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In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

Exploring underwater acoustic sensing networks to detect and monitor ocean characteristics provides a novel approach for marine environment monitoring, disaster warning and resource exploration. However, factors such as underwater acoustic communication, ocean currents and sensor power prevent the direct adoption of terrestrial wireless sensing networks. Moreover, most previous studies have simply increased the number of nodes while ignoring the interference caused by high node density. In this paper, data collection in an offshore tidal scenario is investigated by table forwarding based election (TFBE) scheme. To balance node energy and reduce energy consumption of autonomous underwater vehicles (AUVs), the improved algorithm (TFBE-A) is proposed. Meanwhile, in this scheme, we adopt AUVs to assist in collecting and put forward a 3D precision adjustable trajectory planning (3DPATP) algorithm. To better perform 3DPATP, the maximum minimum error estimation algorithm is presented to simplify the computer operations. Additionally, we study the probability distribution function of nodes in space, providing a foundation for arranging nodes and optimizing topology. Simulation results indicate that our algorithm outperforms compared strategies, particularly in latency and energy consumption. [ABSTRACT FROM AUTHOR]

Multi-Input Multi-Output system (MIMO) is a wireless technology that employs transmitters and receivers for simultaneously transferring more amount of data. And Non-Orthogonal Multiple Access (NOMA) is a new technology that accommodates multiple users in the same spectrum to ensure efficient spectral usage. A combination of MIMO-NOMO systems meets the data demands of more users, while ensuring spectral efficiencies. This paper presents a new precoding algorithm using the Gaussian Mixture Modelling (GMM), which is a type of Machine Learning (ML) algorithm used for Clustering, for the MIMO–NOMA systems. Clustering refers to the grouping of data points into clusters. The use of optimal precoding methods would help eliminate inter–cluster interferences. The suggested precoding approach supports multi-layer transmission in multi-antenna wireless communications, incorporating the idea of GMM in a Multiple antenna system at both the transmitting and receiving ends along with a special case of its multiple access methodology being non-Orthogonal. Hence, the resultant MIMO–NOMA system would result in better spectral efficiency and energy efficiency. The simulation results prove this. [ABSTRACT FROM AUTHOR]

Electric impulse drilling (EID) technology has great industrialization potential since it has unique advantages in crushing hard rocks of deep formation. To study the mechanism of dielectric breakdown is of great significance for accelerating the practical application of EID technology. In this paper, the dielectric breakdown damage model (DBDM) is established based on the heterogeneous granite model. On the basis of DBDM, the whole process of absolute dielectric breakdown (ADB) is studied, including the formation of the plasma channel and the variation of the electric field. Then, the mechanism of regional dielectric breakdown (RDB) development to ADB is studied, and the influence of peak voltage, electrode spacing and the parameters of a high-voltage electric pulse (HVEP) is discussed. Besides, the efficiency of two dielectric breakdown modes is evaluated. It is found that dielectric breakdown will only occur in the domain where the strength of the local electric field formed by HVEP exceeds the critical breakdown field strength of rock. RDB will occur when the pulse duration is too short or the peak voltage is too low. It is worth noting that RDB can be developed into ADB under multi-HVEPs. Moreover, under the same voltage, there is almost no difference between the failure volume caused by RDB under multi-HVEPs and ADB under a single HVEP. However, the energy consumption of the RDB under multi-HVEPs decreases exponentially with increasing voltage and is always higher than that of the ADB under a single HVEP. When designing the supporting tools and bits of EID technology, the life of insulation, the characteristics of formation, and energy utilization should be fully considered. The related research in this paper aims to provide reference and guidance for the application of EID technology in drilling engineering. [ABSTRACT FROM AUTHOR]

Stochastic resonance, as a counterintuitive effect between weak coherent signal and noise in certain circumstance, has attracted wide attention from energy harvesting design. In this paper, our aim is to examine in a systematic way how much the efficiency of energy harvesting can be improved under the help of stochastic resonance effect. To this end, a typical bistable energy harvesting system excited by noise and weak periodical signal is taken into account. The phenomenon of stochastic resonance in the energy harvesting system is then displayed via the moment method of derivative matching closure. Finally, the energy harvesting efficiency in presence of the effect of stochastic resonance is analyzed and compared with the energy harvesting efficiency in absence of this effect. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of many sensor nodes with insufficient energy, storage, and processing capabilities. Data gathering and transmission to the base station are two of the main responsibilities of the sensor nodes (BS). As a result, the network lifespan becomes the key factor in the successful design of data collection strategies in WSN. In this study, we provide the Enriched energy optimized LEACH (EE-OLEACH) protocol for data transfer. Through a combination of efficient optimum clustering and an optimal route selection mechanism, it provides a means for energy-efficient routing in WSN. The Homogeneous Hunter-Wolf optimization (HHWO) is used for clustering, and a cluster head is chosen for each cluster to reduce energy loss among sensor nodes and maximize efficiency in their use of available resources. Nodes with the highest residual energy may receive the most energy-efficient routing. In order to send the data to BS, the nodes with the greatest residual energy are chosen. The pheromone-profound Ant optimization (PPAO) method was then used to reduce energy consumption throughout the path-selection process. It contributes to a higher packet delivery ratio while reducing power consumption. According to the experimental findings, the proposed EE-OLEACH performs better than the current Protocol in terms of packet delivery ratio, end-to-end latency, and energy usage. In this paper, we compare the performance of the existing hierarchical routing protocols under varying conditions (packet size, starting energy level, etc.) and demonstrate how the optimal CH selection based on a suggested algorithm improves both network lifetime and energy consumption. The Simulation results shows that the EE-OLEACH enhances energy efficiency by 30%, delay by 35%, node survived by 45%, network lifetime by 56%, packet delivery ratio by 47% and throughput by 38% when compared with other existing protocols. The results clearly show that the suggested EE-OLEACH extends the lifespan of the network and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

