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In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Due to its large leakage power and low density, the conventional SARM becomes less appealing to implement the large on-chip cache due to energy issue. Emerging non-volatile memory technologies, such as phase change memory (PCM) and spin-transfer torque RAM (STT-RAM), have advantages of low leakage power and high density, which makes them good candidates for on-chip cache. In particular, STT-RAM has longer endurance and shorter access latency over PCM. There are two kinds of STT-RAM so far: single-level cell (SLC) STT-RAM and multi-level cell (MLC) STT-RAM. Compared to the SLC STT-RAM, the MLC STT-RAM has higher density and lower leakage power, which makes it a even more promising candidate for future on-chip cache. However, MLC STT-RAM improves density at the cost of almost doubled write latency and energy compared to the SLC STT-RAM. These drawbacks degrade the system performance and diminish the energy benefits. To alleviate these problems, we propose a novel cache organization, companion write cache (CWC), which is a small fully associative SRAM cache, working with the main MLC STT-RAM cache in a master-and-servant way. The key function of CWC is to absorb the energy-consuming write updates from the MLC STT-RAM cache. The experimental results are promising that CWC can greatly reduce the write energy and dynamic energy, improve the performance and endurance of MLC STT-RAM cache compared to a baseline.

Moore’s law has been one of the reason behind the evolution of multicore architectures. Modern multicore architectures offer great amount of parallelism and on-chip resources that remain underutilized. This is partly due to inefficient resource allocation by operating system or application being executed. Consequently the poor resource utilization results in greater energy consumption and less throughput. This paper presents a fuzzy logic-based design space exploration (DSE) approach to reconfigure a multicore architecture according to workload requirements. The target design space is explored for L1 and L2 cache size and associativity, operating frequency, and number of cores, while the impact of various configurations of these parameters is analyzed on throughput, miss ratios for L1 and L2 cache and energy consumption. MARSSx86, a cycle accurate simulator, running various SPALSH-2 benchmark applications has been used to evaluate the architecture. The proposed fuzzy logic-based DSE approach resulted in reduction in energy consumption along with an overall improved throughput of the system.

This paper presents a method to characterize, identify and classify some pathological Electroencephalogram (EEG) signals. We use some Time Frequency Distributions (TFDs) to analyze its nonstationarity. The analysis is conducted by the spectrogram (SP), the Choi–Williams Distribution (CWD) and the Smoothed Pseudo Wigner Ville Distribution (SPWVD). The studies are carried on some real EEG signals collected from a known database. The estimation of the best value of parameters for each distribution is achieved using the Rényi entropy (RE). The time-frequency results have permitted to characterize some pathological EEG signals. In addition, the Rényi Marginal Entropy (RME) is used for the purpose of detecting the peak seizures and discriminates between normal and pathological EEG signals. The frequency bands are evaluated using the Marginal Frequency (MF). The EEG signal classification of two sets A and E containing normal and pathologic EEG signals, respectively, is performed using our proposed method based on energy extraction of signals from time-frequency plane. Also, the Moving Average (MA) is used as a tool to obtain better classification results. The results conducted on real-life EEG signals illustrate the effectiveness of the proposed method.

The rebel of global networked resource is Cloud computing and it shared the data to the users easily. With the widespread availability of network technologies, the user requests increase day by day. Nowadays, the foremost complication in Cloud technology is task scheduling. The cargo position and arrangement of the tasks are the two important parameters in the Cloud domain, which can provide the Quality of Service (QoS). In this paper, we formulated the optimal minimization of makespan and energy consumption in task scheduling using Local Pollination-based Gray Wolf Optimizer (LPGWO) algorithm. In the hybrid concept, Gray Wolf Optimizer (GWO) algorithm and Flower Pollination Algorithm (FPA) are combined and used. In the presence of GWO, the best searching factor is used to increase the convergence speed and FPA is used to distribute the data to the next packet of candidate solution using local pollination concept. Chaotic mapping and OBL are used to provide a suitable initial candidate for task solutions. Therefore, the experiments delivered better task scheduling results in low and high heterogeneities of physical machines. Ultimately, the comparison with the simulation results had shown the minimum convergence speed of makespan and energy consumption.

