Energy Of Nodes Research Articles

Adaptive Reinforcement learning with Dij-Huff Method to Secure Optimal Route in Smart Healthcare System

The Wireless Sensor Network (WSN) is a multi-hop wireless network that contains multiple sensor nodes agreed in a self-organized mode. The significant advancement in the healthcare, the security of the medical data became huge disputes for healthcare services. Intrusion Detection System increasingly demands automatic and intelligent intrusion detection approaches to handle threats caused by a growing number of attackers in the WSN environment. Reinforcement Learning is a fundamental approach to improving routing efficiency. This approach introduces Adaptive Reinforcement learning with Dij-Huff Method (ARDM) for secure optimal route in WSN. Adaptive Reinforcement Learning (ARL) is a machine learning technique that selects the best forwarder node. The node energy, node Received Signal Strength, and node delay parameters determine the forwarder nodes in the WSN. Furthermore, the Dij-Huff Procedure (DHP) is a mixture of Dijkstra’s algorithm and Huffman coding. Dijkstra’s algorithm is applied to discover the sensor nodes with the highest energy and the best possible distance route. Huffman coding computes the binary hop count to offer each hop security. The simulation results and their comparison with conventional protocol demonstrate that the proposed scheme has a better detection ratio, a better throughput, and increased energy efficiency.

Artificial Intelligence Machine Learning in Healthcare System for improving Quality of Service

A vital element of widespread patient monitoring is consistency in transmission between the patients and the healthcare professionals not including time and position dependencies. Artificial intelligence (AI) and machine learning (ML) techniques have a vast possibility to proficiently handle the automated function of the mobile nodes distributed in the Mobile ad-hoc network (MANET). ML is a part of AI in that the computer algorithms learn themselves by improving from historical experiences. The main issues in MANET are autonomous operation, maximization of a lifetime, coverage of the network, energy utilization, connectivity issues, quality of service, high bandwidth necessity, communication protocol design, etc. ML is valuable for data aggregation, and it saves the energy of mobile nodes and enhances the network lifetime. In this paper, we propose an Artificial Intelligence Machine Learning Algorithm for improving the Quality of Service in MANET (AIMQ). ML techniques based on artificial neural networks (ANN) algorithm is helpful for data aggregation tasks. This approach formed the clusters by node mobility and connectivity. Glowworm Swarm Optimization (GSO) algorithm is applied in every cluster to choose a proficient Cluster Head (CH). Here, we choose the CH by GSO fitness function based on mobile node degree, node distance, node reliability, and energy. ANN algorithm recognizes and chooses the data aggregator with great energy and more extended stability. It updates the weight of input parameters such as node energy, node degree, packet loss ratio, and node delay to reduce the errors. It minimizes the repetitive CHs selection and member nodes’ re-affiliation in a cluster.

Efficient and Reliable Multihop Routing for IoT Healthcare Applications

A wireless body area network (WBAN) collects several tiny sensor nodes in the human body. They are intended to observe data constantly forward and perform as critical structures for remote healthcare monitoring and healing. These protect the preferred reliability and efficiency of data transmissions of individual body sensors. However, with the restricted sensor energy, unreliable link connection, and complex channel environment, the design of efficient routing in Multihop WBANs is demanding. This article, Efficient and Reliable Multihop Routing (ERMR) for IoT Healthcare Applications, is proposed. The main concept is to incorporate a fuzzy-logic system to handle multiple cross-layer input variables independently. This approach uses the fuzzy logic method to select the optimal route in the WBAN. The fuzzy input like sensor node distance, link strength, energy, and node quality of Service and produce the output is a reliable forwarder selection. Simulation results demonstrate the proposed approach increases the network throughput and minimizes the network delay in the WBAN.

