Network Energy Research Articles

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network

Introduction: This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path. Method: The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACODijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB. Results: At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED. Conclusion: In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).

Trade-Offs Between Energy and Depth of Neural Networks.

We present an investigation on threshold circuits and other discretized neural networks in terms of the following four computational resources-size (the number of gates), depth (the number of layers), weight (weight resolution), and energy-where the energy is a complexity measure inspired by sparse coding and is defined as the maximum number of gates outputting nonzero values, taken over all the input assignments. As our main result, we prove that if a threshold circuit C of size s, depth d, energy e, and weight w computes a Boolean function f (i.e., a classification task) of n variables, it holds that log( rk (f))≤ed(logs+logw+logn) regardless of the algorithm employed by C to compute f, where rk (f) is a parameter solely determined by a scale of f and defined as the maximum rank of a communication matrix with regard to f taken over all the possible partitions of the n input variables. For example, given a Boolean function CD n(ξ) =⋁i=1n/2ξi∧ξn/2+i, we can prove that n/2≤ed( log s+logw+logn) holds for any circuit C computing CD n. While its left-hand side is linear in n, its right-hand side is bounded by the product of the logarithmic factors of s,w,n and the linear factors of d,e. If we view the logarithmic terms as having a negligible impact on the bound, our result implies a trade-off between depth and energy: n/2 needs to be smaller than the product of e and d. For other neural network models, such as discretized ReLU circuits and discretized sigmoid circuits, we also prove that a similar trade-off holds. Thus, our results indicate that increasing depth linearly enhances the capability of neural networks to acquire sparse representations when there are hardware constraints on the number of neurons and weight resolution.

Optimal operation of multi-carrier energy systems considering demand response: A hybrid scenario-based/IGDT uncertainty method

The optimal operation of multi-carrier energy systems (MCESs) has opened new horizons for energy network management and the satisfaction of consumers. In this paper, the optimization of the MCES's operation cost is considered by combining several energy hubs (EHs). To make optimal use of thermal and electrical demand response programs (TDRPs and EDRPs), the cost factors are extracted using a new sensitivity analysis (SA) method. Then, taking into account the optimized cost coefficients in EDRP and TDRP, the optimal operation of MCES is investigated in the presence of uncertain parameters. In this paper, a two-stage uncertainty method is used for solving the uncertain problem. First, the uncertainty of wind, photovoltaic (PV), and electrical and thermal loads is modeled with a scenario-based method, in the second step, the price uncertainty of generated electricity is added to the previous model using the information gap decision theory (IGDT). A hybrid scenario-based/IGDT uncertainty has been performed simultaneously at the GAMS platform. The results show the impact of each uncertainty parameter and system resilience due to the optimization done in the problem and the EDRP and TDRP programs. The results show about 135$ difference between the lowest and highest scenarios at a total operation cost.

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

Coordinated energy storage and network expansion planning considering the trustworthiness of demand-side response

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems.

NEMa: A Novel Energy-Efficient Mobility Management Protocol for 5G/6G-Enabled Sustainable Vehicular Networks

As the advancement of 5G and the emergence of 6G technologies unfolds, it is anticipated to support a diverse set of technologies, services, energy resources, and storage capabilities, all while maintaining a high quality of service to users and balancing the load on network devices; however, challenges such as energy consumption, network densification, and rapid mobility of vehicles need to be addressed A critical aspect of this evolution is the mitigation of mobility management, communication, and tracking of vehicles’ rapid mobility in network-dense environments, which currently consumes significant power resources. Consequently, existing mobility management protocols must be adapted and reconfigured to meet the requirements and limitations of energy-constrained vehicular networks. In this article, we introduce a novel energy-efficient mobility management protocol, NEMa, specifically designed for vehicular networks. This protocol optimizes network resources and vehicles’ on-board sensing to minimize energy consumption while maintaining a high packet delivery ratio and minimal interruption time. We have evaluated the performance of the proposed protocol and compared it to existing benchmark mobility management solutions in terms of network overhead, latency, and energy efficiency. Our results demonstrate that our proposed protocol surpasses existing benchmark solutions, thereby contributing to the enhancement of 5G/6G and beyond vehicular networks by addressing the challenges of energy efficiency and network performance.

The 5G Era: A Comprehensive Review of Recent Advancements and Applications

The fifth generation is a promising and transformative leap in network capabilities, bringing out applications beyond traditional technologies. This review paper discusses the novel technologies shaping 5G, studying their impact and potential. We explore technological advancements like Network Function Virtualization (NFV) and Software- Defined Networking (SDN) for their role in creating flexible and efficient networks. Services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC) are also examined, along with their details, and role in shaping the benefits of 5G for various fields. This paper also investigates how 5G addresses the aspects like energy efficiency enhancements and network security enhancements brought about in 5G. We discuss the details of integration of Artificial Intelligence (AI) for network optimization, security, automation and user experience, along with its impact in various domains like healthcare, manufacturing, education and transportation. Further, the paper explores the incorporation of edge computing and its role in supporting real-time applications. Finally, we examine the growing importance of Non-Public Networks (NPNs) tailored for private use cases, another aspect that adds to the benefits and novelty of the fifth generation. This review provides a comprehensive overview of the key technological advancements driving the 5G revolution, highlighting their role in transforming communication infrastructures and other key use cases, supporting a new era of communication.

Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

A energy efficiency optimization routing processing method for Linear Wireless Sensor Networks

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

Neural Network Energy Management-Based Nonlinear Control of a DC Micro-Grid with Integrating Renewable Energies

The broad acceptance of sustainable and renewable energy sources as a means of integrating them into electrical power networks is essential to promote sustainable development. Microgrids using direct currents (DCs) are becoming more and more popular because of their great energy efficiency and straightforward design. In this work, we discuss the control of a PV-based renewable energy system and a battery- and supercapacitor-based energy storage system in a DC microgrid. We describe a hierarchical control approach based on sliding-mode controllers and the Lyapunov stability theory. To balance the load and generation, a fuzzy logic-based energy management system has been created. Using a neural network, maximum power defects for the PV system were determined. The global asymptotic stability of the framework has been verified using Lyapunov stability analysis. In order to simulate the proposed DC microgrid and controllers, MATLAB/SimulinkR (2019a) was utilized. The outcomes show that the system operates effectively with changing production and consumption.

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

Optimizing Grid-Connected Inverter Performance Through Voltage Stability Control

Everyday, non-renewable energy sources are being depleted while energy use rises. In response to these arguments, we ought to generate more renewable energy. But a big problem with renewable energy is that its production is weather-dependent. In overcast and unfavorable weather, the energy generated on the grid side is insufficient to match the demand on the load side. The primary load-side issues may be non-linear loads that lower the energy network’s power quality or excessive or unmanaged energy demand. When compared to the production of renewable energy, energy sources that are connected to the grid through an electronic converter or inverter have quite different operating characteristics. The stability of the grid and solutions to the grid-to- transmission-line stability issue are the main topics of this study. The benefits of using inverters in a power system from the grid to the transmission line with MPPT and PI controllers are examined in this article. A robust control system with an inverter-equipped PI controller and MPPT addresses the main problem.

Empirical design and comprehensive analysis of multiport converter in distributed generation enabled microgrids

Distributed generation-enabled microgrids are a revolutionary approach for the energy paradigms that will transform the ways of generation, distribution, and utilization. A decentralized approach to energy generation in microgrids results in the robustness and independence of energy networks. Empowerment of the communities to take control of energy requirements with multiport converter-based microgrids promotes sustainability, efficiency, reduced losses, and increased resilience against the evolving trends of the energy sector. In this research, a hybrid energy-integrated multiport converter is designed and analyzed under its various modes of operation. Rapid transitions from one energy source to another are attained within 0.4μs to feed the load at a steady state voltage of 400 V, and within 0.15μs to charge the energy storage system at a steady state voltage of 48 V which indicates the robustness and independence of energy systems via this proposed approach. The resilience of the microgrid is obtained because of this rapid trans Moreover, the proposed converter offers a voltage gain of 8.3 with 96 % efficiency making it sustainable for maximization of renewable energy. In addition, the resilience of the proposed converter is exhibited through high power densities at power losses of not more than 7.5 % making it an innovative candidate for hybrid energy systems as compared to existing technologies. Simulation scenarios are validated through mathematical analysis, proving their effectiveness in distributed generation-enabled microgrids. The proposed research model is effective in a real environment because of the minimal effects of parasitic resistance, reduced total harmonic distortion under different loads, and negligible electromagnetic interferences, demonstrated through simulations.

ReLeC-based clustering and multi-objective optimization for efficient energy optimization in IoT networks

In response to the escalating demand for energy-efficient wireless sensor networks (WSNs) within the expanding Internet of Things (IoT) landscape, we introduce ReLeC-MO, a novel protocol that integrates the ReLeC clustering algorithm with multi-objective optimization. Leveraging reinforcement learning-based clustering, ReLeC optimizes network topology to enhance energy efficiency. Multi-objective optimization further refines this process by identifying non-dominated solutions on the Pareto front, facilitating a balanced trade-off between network lifetime, energy consumption, and data transmission quality. Our comprehensive simulations reveal the remarkable performance improvements achieved by ReLeC-MO over existing techniques. Specifically, ReLeC-MO demonstrates a 39% reduction in delay, a 50% decrease in energy consumption, and a 25% increase in throughput, showcasing its efficacy in enhancing both energy efficiency and network performance. It also increases network lifetime by 20%, surpassing the latest existing model. Furthermore, its implementation in MATLAB ensures ease of replication and adaptation across diverse IoT applications.

