Energy-efficient Data Transmission Research Articles

HT-WSO: A hybrid meta-heuristic approach-aided multi-objective constraints for energy efficient routing in WBANs

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.

An optimized deep learning-based fault-tolerant mechanism for energy efficient data transmission in IoT

<p class="Author">Artificial Intelligence (AI) based framework for the Internet of Things (IoTs) have gained worldwide attention in recent years, mainly with the explosion of Micro-Electro-Mechanical Systems (MEMS) technology. Basically, MEMS has facilitated the development of tiny and smart sensors for the IoT-based framework. An AI-based IoT model is an emerging technology that helps in both fault-tolerant as well as energy-efficient data transmission purposes. For efficient data transmission in an IoT-based model, the concept of Wireless Sensor Network (WSN) plays a vital role that comprises various sensor nodes that communicate together to monitor and gather information from the various Region of Interest (RoI). Generally, sensor nodes are tiny in size and having a small battery life, limited sensing, processing, and communication capabilities. So, the fault-tolerant mechanism for energy efficient data transmission in IoT is a good initiative with the combination of Deep Learning as an AI approach. In this research article, the concept of deep learning-based fault-tolerant mechanism in IoT frameworks for energy efficient data transmission is proposed in an optimized manner. Here, the concept of the Grouped-Bee Colony (GBC) algorithm is designed for the fault detection mechanism as an optimization approach along with the Deep Learning as an AI.<em></em></p>

An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT

ABSTRACT: The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML’s area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network’s lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT. Communicated by Ramaswamy H. Sarma

Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

<span lang="EN-US">A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.</span>

Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC

Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC

Sustainable Next-Generation Network Design using Social Aware and Delay Tolerant Approach

With the ever-growing popularity of smartphones, new services are emerging where the local and positioning aspects become more important. Additionally, new routing algorithms are being developed based on various networking technology such as Delay Tolerance Networks (DTN), Deviceto- Device communication, Opportunistic Network, etc. DTN is categorized as stable and unstable, a continuous path between the source and the destination node. Here, communication is done by carrying the message through the intermediate relay node on the store and carry forward paradigm. In this work, a Sustainable Energy Delay Tolerance Approach (SEDA) considers nodes mobility patterns for energy-efficient data transmission. This routing model is based on the appropriate usage of node information along with node mobility and its contacts for data dissemination. Next step is applying some techniques to predict the most likely position for next encounter. We will design and implement a social relation-aware routing relation-awareness the social relation as a part of the routing matrix. We assume that most of the movement of nodes are included in various numbers of social contacts; therefore, there is a higher probability of message delivery to the destination. Simulation results show that the proposed routing protocol improves the delivery ratio by reducing network size and distance, which also impacts energy saving.

Traffic and delay aware routing using optimization algorithm for wireless sensor networks

The Internet of Things (IoT) is a system of machines, computing devices, electronic equipment, and different sensors. It forms a network, where the transmission of device-related data can be accomplished. The devices in the IoT are connected to each other through wireless links and form ad-hoc networks. In IoT based applications, the lifetime of the communicating nodes is a greater concern. The network lifetime can be maximized by introducing energy efficient data transmission in the network. Therefore, a traffic and delay-aware energy-efficient routing (TADEER) protocol for IoT-based networks are proposed in this work. The proposed technique assigns delay for transmitting data based on the criticality level of data and traffic rate at the forwarding nodes. Fixing delays for data transmission helps to avoid unnecessary transmissions. The route selection process is implemented using an optimization algorithm. A Fuzzy logic (FL) based biogeography-based optimization (BBO) algorithm is presented in this work. Thus, the number of data transmission and energy consumption can be reduced. The performance of the proposed method is evaluated by analyzing transmission delay, network lifetime, and energy consumption. By comparing the simulation results to the existing methods TEAR and ETASA, the simulation results are validated.

Energy-Efficient Data Aggregation and Cluster-Based Routing in Wireless Sensor Networks Using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm

One of the major significant problems in the existing techniques in Wireless Sensor Networks (WSNs) is Energy Efficiency (EE) because sensor nodes are battery-powered devices. The energy-efficient data transmission and routing to the sink are critical challenges because WSNs have inherent resource limitations. On the other hand, the clustering process is a crucial strategy that can rapidly increase network lifetime. As a result, WSNs require an energy-efficient routing strategy with optimum route election. These issues are overcome by using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm for Data Aggregation and Cluster-Based Routing in WSN (TFR-DLN-PMPOA-WSN) which is proposed to expand the network lifetime. Initially, Tasmanian Fully Recurrent Deep Learning Network (TFR-DLN) is proposed to elect the Optimal Cluster Head (OCH). After OCH selection, the three parameters, trust, connectivity, and QoS, are optimized for secure routing with the help of the Pelican Variable Marine Predators Optimization Algorithm (PMPOA). Finally, the proposed method finds the minimum distance among the nodes and selects the best routing to increase energy efficiency. The proposed approach will be activated in MATLAB. The efficacy of the TFR-DLN- PMPOA-WSN approach is assessed in terms of several performances. It achieves higher throughput, higher packet delivery ratio, higher detection rate, lower delay, lower energy utilization, and higher network lifespan than the existing methods.