In this paper, a monitoring technique based on the wireless sensor network is investigated. The sensor nodes used for monitoring are developed in a simulation environment. Accordingly, the structure and workflow of wireless sensor network nodes are designed. Time-division multiple access (TDMA) protocol has been chosen as the medium access technique to ensure that the designed technique operates in an energy-efficient manner and packet collisions are not experienced. Fading channels, i.e., no interference, Ricean and Rayleigh, are taken into consideration. Energy consumption is decreased with the help of ad-hoc communication of sensor nodes. Throughput performance for different wireless fading channels and energy consumption are evaluated. The simulation results show that the sensor network can quickly collect medium information and transmit data to the processing center in real time. Besides, the proposed technique suggests the usefulness of wireless sensor networks in the terrestrial areas.

Wireless Sensor Networks (WSNs) have come across several things which include collecting data, handling data and distribution for super visioning specific applications such as the services needed, managing anything that occurred naturally, etc. They totally rely on applications. Therefore, the WSNs are classified under major networks. This is very essential. It can be defined as a network of networks that helps in proper flow of data. The main characteristics of WSN include its continuous changes in topologies, connected nodes with several chips and tunic routing protocol. There should be the better utilization of the available resources so that its life span may exceed. There should be an effective usage of available assets to avoid the waste. In our research, we propose a hybrid approach, namely, the Power Control Tree-Based Cluster (PCTBC) to identify the Sybil attacks in WSNs. It employs several stages structured clustering of nodes based on position and identity verification. This approach is utilized for the usage of energy consumption, effectiveness of detecting Sybil attacks inside clusters. The main aspects put into consideration are the efficient routing protocol of the distance between the hops and the energy power remains, where the distance between the hops is being computed using the Received Signal Strength Indication (RSSI) and also the packet transmission can be properly tuned on the basis of the distance. Also, the proposed approach considers the energy consumption for the transmission of the defined packet.

The emergence of cloud computing in big data era has exerted a substantial impact on our daily lives. The conventional reliability-aware workflow scheduling (RWS) is capable of improving or maintaining system reliability by fault tolerance techniques such as replication and checkpointing based recovery. However, the fault tolerant techniques used in RWS would inevitably result in higher system energy consumption, longer execution time, and worse thermal profiles that would in turn lead to a decreased hardware lifespan. To mitigate the lifetime-energy-makespan issues of RWS in cloud computing systems for big data, we propose a novel methodology that decomposes the complicated studied problem. In this methodology, we provide three procedures to solve the energy consumption, execution makespan, and hardware lifespan issues in cloud systems executing real-time workflow applications. We implement numerous simulation experiments to validate the proposed methodology for RWS. Simulation results clearly show that the proposed RWS strategies outperform comparative approaches in reducing energy consumption, shortening execution makespan, and prolonging system lifespan while maintaining high reliability. The improvements on energy saving, reduction on makespan, and increase in lifespan can be up to 23.8%, 18.6%, and 69.2%, respectively. Results also show the potentiality of the proposed method to develop a distributed analysis system for big data that serves satellite signal processing, earthquake early warning, and so on.

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption.

The routing information is hard to maintain and the energy is limited in highly dynamic wireless sensor network. To solve these problems, energy-saving geographic routing (ESGR) is proposed, which does not maintain the network topology and can save energy. A node broadcast its position information to its neighboring nodes before transmitting data. The neighboring nodes compute the position of the virtual relay node using the data transmitter position, the base station position and the energy consumption for circuits and propagation. The neighboring nodes determine whether to become the relay node through competition based on its position, the destination position and the virtual relay node position. The neighboring nodes compute the delay time distributedly according to the competition strategy. The neighboring node with the shortest delay time can respond to the data sender first and become the sole relay node. The handshake mechanism efficiently prevents the collision among the neighboring nodes during competition, which is of high communication efficiency. When a routing hole is found, the relay region is changed and an approaching destination relay strategy is adopted, which reduces the impact of routing holes. The simulation shows that the proposed algorithm is better than BLR, because of the lower energy consumption and lower packet loss ratio. The ESGR algorithm is more appropriate for highly dynamic wireless network.

Energy consumption is important to consume less power, reducing toxic fumes released by plants, preserving natural resources, and protecting ecosystems against damage. The challenging characteristics in energy supply include lack of renewable energy adoption, and policy and energy management are 0considered essential factors. An artificial intelligent building with a multi-energy planning method (AIBMEM) has been proposed to design multi-energy systems to achieve the best policy and energy management techniques. The intelligent construction problem with multi-energy is framed as a predictive energy model to minimize the overall utilization of energy levels. The normal distribution with the artificial intelligent model is introduced to solve the problem of renewable energy. The experimental results based on reliability, effectiveness, preservation, energy consumption, and control systems show that the suggested model is better than existing models, producing good performance analysis results.

