Balance Energy Consumption Research Articles

Localization for Wireless Sensor Networks Assisted by Two Mobile Anchors with Improved Grey Wolf Optimizer

Localization is crucial to wireless sensor networks. Among the recently proposed localization algorithms, the mobile anchor-assisted localization (MAL) algorithm seems promising. A MAL algorithm using a single mobile anchor has low energy consumption but a high localization error. Conversely, a MAL algorithm with three or more mobile anchors has minor localization errors but high energy consumption. By balancing energy consumption and localization accuracy, our study developed a localization algorithm assisted by two mobile anchors. A mobile anchor traverses the network along a double anchor SCAN (DASCAN) path, which divides the deployment region into grids and requires the two mobile anchors to traverse different horizontal lines in a zigzag pattern. Sensor nodes estimate their locations using a multiple-disturbance strategy grey wolf optimization (MDS-GWO) algorithm, which improves optimization by introducing a nonlinearly decreasing weight, a random perturbation of grey wolves and a mirror grey wolf. Using MATLAB, DASCAN was compared with GTURN, GSCAN, PP-MMAN, H-Curves, M-Curves, and SCAN paths by their energy consumption and localization rates. The localization error of MDS-GWO was compared with trilateration, PSO, WOA, and GWO. The impacts of radio irregularity, radio radius, and fading effect on MDS-GWO with different paths were also analyzed. The simulation results showed that the energy consumption of DASCAN was, on average, 30.1% less than GSCAN, GTURN, and PP-MMAN, but they had almost the same localization accuracy. The energy consumption of DASCAN was an average of 18.67% more than M-Curves, H-Curves, and SCAN, but the localization error of DASCAN was average of 32.3% less than SCAN, H-Curves, and M-Curves. The localization error of MDS-GWO was average of 25.5% less than trilateration, PSO, WOA, and GWO. Moreover, the performance of the proposed algorithm was less affected by different setups than the compared methods.

An energy fault and consumption optimization strategy in wireless sensor networks with edge computing

With the operation of wireless sensor networks (WSNs), energy-constrained sensor nodes and inadequate energy constraint methods are becoming obsolete, as they reduce the efficiency of data transmission. The appearance of edge computing (EC) and causality graph provide a new opportunity for energy fault analysis and assessment in WSNs. As a result, by selecting a reliable data transmission path and averaging the energy consumption, we present an energy fault and consumption optimization (EFCO) algorithm for solving the energy hole and consumption balance (EHCB) problem in wireless sensor networks with edge computing (ECWSNs). Specifically, we first build a novel four-layer network architecture by using edge computing technology and causality graph theory. Then, the energy fault cost (EFC) in ECWSNs is formulated as an optimization problem that is constrained by the energy allocation of relay nodes. Furthermore, we propose a causal reasoning algorithm to deduce the single-value fault status probability of relay nodes. Finally, we utilize version 2 of a network simulator (NS-2) to evaluate the fault derivation and energy allocation efficiency of the EFCO algorithm in ECWSNs.

Motion planning for an under-actuated autonomous underwater vehicle based on fast marching nonlinear model-predictive quantum particle swarm optimization

The motion planner determines the autonomy of the autonomous underwater vehicle. However, many studies often ignore the characteristics of autonomous underwater vehicles and the underwater environment, resulting in unreasonable trajectories. To solve this problem, this paper plans the motion force through prediction and optimization to integrate the characteristics of the autonomous underwater vehicle. The fast travel algorithm is modified to take advantage of favorable currents and output arrival times, taking into account the peculiarities of the underwater environment. A novel objective function is proposed, which incorporates the arrival times to imply the requirements of obstacle avoidance and distance optimization. A penalty item is included in the objective function to avoid strong side flow. At the same time, the force and its changing rate limited in appropriate ranges are also included to balance energy consumption. In order to meet the high running time requirements in practical applications, the warm-start improved quantum particle swarm optimization is introduced into force optimization. Simulation studies are performed on a self-developed autonomous underwater vehicle. The method has good application effects in multi-island areas, ocean currents, and dynamic three-dimensional environments. Comparative simulations show that the planner plans shorter paths with good robustness and adaptability.

Joint Optimization of Energy Consumption and Data Transmission in Smart Body Area Networks.

