Abstract Wireless sensor networks (WSNs) play a critical role in applications such as wildlife monitoring, disaster recovery, and precision agriculture, where continuous coverage and longevity are paramount amidst dynamic environmental challenges. To address these demands, the cellular adaptive energy forecasting and coverage optimization (CAEFCO) framework integrates localized neuro-symbolic energy forecasting (LNS-EF), a novel concept that combines symbolic reasoning with neural network learning directly on sensor nodes. LNS-EF enables nodes to not only predict energy depletion based on past consumption patterns and environmental factors but also incorporate rule-based contextual reasoning for enhanced decision-making. Alongside this, CAEFCO employs an anomaly detection module that identifies disruptions, such as sensor damage or environmental interference, allowing real-time task redistribution. This dual approach ensures seamless task reallocation while extending network lifetime. CAEFCO’s proactive methodology demonstrates a 97% reduction in data loss and an 85% improvement in network longevity, offering a breakthrough in the resilience and sustainability of WSNs in mission-critical and harsh environments.
Due to the enormous increase in the power system load the conventional power generation plants never satisfy the power demand. So the power generating sectors turn into renewable energy sources. Wind power is a promising renewable energy source. It is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper GA and PSO algorithm are used to perform ED considering wind power generation and valve effect of thermal unit. The proposed method is validated with three and six unit test system. The results show the performance comparison of the two methods for solving the wind thermal dispatch problem.
This paper presents a new approach ~o solving short-term unit commitment problem using Neural Based Simulated Annealing. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Simulated Annealing is a powerful technique for solving combinatorial optimisation problems. It has the ability of escaping local minima by incorporating a probability function in accepting or rejecting new solutions. The neural network combines good solution quality for Simulated Annealing with rapid convergence for artificial neural network. The neural based Simulated Annealing method is used to find the unit commitment. By doing so, it gives the optimum solution rapidly and efficiently. The Neyveli Thermal Power Station (NTPS) Unit - II in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consists of 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the Simulated Annealing method and other conventional methods like Dynamic Programming and Legrangian Relaxation in reaching proper unit commitment.
Abstract Wireless sensor networks (WSNs) play a critical role in applications such as wildlife monitoring, disaster recovery, and precision agriculture, where continuous coverage and longevity are paramount amidst dynamic environmental challenges. To address these demands, the cellular adaptive energy forecasting and coverage optimization (CAEFCO) framework integrates localized neuro-symbolic energy forecasting (LNS-EF), a novel concept that combines symbolic reasoning with neural network learning directly on sensor nodes. LNS-EF enables nodes to not only predict energy depletion based on past consumption patterns and environmental factors but also incorporate rule-based contextual reasoning for enhanced decision-making. Alongside this, CAEFCO employs an anomaly detection module that identifies disruptions, such as sensor damage or environmental interference, allowing real-time task redistribution. This dual approach ensures seamless task reallocation while extending network lifetime. CAEFCO’s proactive methodology demonstrates a 97% reduction in data loss and an 85% improvement in network longevity, offering a breakthrough in the resilience and sustainability of WSNs in mission-critical and harsh environments.
The use of Electric Vehicles (EVs) has been increasing in a wider range owing to the increase in population and energy demand.Nowadays, the Solar, which is one of the RES (Renewable Energy sources),assists in EV charging and is gaining higher importance. Moreover, the main function involved in an EV system is the battery charging, and many different types of issues like battery life time, interruptible power supply and poor energy management occur in conventional types of charging. Hence, in the proposed paper, a novel EV charging station is introduced and it involves an optimized DC-DC Bi-directional Boost-Zeta converter. In this work, the EV battery attains energy directly from the PV panel and the additional energy produced by PV is transferred to the grid. The proposed converter functions in boost mode and aids in improving the PV output; the resulting DC-link voltage is regulated and maintained constant using an optimization algorithm known as Firefly Algorithm (FFA). Further, through a single phase VSI, the DC link voltage is given to the grid. In order to attain grid synchronization, a PI controller is employed. Whenever, there is less sunshine, the energy from the grid is fed to the EV battery, thereby supplies continuous power to the EV, even in the absence of solar energy and during this condition, the DC-DC converter functions in Zeta mode. Then, for analysing the performance of the proposed work, it is implemented in MATLAB/Simulink and from an analysis, it is identified that the proposed EV charging station possess a less THD of 3.1%, better switching losses and reactive power compensation.
The objective of this paper is to build up a LoRa-based smart agricultural management and monitoring system using Wireless Sensor Networks (WSNs) in rural areas, in order to replace the current technology of the agricultural monitoring system. A private network server is created and interfaced with a gateway that collects data or signals from end nodes and transmits the data to the cloud without the use of routers. The data can be used for end user application. The network interface is fulfilled by LoRa by solving communication failure problems and energy saving data transmission. This intelligent agriculture platform improves the efficiency of agricultural techniques.
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