Intelligent transportation systems (ITS) are a collection of technologies that can enhance transport networks and public transit and individual decision-making about various elements of travel. ITS technologies comprise cutting-edge wireless, electronic and automated technology intending to improve safety, efficiency and convenience in surface transit. In certain cases, reducing energy usage has proven to be an ITS advantage. In this report, the primary energy advantages of a range of ITS systems established through models, pilot projects/field tests and extensive use are examined and summarized. In worldwide driving, the Internet of Things (IoT) solutions play a vital role. A new age of communication leading to ITS will be the communication between cars via IoT. IoT is a mixture of data and data analysis data storage and processing to manage the traffic system efficiently. Energy management, which is seen as an efficient, innovative approach to highly efficient energy generation plants. It simultaneously takes care of optimizing traditional sources of the IoT based intelligent transport system, helps to automate railways, roads, airways and shipways, which improve customer experience in the process. Following an evaluation of the situation, a proposal named energy management in intelligent transportation (EMIT) improves energy efficiency and economic efficiency in transportation. It improves energy management to reduce economic and ecological waste by decreasing global transport energy consumption. The sustainable development ratio is 85.7%, accidents detection ratio is 85.3%, electric vehicle infrastructure ratio is 83.6%, intelligent vehicle parking system acceptance ratio is 82.15%, and reduction ratio of energy consumption is 91.4%.

One of the significant approaches in implementing the routing of WSNs is clustering that leads to scalability and extending of network lifetime. In the clustered WSN, cluster heads (CHs) utilize maximum energy to another node. Moreover, it balanced the load present in the sensor nodes (SNs) between the CHS for enhancing the network lifespan. Moreover, the CH plays an important part in efficient routing, as well as it must be selected in an optimal way. Thus, this work intends to introduce a cluster-based routing approach in WSN, where it selects the CHs by the optimization algorithm. A new hybrid seagull rock swarm with opposition-based learning (HSROBL) is introduced for this purpose, which is the hybridized concept of rock hyraxes swarm optimization (RHSO) and seagull optimization algorithm (SOA). Further, the optimal CH selection is based on various parameters including distance, security, delay, and energy. At the end, the outcomes of the presented approach are analyzed to extant algorithms based on delay, alive nodes, average throughput, and residual energy, respectively. Based on throughput, alive node, residual energy, as well as delay, the overall improvement in performance is about 28.50%.

There are many interests in developing life-like robots, or robots which are both intelligent and autonomous. And, one obvious characteristics of life-like creatures is that they can autonomously develop and learn during their life span. Such abilities obviously depend on the ways of designing human-like minds. Then, a fundamental question is how to devise the innate, or built-in, principles behind the blueprint of a human-like mind, and to apply these findings to guide the design of the mind of life-like robots. In the literature, there are two schools of thoughts. One advocates the study of the nervous systems of biological brains (e.g. human brain) until the discovery of the blueprint of a mind. The second approach is to follow the path of invention and validation until the full understanding of physical principles which enable the design of an artificial mind that is as good as a biological mind. This paper embraces the second approach, and aims at formulating a new ground which could guide the design of the minds of life-like robots at various stages. In particular, the discussion is focused on answering the question of what life is from an engineering point of view. And, we approach the answer by examining the key steps of evolution from non-life to life. In this paper, five key steps of evolution from non-life to life will be discussed in detail. They are embodiment of energy flow, embodiment of signal flow, embodiment of knowledge flow, embodiment of decision flow, and embodiment of awareness flow. These findings are grounded on our engineering works toward the development of low-cost humanoid (LOCH) robot, and offer a unique perspective and an engineering basis. Whenever possible, the discussions in this paper are supported by real results of experiments on real robots.

This paper proposes an energy control method for dynamic obstacle crossing by a planar biped. This approach was tested in a simulation where it was found to enable the biped robot to cross obstacles of different heights, due to inertial forces, by leaning with the front foot on the obstacles. The propulsion energy of the system is produced by the rear leg, which is endowed with four actuated degrees-of-freedom (hip, knee, ankle, toes), and is controlled by force control with four degrees-of-freedom in the non-singular case, and three degrees-of-freedom in the singular case. This paper identifies ten geometric, energetic and servo-control parameters necessary for dynamic obstacle crossing. The methodology presented allowed the dynamic crossing of an obstacle up to 20 cm high, at which point the joint torque limit for the propelling ankle was reached.