In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance.

An Adaptive Multipath Routing Method Based on Improved GA and Information Entropy

With the development of artificial intelligence technology, some emerging applications (such as computation offloading and digital twin) require higher transmission quantity and quality for wireless sensor networks (WSNs). The increasing amount of data aggravates the imbalance of node energy consumption. In the single-path routing, nodes on the optimal path consume too much energy, which leads to the premature failure of the network. In addition, the transmission protocols based on the acknowledge character (ACK) mechanism ensure the quality of service, but bring additional energy overhead. In this article, an adaptive multipath routing method based on balanced energy consumption (AMRBEC) is proposed for WSNs that significantly reduce energy consumption and prolong network lifetime. We design a new multidimensional fitness function based on the path energy, distance, hops and the lowest energy node, which servers to an improved genetic algorithm (GA) to select the multiple optimal paths. The packet loss rate is predicted by random forest to realize the non-feedback transmission. According to the residual energy and the information entropy of each path, the amount of data transmitted is dynamically adjusted, and the optimal paths are updated to balance the energy consumption of the whole network. A simulation analysis shows that the proposed AMRBEC performs better than other algorithms from the literature by improving the lifetime by 20% and reducing energy consumption by more than 20%.

A fuzzy based approach to enhance the lifespan of sensor network

The use of wireless sensor networks (WSNs) for data collection is widespread. The resource constraint is an important factor in WSN communications design. The issue arises naturally in WSNs as a result of uneven energy consumption caused by multi-hop routing and dynamic network models, which substantially affects network lifetime. The nodes are dispersed over distant sensing areas and are powered by finite or limited energy batteries that are difficult to replace. The energy of nodes is reduced as a result of changes in network topology or the network’s lifespan and the main intention of this research is to figure out how to make sensor networks last longer. The suggested study work focuses on a specific routing strategy for WSNs that employs the AO-star algorithm with a Fuzzy approach and link stability for extending the network lifetime. The technique chooses the optimum routing path by the sensing point to the receiving node based on how much energy is consumed, the smallest number of nodes with the shortest latency, and lower transmission loads with higher throughput. To compare the proposed strategy’s efficiency in energy consumption balancing and network lifespan enhancement, the proposed technique may achieve a 30% longer average network lifetime than the A-star algorithm.

Exchange rate stability in the emerging economies: does renewable energy play a role-a panel data analysis.

Due to the high and rising rates of carbon emissions, the use of renewable energy sources has been encouraged to help achieve carbon neutrality goal. However, renewable energy sources are said to be expensive than fossil fuels. Major studies have been undertaken to ascertain the association between renewable energy and many economic indicators, such as gross domestic product, employment rate, and inflation rate. The current study is aimed at investigating whether renewable energy use helps stabilize the foreign exchange rate of emerging economies, which has not been widely examined in the past, hence the study originality. Stability in the foreign exchange rate of a nation is very crucial as this helps to stabilize the inflation rate. This study employs the fully modified ordinary least and dynamic ordinary least square methods to analyze panel data of emerging economies. The findings indicate that high real interest rate and gross domestic product causes appreciation in the currency exchange of a country, while high balance of payment, inflation rate and renewable energy consumption are found to cause currency depreciation. The Pedroni and Kao cointegration tests are employed and the results show that a long-run relationship exists among the variables examined. This research recommends balance of payments and inflation rate to be minimized if exchange rate stability is to be achieved, while gross domestic product and real interest rate should be increased.

Catenarian-Trim Medley Routing System for Energy Balancing in Dispensed Computing Networks

Balancing energy consumption to prolong the life of a dispensed computing network is a challenging issue for researchers. In multi-hop transmission, most of the sensor node energy is depleted for the data transmission from the sensor node to the sink and receiving data from other sensor nodes. The energy consumption in the wireless sensor networks (WSN) is directly proportional to the square of the distance between source and destination. While receiving the data, energy consumption is fixed but can play a considerable role. By reducing or minimizing the transmission distance and utilizing the optimal distribution of nodes can control the energy consumption rate between one node to another. This approach leads to performance improvement in the dispensed computing networks. Chain-based routing is a novel idea where each sensor node receives data from one sensor node and transmits it to the nearest neighbor node in the same chain. This strategy results in the formation of a single chain of all nodes and balances the energy consumption. In this paper, a hybrid energy-efficient scheme combining the chain and tree-based routings has been proposed. The proposed Catenarian-Trim Medley (CTM) routing scheme has been investigated to optimize the transmission distance and energy consumption balancing for the dispensed computing networks.