Reinforcement learning based flow and energy management in resource-constrained wireless networks

This paper aims at developing a learning-based framework for MAC sleep–listen–transmit scheduling in wireless networks. The Reinforcement Learning-based paradigm is shown to work in the absence of network time-synchronization and other complex hardware features, such as carrier-sensing, thus making it suitable for low-cost transceivers for IoT and wireless sensor nodes. The framework allows wireless nodes to learn policies that can support throughput-sustainable flows while minimizing node energy expenditure and sleep-induced packet drops and delays. Each node independently learns a scheduling policy without explicit communication with other network nodes. The trade-off between packet drops and energy efficiency is analyzed, and an application-specific solution is proposed for handling the trade-off. It is shown how this model allows users to prioritize between energy efficiency and packet drops depending on specific application requirements. An analytical model is developed for understanding the underlying system dynamics, which is also validated using extensive simulation experiments. Moreover, the developed mechanism is experimented with for heterogeneous network topologies and traffic patterns

Energy Autonomous Wireless Sensing Node Working at 5 Lux from a 4 cm2 Solar Cell

Harvesting energy for IoT nodes in places that are permanently poorly lit is important, as many such places exist in buildings and other locations. The need for energy-autonomous devices working in such environments has so far received little attention. This work reports the design and test results of an energy-autonomous sensor node powered solely by solar cells. The system can cold-start and run in low light conditions (in this case 20 lux and below, using white LEDs as light sources). Four solar cells of 1 cm2 each are used, yielding a total active surface of 4 cm2. The system includes a capacitive sensor that acts as a touch detector, a crystal-accurate real-time clock (RTC), and a Cortex-M3-compatible microcontroller integrating a Bluetooth Low Energy radio (BLE) and the necessary stack for communication. A capacitor of 100 μF is used as energy storage. A low-power comparator monitors the level of the energy storage and powers up the system. The combination of the RTC and touch sensor enables the MCU load to be powered up periodically or using an asynchronous user touch activity. First tests have shown that the system can perform the basic work of cold-starting, sensing, and transmitting frames at +0 dBm, at illuminances as low as 5 lux. Harvesting starts earlier, meaning that the potential for full function below 5 lux is present. The system has also been tested with other light sources. The comparator is a test chip developed for energy harvesting. Other elements are off-the-shelf components. The use of commercially available devices, the reduced number of parts, and the absence of complex storage elements enable a small node to be built in the future, for use in constantly or intermittently poorly lit places.

Optimal topology control of monitoring sensor network based on physical layer security for smart photovoltaic power system

In order to realize the intelligent management of photovoltaic power generation system, wireless sensor network (WSN) is considered as a promised solution. However, the energy of sensor nodes in WSN is limited and the security is not guaranteed. In order to prolong the service life of WSN and to improve the security of monitoring, this paper comprehensively considers the factors those affect the security and efficiency of WSN, such as transmission power, sensor association and the choice of Cluster Head (CH). Therefore, this paper proposes a joint sensor secure rate and energy efficiency optimization algorithm to obtain the optimal topology and transmission power design. Then, an effective iterative algorithm based on Block Coordinate Descent (BCD) technology is proposed to obtain the optimal solution to minimize the energy consumption of WSN and maximize the secure rate of sensor networks, thus ensuring the security and reliability of monitoring. Sensor association is modeled as a 0–1 multi-knapsack optimization problem. Different complexity methods are implemented to solve this NP-hard problem, and their performance are compared with simulations.

Enhancing Heterogeneous Wireless Sensor Networks Using Swarm Intelligence –Based Routing Protocols

The design of efficient communication protocols for wireless sensor networks has aroused great interest in the research community, especially in the face of the limited energy of sensor nodes and the frequent change in network topology. Routing remains a challenging problem in wireless communications, as deploying or replacing sensor nodes in hazardous environments is difficult. Many studies have been devoted to alleviate certain limitations, such as clustering to maintain network connectivity, injecting heterogeneity to avoid the rapid death of nodes, or incorporating evolution-based optimization methods to find the best network configuration. This work combined heterogeneity and swarm-based optimization to efficiently balance energy consumption between nodes to increase network reliability. Specifically, this work employed the binary particle swarm optimizer and the binary artificial bees colony optimizer to find approximately the optimal set of cluster heads (CHs) with their optimal number. Based on the probabilistic principle of the heterogeneous protocols: SEP, EDEEC, and BEENISH, a new refined formulation of CHs selection using swarm optimization is proposed. The swarm flight is guided towards the best CHs with an objective function representing a good balance between the initial and residual energy of nodes. Compared to the standard heterogeneous protocols SEP, EDEEC, and BEENISH, the developed protocols significantly perform better in terms of stability (FND), the round of half nodes' death (HND), the network lifetime (LND), and energy saving. Indeed, the BABC-SEP was found 31,66% better than SEP in terms of remaining energy percentage, and CHs selection in EDEEC and BEENISH using BABC improved them by more than 20% in the percentage of remaining energy.