Noise plays a major role in the behavior of various physical and biological systems, its effects being increasingly pronounced with decrease in system size. While it is jeopardizing the future development of several nanotechnologies, such as magnetic data storage, noise can also play a constructive role in many nonlinear systems, activating a resonance response. In this paper, it is proven that various hysteretic systems can exhibit such coherent behavior — a phenomenon that is generally known as coherence resonance when is solely induced by noise, and stochastic resonance when an external oscillatory signal is present. The quantity used to characterize the regularity of the stochastic output is the power spectrum, which displays a maximum at the resonance frequency. The calculation of the spectral densities for the outputs of hysteretic systems is performed in the framework of stochastic processes defined on graphs. The case of hysteretic systems described by rectangular loops is discussed and analytical expressions for the output power spectra are derived. These theoretical results suggest that hysteretic systems can be used by nanotechnology for concentrating the energy of a flat, noisy input into a short bandwidth frequency region.

In this paper, a new method for global interconnects optimization in nanoscale VLSI circuits using unequal repeater (buffer) partitioning technique is presented. The optimization is performed with the energy-delay product minimization at 65, 90, and 130 nm technology nodes and various loads, using the genetic algorithm (GA) of MATLAB. The results show more improvements of the total propagation delay with respect to the traditional equal buffer partitioning technique. This improvement is obvious for 90 and 130 nm, and with increasing capacitive load, the improvement will be achieved for 65 nm.

Regardless of the state of matter, such as solids, liquids, and gases, the smaller the matter size from bulk to nano-scale, especially in the quantum region, the more rapid is the energy increase. To this end, this study introduces the concept of a group system, in which atoms behave as one, and this system is reinterpreted as that comprising temperature–entropy (TS) energy in thermodynamic data. Based on this concept, water was passed through various mesh-like dissolved tubes, where the size and energy of the water group system were observed to change. Thereafter, as the scale and number of the meshes increased, the ozone, chlorine, and oxygen constituents, which are closely related to sterilization and washing, are generated, changing the basic water composition. Thus, this nano-size impact is not limited to solids and could facilitate in revolutionizing the future applications in fluids.

This chapter carries out an investigation on the consumption of energy and emission of CO₂ in 16 Students’ halls of residence (hostels) in Lagos, Nigeria, to gain an understanding on the status, impacts and performance of energy use in public student residences. The use of energy in Nigeria was highlighted and the continuously evolving built environment was shown to have an impact on the electricity infrastructure of the nation. Residential buildings were shown to significantly impact energy supply around the world. Methods of benchmarking were applied for the identification of variables capable of impacting on the consumption of energy in the buildings, these methods also identified the variables that significantly correlated with the energy consumption. The energy usage intensity (EUI) was characterized and with EUI acting as an indicator for building energy performance (BEP), the derived annual benchmark range of EUI was derived and given as 93.61–147.1kWh/m². The CO₂ emission levels were calculated using an emission factor and correlation analysis was carried out to show that floor area, number of occupants and number of rooms all had significant correlation with the CO₂ emissions.

Senegal is located in West Africa with a population close to 16 million inhabitants unequally distributed on a land of 196,722 km² area. In the 2000s, a national energy information system (known as SIE-Sénégal) aiming at monitoring and forecasting the energy demand and the efficient planning of the energy infrastructure was put in place in the Ministry of Oil and Energies. A lot of data were recorded of which some are analyzed and presented here for a better understanding of the energy system of Senegal. In the period 2000–2013, the energy demand has been increasing reaching 3.72 Mtoe in 2013. The demand is covered by imported fossil fuels and traditional biomass. The energy consumption has been increasing in the same period from 1.69 Mtoe in 2000 up to 2.56 Mtoe in 2013. The energy pattern shows a lion’s share for the residential sector followed by the transport and industrial sectors. In the residential sector, firewood is the main fuel, and electricity is deemed marginal. The transport sector is dominated by the road subsector where diesel oil represents 81% of the energy use. In the industrial sector, more than 80% of energy used is from fossil origin and the share of coal is becoming significant.