Towards Mass-Scale IoT with Energy-Autonomous LoRaWAN Sensor Nodes.

By 2030, it is expected that a trillion things will be connected. In such a scenario, the power required for the trillion nodes would necessitate using trillions of batteries, resulting in maintenance challenges and significant management costs. The objective of this research is to contribute to sustainable wireless sensor nodes through the introduction of an energy-autonomous wireless sensor node (EAWSN) designed to be an energy-autonomous, self-sufficient, and maintenance-free device, to be suitable for long-term mass-scale internet of things (IoT) applications in remote and inaccessible environments. The EAWSN utilizes Low-Power Wide Area Networks (LPWANs) via LoRaWAN connectivity, and it is powered by a commercial photovoltaic cell, which can also harvest ambient light in an indoor environment. Storage components include a capacitor of 2 mF, which allows EAWSN to successfully transmit 30-byte data packets up to 560 m, thanks to opportunistic LoRaWAN data rate selection that enables a significant trade-off between energy consumption and network coverage. The reliability of the designed platform is demonstrated through validation in an urban environment, showing exceptional performance over remarkable distances.

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Integrating renewable wind power (WP) into the grid exacerbates variability and challenges reliability. Establishing an effective forecasting system is crucial for risk avoidance, but achieving effective, interpretable, and accurate predictions remains an obstacle due to the high volatility and uncertainty of WP. This study proposes an innovative and trustworthy wind power forecasting system combined with point and interval predictions. Specifically, the adaptive multi-scale convolutional receptive field (AMSCF) combined with gated recurrent unit (GRU) for WP point prediction is proposed to capture the spatiotemporal dependencies of WP generation. This AMSCF method uses different size receptive fields, fuses them using channel dimension concatenation, operates pooling layer and adaptive fusion through field attention, which extracts hierarchical features for WP forecasting. Then conditional mixture Copula (Con-mCopula) function integrated with multiobjective optimizer is established for WP interval prediction, which reduces the WP interval prediction accuracy error as some information neglected. The wind power cluster dataset from Germany was selected for experiments. Based on the results, for both datasets, the designed prediction system can achieve better point and interval prediction performance resulting in a reduction of mean absolute error (MAE) by approximately 40 % compared to other existing components in the market. This study deconstructs the “Black Box” in deep network for energy forecasting through interpretability analysis, which accelerates the development of AI technology in WP forecasting system and provides decision-makers with trustworthy WP forecasting assistance.

Expediting battery investment returns for residential customers utilising spot price-aware local energy exchanges

This paper presents a local energy exchange mechanism to benefit residential customers and retailers under variable wholesale market spot prices. To do so, a peer-to-peer (P2P) trading-enabled local energy market (LEM) is proposed to facilitate frequent energy transactions among participants. The proposed LEM mechanism considers market operational constraints and maximises electricity cost reductions. Further, the proposed LEM optimises the customers' investment return for battery energy storage systems (BESSs) and minimises retailers’ financial losses during higher spot prices in the wholesale market. Finally, a case study is demonstrated using real-world Australian data in which P2P trading is performed between interested buyers and sellers. Two scenarios with high and low wholesale market spot prices are analysed comprehensively considering five metrics such as solar-to-spot (STS); solar-to-load (STL); forward buy contract (FBC)-to-load (FBC-L); spot-to-load (Short); and FBC-to-spot (Long). The simulation results reveal that the customers reap substantial benefits from the proposed LEM in contrast with business-as-usual (BAU), allowing them to expedite BESS investment returns. Also, the proposed LEM helps a retailer decrease the effect of higher spot prices by reducing its unexpected financial losses while improving self-consumption and self-sufficiency in the local energy network.

The Transition To Artificial Intelligence -Based Solutions For Improving Energy Efficiency In Urban Environments

The urban landscape is rapidly evolving towards sustainable, energy-efficient practices, thanks in large part to theintegration of artificial intelligence (AI) solutions. This study explores the dynamic transition to AI-basedtechnologies for improving energy efficiency in urban environments. AI is emerging as a powerful tool foroptimizing energy consumption, streamlining infrastructure operations and fostering smart, resilient urbanecosystems. The study examines various applications of AI, highlighting the essential role advanced technologiesplay in revolutionizing conventional approaches to urban energy management, and illustrating how thesetechnologies can contribute to the creation of self-sufficient urban spaces. It examines the implementation of AIbased energy networks, smart energy storage solutions and the integration of renewable energy into urbanplanning, and also looks at the potential challenges and ethical considerations associated with its adoption. Itaddresses issues such as data privacy, algorithmic biases and the need for transparent decision-making processes.This research is a literature review that aims to contribute to the current discourse on creating smart, energyefficient cities.