A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme

Clustering methods are promising tools for ensuring the network scalability and maintainability of large-scale flying ad hoc networks (FANETs). However, due to the high mobility and limited energy resources of unmanned aerial vehicles (UAVs), it is difficult to maintain the network reliability and extend the network life of FANETs. In this paper, a new K-means algorithm is developed, and a dynamic transmission power of the cluster heads based clustering (DTPCH-C) scheme is proposed. The goal of this scheme is presented for FANETs to improve the reliability and lifetime of FANETs. Firstly, the optimal number of clusters is calculated and the initial UAV clusters are set up by a K-means algorithm. Then, using a weighted clustering algorithm, the adaptive node degree, the node energy and the distance from the cluster head are weighted and summed for the cluster head election. In the process of inter-cluster communication, the cluster head adjusts its transmit power in real-time through meshing and mobile prediction, thus saving the energy consumption and improving the network lifetime. The proposed DTPCH-C simultaneously optimizes the cluster number, the cluster head energy consumption, the selected cluster head, and the cluster maintenance process. The simulation results show that compared with traditional clustering methods, the proposed DTPCH-C has obvious advantages in terms of the network reliability, network life, and energy consumption.

FBCHS: Fuzzy Based Cluster Head Selection Protocol to Enhance Network Lifetime of WSN

With enormous evolution in Microelectronics, Wireless Sensor Networks (WSNs) have played a vital role in every aspect of daily life. Technological advancement has led to new ways of thinking and of developing infrastructure for sensing, monitoring, and computational tasks. The sensor network constitutes multiple sensor nodes for monitoring, tracking, and surveillance of remote objects in the network area. Battery replacement and recharging are almost impossible; therefore, the aim is to develop an efficient routing protocol for the sensor network. The Fuzzy Based Cluster Head Selection (FBCHS) protocol is proposed, which partitions the network into several regions based on node energy levels. The proposed protocol uses an artificial intelligence technique to select the Cluster Head (CH) based on maximum node Residual Energy (RE) and minimum distance. The transmission of data to the Base Station (BS) is accomplished via static clustering and the hybrid routing technique. The simulation results of the FBCHS protocol are com- pared to the SEP protocol and show improvement in the stability period and improved overall performance of the network.

Two-dimensional convolution-based power system reliability assessment

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy.

A Centralized Energy-Efficient Clustering Protocol for Wireless Sensor Networks

Energy saving is one of the hot research topics in wireless sensor networks (WSNs), and a number of solutions have been proposed. Clustering protocols have become the widely used method to reduce energy consumption. However, the energy efficiency in existing clustering protocols is limited because of the excessive energy consumption in periodic reclustering. In this article, we propose a centralized energy-efficient clustering (CEEC) protocol, which includes two aspects of node energy balancing schemes, to enhance energy efficiency in WSNs. As for intracluster energy balancing, we employ the cluster head (CH)-rotation approach to balance the energy load among nodes in each cluster. With respect to intercluster energy balancing, we suggest four sequential algorithms to balance the energy consumption among clusters, and the main contributions are as follows: 1) a size-balanced cluster formation (SBCF) algorithm aims to partition the whole network into several small clusters of the same size; 2) a low-cost forecast-based maintenance mechanism is raised to help the base station (BS) maintain the total nodes’ energy information of each cluster in the network, and then, BS utilizes our introduced intercluster energy adjustment (ICEA) algorithm to reallocate a few cluster members in related clusters with low overhead; and 3) an energy-aware data forwarding (EDF) scheme is to maintain the nodes’ energy balance among clusters in intercluster communication. Simulation results show that our CEEC protocol outperforms the classical clustering mechanisms in terms of network lifetime, energy consumption, and network throughput.