Energy-conscious maintenance and production scheduling for single machine systems under time-of-use tariffs

In view of the joint optimization problem of preventive maintenance and production scheduling for modern production systems under time-of-use tariffs, a two-stage joint decision-making policy is proposed to achieve the peak-shifting reduction of production power. In the first stage, a dynamic preventive maintenance schedule is sequentially obtained based on the availability of machine. In the second stage, the production scheduling optimization of multi-workpiece processing is further carried out. The power consumption cost and the delay penalty cost under the time-of-use electricity tariff are considered, and the mixed integer programming model is established to achieve the balance of energy consumption and production delay. Numerical experiments have shown that by reasonably planning the idle time at the time of production batch conversion, the proposed model can effectively shift the on-peak power demand to off-peak, meet the stable electricity demand of enterprises, and improve the sustainable utilization level of power.

Energy efficient with prolonging lifetime in homogeneous wireless sensor networks

In this artical, we suggest a new clustering model called the Fuzzy Low Energy Adaptive Clustering Hierarchy Protocol (FLEACH). It has the ability to go around obstacles and fix the issue of uneven energy consumption in homogenous wireless sensor networks (WSNs). In order to determine the best routing path for the homogeneous WSNs, we also suggest the Ant Colony Routing Method (ACORM), which is an energy-efficient routing technique. ACORM aims to investigate issues relating to balancing energy consumption and maximizing network life. To illustrate FLEACH-ACORM's effectiveness in managing energy consumption and optimizing homogeneous network life, we compare our system with two approaches: Power-Efficient Sensor Information Systems (PEGASIS) and LEACH. Results of the simulation indicate that the network lifetime achieved by FLEACH-ACORM could be increased by almost 28.5 % and 19.8 % more than that obtained by LEACHPEGASIS, LEACH clustering protocols, respectively.

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

Connected eco‐driving for electric buses along signalized arterials with bus stops

AbstractWith the rapid development of the cooperative vehicle infrastructure system, eco‐approach and departure has been attracting increasing attention for mitigating the impact of signalized intersections, which helps improve the energy efficiency of equipped vehicles. However, existing studies have rarely considered the balance of electric bus energy consumption and traveling time. Meanwhile, the joint effect of bus stops and signalized intersections, and their interactions were not discussed widely with connected and automated electric buses (CAEBs). In this study, an integrated CAEB trajectory planning methodology with the balance of traveling time and energy consumption is proposed. A bi‐level programming model is developed to obtain the optimal trajectories of CAEBs. Numerical experiments are conducted to analyse and compare the model with two baseline models. The results demonstrate the optimal performance of the derived scheme in terms of bus energy consumption, with approximately 30–50% energy savings. Furthermore, model sensitivity analysis is conducted to quantify the impacts of the related system parameters. The findings also reveal that the entry time, conversion coefficient, and final speed have significant impacts on electric bus energy consumption along signalized arterials with bus stops.

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy.

The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.

LEACH Clustering Routing Protocol Based on Balanced Energy Consumption

<p>Faced with the problems of unbalanced energy consumption (EC) and short lifetime of nodes in Wireless Sensor Networks (WSN), a Low Energy Adaptive Clustering Hierarchy (LEACH) clustering routing protocol based on energy balance, namely LEACH-EB (LEACH Based on Energy Balance) protocol was proposed. At the initial selection stage, the nodes which are close to the base station (BS) with great remaining energy and many neighbor nodes are selected as the cluster heads (CHs); then, the non-CH nodes enter the clusters which have the least costs based on the strength and remaining energy of the communication signals between themselves and different CHs. At the data transmission stage, if the CH which sends the information is one hop away from BS, the CH needs to select a neighbor CH with the largest forwarding probability as the next hop relay node based on the remaining energy of each neighbor CH, the number of nodes in the cluster, and the distance from BS. The selected neighbor CH continues to determine the next hop in the above manner until the data is successfully sent to BS. Simulation tests show that LEACH-EB protocol can receive more data and extend the network life cycle by 60%, 43.1%, and 13.36% compared with LEACH, LEACH-C, and FIGWO, respectively. </p> <p> </p>

A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation.