Cognitive Radio Wireless Sensor Networks (CRWSNs) promise optimized spectrum utilization but face challenges in sustaining energy balance, particularly due to the emergence of “hot spots.” In CRWSNs, Cluster Heads (CHs) closer to the sink experience higher traffic as compared to those farther away, primarily due to their role in data collaboration and relaying to the sink. This leads to early depletion of their energy reserves and potentially causing the network to partition creating hot spots or energy holes. Effective clustering algorithms are needed to mitigate these hot spots. The main objective of the paper is to propose a novel clustering scheme titled “Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm” to address hot spot issues in CRWSNs. UCEHA partitions the network into clusters based on sink proximity, selecting CHs considering node energy, communication channels, neighbors, and sink distance. An enhanced spectrum-aware AODV mechanism facilitates efficient data routing. To test and validate the proposed methodology, extensive experimentations were conducted and the results demonstrate UCEHA’s superiority over existing methods, exhibiting reduced energy consumption (average 19%), improved network load balance (average 26%), increased network lifetime (average 40%), and enhanced throughput (average 8%). These results highlight the effectiveness of UCEHA algorithm in addressing energy imbalance and hot spot issues in CRWSNs, ultimately leading to enhanced network performance and longevity. [ABSTRACT FROM AUTHOR]

This paper presents the generalized experience in developing and implementing technology for high specific natural gas consumption in blast furnaces at the Severstal Company. In-depth analysis was performed, problem areas in the technology of replacing coke with natural gas were identified and described, and corrective measures were developed at four process control levels. Only a systematic application of all elements of the developed technology enables us to achieve high economic indicators. Experience in technology development has made possible a specific natural gas consumption of more than 190 m3/t of cast iron and a solid fuel consumption of less than 350 kg/t of cast iron. The most efficient technology development was achieved at blast furnace No. 3, with natural gas consumption of over 210 m3/t of cast iron and solid fuel consumption of less than 325 kg/t of cast iron. [ABSTRACT FROM AUTHOR]

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential due to the restricted availability of resources and virtual machines (VMs). IaaS is one of the main CC models since it controls the backend, which includes VMs and data centers. Cloud service providers can ensure satisfactory service delivery performance in these models by preventing situations of host underutilization or overloading. This is because both results increase network execution time and lead to VM failure. To solve these problems, an improved load balancing approaches was proposed in this work. Therefore, this paper suggested an enhanced load balancing approaches to address these issues. The Artificial Bee Colony (ABC) method and the Bat algorithm are combined to create the balancing technique known as the Hybrid BAT and ABC (HBABC) algorithm, which dynamically distributes resources. The suggested HBABC method was assessed using CloudSim and standard workload format (SWF) data sets, which had file sizes of 200 KB and 400 KB. The evaluation was conducted on even workloads ranging from 200 to 20,000, and the performance of the HBABC method was compared with other state-of-the-art algorithms. The implementation of the suggested HBABC method resulted in a reduction of the Makespan (energy level) within the data center and showed improved accuracy in task allocation for VMs in a cloud data center. The ANOVA comparison test revealed a 1.98 percent enhancement in VM accuracy and task distribution, as well as a 0.98 percent decrease in the Makespan or energy level of the cloud data center. The outcomes are in line with various services broker rules that are employed during process of simulating the suggested algorithm in a cloud datacenter. The suggested method will be employed in subsequent studies as a prediction strategy for the resource management system in cloud datacenters. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) has a significant importance with respect to resource utilization in cloud data centers. Indeed, the optimized management of machine placement usually results in a significant reduction in energy consumption. VMP is a bin packing problem generalization, which is a well known hard combinatorial optimization problem. Besides being NP-hard, VMP is characterized by conflicting objectives and a noisy search space. Meta-heuristics, such as genetic algorithms, particle swarm optimization (PSO), cuckoo search (CS), tabu search and simulated annealing (SA) have been shown to be effective for this category of problems. This paper reports a performance comparison between SA, CS and PSO meta-heuristics to solve the VMP problem. In contrast to reported research work in this area, we study the performance behavior of these three meta-heuristics with respect to, not only the quality of solutions, but also the quality of the explored solution sub-space, in addition to the convergence speed towards reported solutions and the speed with which each meta-heuristic evolves towards the best reported optimized solution. Extensive simulations on randomly generated tests with sizes varying between 200 and 1000 virtual machine demands show that PSO achieves the best performance behavior with respect to all criteria. Moreover, for all tests, PSO produces a reduction of as much as 17% of the number of physical machines, 15% of the energy cost and 21% of the resource utilization of physical machines. [ABSTRACT FROM AUTHOR]

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]