Tunable unconventional kagome superconductivity in charge ordered RbV3Sb5 and KV3Sb5

Unconventional superconductors often feature competing orders, small superfluid density, and nodal electronic pairing. While unusual superconductivity has been proposed in the kagome metals AV3Sb5, key spectroscopic evidence has remained elusive. Here we utilize pressure-tuned and ultra-low temperature muon spin spectroscopy to uncover the unconventional nature of superconductivity in RbV3Sb5 and KV3Sb5. At ambient pressure, we observed time-reversal symmetry breaking charge order below {T}_{{{{{{{{rm{1}}}}}}}}}^{*}simeq 110 K in RbV3Sb5 with an additional transition at {T}_{{{{{{{{rm{2}}}}}}}}}^{*}simeq 50 K. Remarkably, the superconducting state displays a nodal energy gap and a reduced superfluid density, which can be attributed to the competition with the charge order. Upon applying pressure, the charge-order transitions are suppressed, the superfluid density increases, and the superconducting state progressively evolves from nodal to nodeless. Once optimal superconductivity is achieved, we find a superconducting pairing state that is not only fully gapped, but also spontaneously breaks time-reversal symmetry. Our results point to unprecedented tunable nodal kagome superconductivity competing with time-reversal symmetry-breaking charge order and offer unique insights into the nature of the pairing state.

A neuro-fuzzy approach for multipoint data prediction in passive clustered wireless sensor networks

In Passive Clustered Wireless Sensor Networks (WSNs), energy is lost in a sensor node during the data transmission. In order to avoid the energy loss due to data transmission, a data prediction technique is implemented. In this paper, we present a new multi-point data prediction technique, in which the prediction algorithm is initially implemented at both member nodes and cluster heads. The algorithm is updated to cluster head by member nodes by tracking temporal correlation of data. Neuro-Fuzzy model is used as a predictor in both member nodes and cluster heads. The simulation is performed using MATLAB and the overall energy in nodes seems to increase. The mean square error (MSE) value is reduced to greater extend.

TEARP: New Temperature and Energy Aware Routing Protocol for Wireless Body Area Network (WBAN)

In the near future, the biomedical research will be one of the major applications for sensor networks. Wireless Body Area Networks (WBANs) consist of a set of bio-sensors that are implanted inside the human body to sense several health conditions, the communication between these sensors is made wirelessly. These sensors are charged by a battery. The nodes will transmit the sensed data through other nodes in the order it to the base station which is located outside the sensing environment which in this case represents the human body. The communication field consumed a significant amount of node’s energy and minimizes the lifetime of the node. Furthermore, the sensor’s operation generates heat which raises the tissue temperature around the implanted sensors. Therefore, the research majorly addresses these issues of communication, a new Temperature and Energy-Aware Routing Protocol (TEARP) has been presented for WBANs implementing a new route-choice system that seeks to equalize the use of energy of sensors and minimize their overheating. This method is depending on a formula that takes into account the remaining energy of sensor nodes, the distance between nodes as well as between nodes to sink, in addition to their temperature when selecting the following relay node. The simulation results show that the TEARP protocol has improved network lifetime efficiency by about 300%and 90%, stability by about 82% and 11%, and residual energy by about 71% and 74% compared to the M-ATTEMPT and Simple protocols respectively.