Bio-inspired neuron models are the key building blocks of brain-like neural networks for brain-science exploration and neuromorphic engineering applications. The efficient hardware design of bio-inspired neuron models is one of the challenges to implement brain-like neural networks, as the balancing of model accuracy, energy consumption and hardware cost is very challenging. This paper proposes a high-accuracy and energy-efficient Fast-Convergence COordinate Rotation DIgital Computer (FC-CORDIC) based Izhikevich neuron design. For ensuring the model accuracy, an error propagation model of the Izhikevich neuron is presented for systematic error analysis and effective error reduction. Parameter-Tuning Error Compensation (PTEC) method and Bitwidth-Extension Error Suppression (BEES) method are proposed to reduce the error of Izhikevich neuron design effectively. In addition, by utilizing the FC-CORDIC instead of conventional CORDIC for square calculation in the Izhikevich model, the redundant CORDIC iterations are removed and therefore, both the accumulated errors and required computation are effectively reduced, which significantly improve the accuracy and energy efficiency. An optimized fixed-point design of FC-CORDIC is also proposed to save hardware overhead while ensuring the accuracy. FPGA implementation results exhibit that the proposed Izhikevich neuron design can achieve high accuracy and energy efficiency with an acceptable hardware overhead, among the state-of-the-art designs.

The Study of Architectural Geometry and Shape in the Energy Balance of Glazed Roofs

Triggered by the global call for low-carbon design, the idea of “productivity for a building envelope” has permeated the role of building to produce alternative (renewable) energy alongside weather and solar protection, and the authors hypothesized that the geometrical shape and configuration of a roof is a significant contributor to low-carbon design. Bibliometric networks such as VOS Viewer revealed a gap among most research works which have yet to discover “roof geometry” as a design determinant for photovoltaic electricity production. In this study, the authors tested their hypothesis by studying and comparing the balancing of solar energy harvesting and energy consumption and saving due to the uncontrolled admittance of daylight, glare, and solar heat gain of different geometric shapes of roofs in the subtropical climate. Twelve recent signature public buildings in Shenzhen city are studied for the tendency of architectural geometry of roof shapes. These roof shapes are then simplified and classified into three distinct geometries—square, pyramidal, and curvilinear—for comparative study of the best-performing low-carbon architectural geometry. The results of the simulations using the “Daysim” and “Energy-Plus” models show the desirability of an optimized design. The preliminary findings shed light on the preferred use of specific roof shapes for enhanced PV output. The curvilinear geometry has been shown to be the most effective of all. This study targeted the roof potentials by multiple criteria and a parametric evaluative protocol for building design known as the energy balance paradigm. This research paves the way in (1) changing the impression of the roof as a mere weather protector to that of a “productive roof” in response to the global call for carbon neutrality, (2) raising the awareness of architectural geometry (i.e., the building envelope), focusing on the roof form and its shape in response to low-carbon design requirements, and (3) identifying multiple criteria for the low-carbon design of architectural roof geometry.

Comprehensive Comparison of Hybrid Cooling of Thermal Power Generation with Airside Serial and Parallel Heat Exchange

Natural draft hybrid cooling (NDHC) for thermal power generating units is proposed to achieve a balance of energy and water consumption for arid areas. This study examines the two main design forms of hybrid cooling with airside in serial and parallel heat exchange based on the same tower shell and heat transfer areas. Taking full consideration of the thermal cycle of the power generating unit, simplified simulation models for different cooling systems are established to show the influences of ambient conditions and marketing factors. Results show that both the hybrid cooling designs have a better cooling efficiency than either dry cooling or wet cooling. Expanded inlet areas of hybrid cooling in the parallel heat exchange design bring high heat transfer performance. As for the serial design, the higher temperature of the air at the outlet of the dry section maintains a larger airside mass flow rate, obtaining a high-efficient cooling system. The hybrid cooling in the serial design type relies more on the heat transfer performance of the wet section and is more sensible to ambient humidity, while the performance of hybrid cooling in the parallel design mainly depends on the dry section and is more easily affected by ambient temperature. Considering the unit cost variations of coal and water treatment, hybrid cooling in the parallel design has a wider range of applications compared with the serial design. With the growth in coal cost, there exist more benefits with the serial design.

