the WL pathway. Supplying liquid C1 substrates, which can be obtained from CO2, or electricity is introduced as a strategy to overcome the energy limitation. Future prospective approaches on engineering acetogens based on systems and synthetic biology approaches are also discussed.

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.