An accurate attack detection framework based on exponential polynomial kernel‐centered deep neural networks in the wireless sensor network

AbstractA novel type of wireless network that is rising in popularity with a wide assortment of civilian along with military applications is called WSN. Owing to several factors like nodes with resources that are constrained and packages that are resistant to tamper, WSNs are tremendously vulnerable to internal attacks and mainly external attacks also. Attacks on such critical schemes comprise penetrations into their network along with the installation of malicious tools or programs, which can reveal sensitive data or alter the specific physical equipment's behavior. For their action, the wireless networks are utilized by the threats like spoofing, injection, denial of services, and numerous attacks. Thus, for protecting devices from intruder attacks, security solutions are necessary. An intrusion detection system (IDS), which is wielded for detecting attacks against a system or a network by evaluating their activities along with events, is a tool. In this article, an efficient attack detection technique grounded on exponential polynomial kernel‐centered deep neural networks (EPK‐DNN) is proposed since intrusion detection is crucial in securing the data. Intrusion detection in WSN is extremely intricate for tasks like fault diagnosis, and real‐time monitoring applications, owing to the WSN's dynamicity. To find diverse attacks along with to safeguard WSNs from security risks, numerous detection methodologies are created, because intrusion detection is decisive for protecting the data in WSNs. However, owing to the restricted resources and energy of WSN nodes, widespread computation and so forth, they are inefficient. In this article, an efficient attack detection methodology centered on EPK‐DNN is proposed to lessen these problems. The attack detection system's training is the foremost step in the EPK‐DNN technique. In step one, the input data are preprocessed; and then, in the training process, the preprocessed data are exploited for attribute extraction. In step two, by utilizing the linear scaling based BAT optimization (LS‐BAT), the major attributes are chosen. Then, to detect attacks in WSN, the chosen features are trained by the EPK‐DNN. In step three, by utilizing the Damerau‐Levenshtein‐based K‐means algorithm (DL‐K‐Means), the WSN network is initialized along with the sensor nodes are clustered. To amass the sensor data, the cluster heads are selected by utilizing the swap, displacement, and reversion‐centered rock hyraxes swarm optimization algorithm. After that, for testing, the sensor data are inputted into the trained ADS. The outcomes exhibited that the greatest accuracy rate of 97.21% was attained by the EPK‐DNN technique for the real‐time BC dataset and 96.86% for the real‐time MC dataset. When analogized to the customary deep learning (DL) methodologies, the investigational findings reveal that the EPK‐DNN technique accomplishes desirable detection accuracy.

Application of nature inspired optimization algorithm on hierarchal LEACH protocol for improved performance

In today’s scenario Wireless Sensor Network is playing a dynamic role with the advancement in IOT for mission critical applications due to the fact that these applications are either event driven or time driven applications. Sensor network comprises of battery operated tiny sensors for sensing any change in the environment since the battery life is limited so is the sensor life. One solution to overcome this problem is the use of clustering protocol. A lot of optimization techniques are employed for performance improvement of the hierarchal LEACH routing protocol. This paper explores the three types of Foraging behavior for cluster head selection which means the process to search for food or the energy of the node so that it can be elected as CH. A fitness function is defined relating to residual energy, CH distance to Base station, position of base station. Theses algorithm progresses on the basis of the energy of sensor node in the deployed network. Here the higher energy of node is analogous to food for which the search goes on; in doing so the best CH is selected. The algorithm uses the best location of the node. The overall performance of the network is improved in terms of Energy consumption; packet delivery ratio metrics the results are compared with the existing LEACH protocol.

Optimization and Analysis of Wireless Networks Lifetime using Soft Computing for Industrial Applications

Recently, wireless networks are applied in various engineering and industrial applications. One of the critical problems in wireless network system optimization in intelligent applications is obtaining an adequate energy fairness level. This issue can be resolved by applying effective cluster-based routing optimization with multi-hop routing. Hence a new network structure is developed that is derived from energy consumption architecture by applying soft computing strategies such as evolutionary operators in determining the exact clusters for optimizing energy consumption. The new effective evolutionary operators are tested in the optimization of a lifetime. The proposed method is simulated for different values of the routing factor, α, for different types of networks. The energy levels range from 0.4 to 0.8, achieving good results for nearly 2500 rounds. The proposed strategy optimizes the clusters, and its head is selected reliably. The optimization of cluster head choice has been done based on the base station distance, the energy of the node, and the node's energy efficiency. The reliability of the long-distance nodes is increased during the data transmission by modifying the size of the area of the candidate set of nodes in contrast the near-distance node's energy consumption is reduced. For the energy levels that range from 0.4 to 0.8, the higher network throughput is obtained at the same time network lifetime is optimized compared to other well-known approaches. The proposed model is expected for different industrial wireless network applications to optimize the systems during the long-run simulation and to achieve high reliability and sustainability.