Sustainable development of the economy in the conditions of the energy crisis

Purpose. To establish the factors of the impact of the energy crisis on the formation of a sustainable economy. To propose a method for forecasting the balance of energy production and consumption. To establish the level of correlation between sustainable development indicators and energy indicators. Methodology. Cognition methods were used to perform the work: analysis and synthesis to choose the topic and purpose of the study; content analysis for literature review; critical analysis to identify unsolved aspects of the problem and national characteristics of the crisis; the ascent from the abstract to the concrete to prove the relationship between the energy crisis and achievement of the sustainable development goals (SDG); system analysis to substantiate the need for energy balance and import substitution of energy resources; induction and deduction for the selection of SDGs, which are affected by the energy crisis and energy balance forecasting models, establishing the level of correlation with SDG indicators; idealization and formalization to identify factors inhibiting the formation of a sustainable economy. Findings. It is established that in addition to the global factors of the energy crisis, national economies have their own factors influencing the pace of achieving the SDG. The main reasons for the gap between energy production and consumption and trends in import substitution in the energy sector are indicated. The presence of direct and reverse effects of energy development and sustainable development is proved. It is established that the solution to the problem of sustainable economic development is to systematically ensure the dynamic balance of production and consumption of energy, and import substitution of energy resources. Originality. The reasons for reducing the level of relevance of forecasting and energy plan required to achieve the SDG are identified. There is a significant level of correlation between the Mtoe indicator and groups of SDG indicators and the fact that indicator of relative fluctuations as for average Mtoe is an integral indicator of the SDG achievement. Practical value. The MCDA method and a quantitative approach to its implementation are proposed to forecast energy consumption and to model sustainable development scenarios.

Comparative Analysis of MCDM Methods for the Assessment of Corporate Sustainability Performance in Energy Sector

Energy is an important input for production, growth, and development. A sustainable energy sector, where energy production and consumption balance is ensured, constitutes a key point for nature and humanity. In this study, a multidimensional framework is presented to measure corporate sustainability in the energy sector. Based on this framework, the sustainability performance of energy companies operating in the Asia and Europe regions is measured by hybrid multiple-criteria decision-making (MCDM) methods, taking into account the economic, environmental, and social dimensions of sustainability. The Entropy method is used to determine the weights of the criteria, the Proximity Indexed Value (PIV) - Range of Value (ROV) - Grey relational analysis (GRA) - Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods are used to rank the alternatives. The Copeland method is used to obtain a single rational ranking from different rankings. Based on the obtained results, it is concluded that energy companies in the Asian region are more sustainable than in the European region. Moreover, Thailand is the most sustainable country in the Asian region. The proposed framework can be contribute to the development of the energy sector.

An energy-efficient and self-triggered control method for robot swarm networking systems

Robot swarm networking systems (RSNS) exhibit complex emergent behavior by using local control laws based on spatial information from nearby environment and adjacent robot agents. The consensus behavior of the RSNS depends on a set of parameters of robot agent algorithms or system parameters for their operation, issued mainly by the operator. The challenge in the RSNS is developing techniques for the operator to interact with the RSNS in order to make system behavior adaptive to changes in system configuration and for operator commands without having to handle them individually. Another challenge is saving energy consumption over the robot agents in the RSNS by reducing the number of information exchange between robot agents when the system configuration is spread out the network from the operator. To address these issues, this paper presents an energy-efficient control approach for system configuration propagation with self-triggering control. The proposed method controls the RSNS operation by indirectly propagating the system configuration within the framework of local rules. Moreover, a self-triggered propagation model is designed according to the convergence rate of configuration propagation in order to save and to balance energy consumption among robot agents in the RSNS. This model is then extended to an optimal timing control, where the operator determines its next input time without having to keep track of all the states of robot agents. Theoretical analysis and simulation results are performed to demonstrate the superiority of the proposed method.