Resource-aware routing in opportunistic networks: existing protocols and open research issues

Opportunistic networks provide an effective communication in challenging scenarios such as interplanetary communication, post-disaster situation, etc. As the nodes have limited resources, thus designing an efficient routing protocol in opportunistic networks always remains a challenging task. Several review papers presenting features and applications of opportunistic networks, mobility models, challenges, taxonomies of routing, and incentive schemes exist, but a comprehensive review on resource-aware routing appears to be missing. Therefore, we propose a novel taxonomy for categorising existing resource-aware routing protocols proposed in this field. These are mainly categorised into energy-aware, buffer aware, bandwidth aware, and hybrid routing that considers the energy of nodes, buffer space, bandwidth, and multiple parameters respectively to make forwarding or routing decision. Further, we discuss various existing routing protocols with their strengths and weaknesses, leading to open research issues. This survey aims to help future researchers devise a novel routing protocol for opportunistic networks.

Performance analysis of multi-layered clustering network using fault tolerance multipath routing protocol (MRP-FT) in a wireless sensor network (WSN)

Wireless sensor networks (WSNs) are ad hoc and self-configuring networks having the possibility that any sensor node can connect or leave the network. With no central controller in WSN, wireless sensor nodes are considered responsible for data routing in the networks. The wireless sensor nodes are very small in size and have limited resources, therefore, it becomes difficult to recharge or replace the battery of the sensor nodes at far places. The present study focused on reducing the battery consumption of the sensor nodes by the deployment of the newly proposed Fault Tolerance Multipath Routing Protocol (MRP-FT) as compared with the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol under particle swarm optimisation based fault tolerant routing (PSO-FT) technique. The proposed algorithm of MRP-FT-based on the dynamic clustering technique using Boltzmann learning of the neural network and the weights were adjusted according to the area of networks, number of nodes and rounds, the initial energy of nodes (E0), transmission energy of nodes (d < d0), data reviving energy (ERX), data aggregation energy (EDA), energy dissipation on free space (εfs), energy dissipation of multi-path delay (ε mp) and the packet size. The results of the present study revealed that the packet heads remains constant during the initial time period (up to 2500 seconds), and exhibited a sharp increase thereafter. The network energy consumption remains constant up to 2300 s and exhibited a sharp increase thereafter. High energy use after 2300 seconds describes the faulty occurrence in the network and leads to decreased reliability (%) of the existing protocol. The energy consumption was substantially reduced by 15 J (38.5%) due to the implementation of the newly proposed MRP-FT, compared with the existing PSO-FT protocol. The reduced delay of 22 packets was achieved with MRP-FT protocol, compared with the existing PSO-FT technique-based LEACH protocol. Nonetheless, the MRP-FT enhanced the packet overhead of 10.8% over the current protocol due to deploying more uniform clustering. Additionally, a 12% increase in reliability was achieved with the implementation of MRP-FT protocol emphasizing that network lifespan was prolonged efficiently with the proposed algorithm.

Path Generation Protocol to Improve Lifetime of WSN

In WSN sensor nodes are connected to their neighbor nodes by a wireless channel and these all nodes are operated on a battery to perform any operations. So improving the lifetime of WSN is a major research challenge by effective utilization of each and every node's energy. The lifetime of WSNs can be improved in three steps: path creation, path selection, and power saving. In existing works, path selection has the major role to utilize and enhance lifetime. In the proposed PStack algorithm, path generation is a major concern, and routing is applied. It takes advantage of and solves the limitations of existing algorithms using the stack concept in WSN. The proposed algorithm shows a better result than the improved LEACH protocol because it uses the data structure with graph theory.