Energy-efficient data transmission with proportional rate fairness for NANs of smart grid communication network

In smart grid, the neighborhood area network (NAN) serves as a bridge connecting smart meters and meter data management system (MDMS) and is one of the most important components of the smart grid communication network. In this work, we study the energy-efficient data transmissions from NAN gateways (i.e., routers) to the concentrator that connects the MDMS. Particularly, to assure that each router associated with diverse number of associated smart meters can achieve a required data rate, a set of proportional rate fairness constraints are imposed into the optimization problem. In this case, an optimization problem that balances energy efficiency and fairness is formulated. Due to the fractional form of objective function, the combinatorial constraints on channel allocation variables, and the proportional rate fairness requirements, the formulated energy-efficient data transmission problem is non-convex and extremely computationally complex. In order to solve the problem efficiently, firstly, a subtractive transformation is introduced to handle the objective function, and an iterative algorithm based on Dinkelbach method is proposed; then for the inner loop optimization problem in each iteration, a low-complexity suboptimal algorithm that separates channel allocation and power distribution is developed. Finally, numerical results demonstrate that the proposed algorithm converges in a few steps and achieves a near-optimal energy efficiency performance.

Cryptographic Data Security for Reliable Wireless Sensor Network

Wireless Sensor Networks (WSNs) are widely utilized in a variety of applications. Attackers might interfere with equipment, and eavesdrop on conversations. Attackers can also modify the data which has been communicated or connect unauthorized devices to the network if such devices are deployed in the physical environment. To ensure WSN security, messages exchanged between communicating nodes must be encrypted, and for encryption and decryption, the network must store a key. Consequently, Key management is of significant importance in achieving security in WSN. Obtaining such a key arrangement in a resource-constrained environment is required for secure and energy-efficient data transmission in WSN. Current algorithms have certain security issues, such as vulnerability to plaintext attacks, brute force attacks, side-channel attacks, and computational complexity. Cryptography along with clustering has been found to be a practical and efficient technique to improve the secure and energy-efficient data transmission in WSNs. In this article we have used an asymmetric Elliptic Curve Cryptography (ECC) method for key generation whereas encryption and decryption of data, has been done by a hybrid of Advanced Encryption Standard (AES) and ECC cryptography A novel algorithm has been developed that incorporates both techniques (Advanced Encryption Standard (AES) and ECC cryptography) along with clustering through standard LEACH protocol for improving energy efficiency, data security, and network lifetime. The proposed algorithm has been able to overcome the issue of key exchange that plagues AES, simpler than ECC and more reliable than AES. More significantly, it is able to encounter a variety of security threats including side-channel attacks. The proposed algorithm is better and has been compared to current techniques with respect to time complexity, encryption time and decryption time to demonstrate its efficacy.

Novel Framework for Secure Data Aggregation in Precision Agriculture with Extensive Energy Efficiency

Precision agriculture (PA) is the next generation of a technological revolution in smart farming, where sensing technology is the core technological player. Energy-efficient data transmission in PA via sensing technology is possible only when additional security measures are synchronized. Nevertheless, security considerations often introduce additional overhead. Thus, it is necessary to develop an efficient mechanism to achieve an optimal trade-off between security and resource efficiency. The prime purpose of the proposed study is to introduce a lightweight communication protocol that can ensure an adequate balance between energy efficiency and maximum-security demands to benefit the success of PA. This paper proposes a synchronized framework where unique public-key encryption has been used, unlike any existing approach to facilitate the participation of legitimate on-field sensors in PA. On the other hand, an algorithm for energy efficiency where unique structural management of routing is discussed concerning aggregator nodes. In contrast, the security algorithm discusses a uniquely progressive and noniterative mechanism to perform secure data aggregation with a parallel validation technique. The proposed logic is scripted in MATLAB, considering a suitable PA environment where comparative assessment is carried out on a uniform testbed. The study outcome exhibited the effectiveness of the proposed scheme concerning better energy efficiency and higher resiliency from threats in contrast to existing schemes.

Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications

Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.

Trusted Cluster-Based Communication for Wireless Sensor Network Using Meta-Heuristic Algorithms

The mobile transient and sensor network’s routing algorithm detects available multi-hop paths between source and destination nodes. However, some methods are not as reliable or trustworthy as expected. Therefore, finding a reliable method is an important factor in improving communication security. For further enhancement of protected communication, we suggest a trust cluster based secure routing (TCSR) framework for wireless sensor network (WSN) using optimization algorithms. First, we introduce an efficient cluster formation using a modified tug of war optimization (MTWO) algorithm, which provides load-balanced clusters for energy-efficient data transmission. Second, we illustrate the optimal head selection using multiple design constraints received signal strength, congestion rate, data loss rate, and throughput of the node. Those parameters are optimized by a butterfly optimal deep neural network (BO-DNN), which provides first-level security towards the selection of the best head node. Third, we utilize the lightweight signcryption to encrypt the data between two nodes during data transmission, which provides second-level security. The model provides an estimation of the trust level of each route to help a source node to select the most secure one. The nodes of the network improve reliability and security by maintaining the reliability component. Simulation results showed that the proposed scheme achieved 45.6% of delivery ratio.

A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

Mobile Ad Hoc Network (MANET) is an infrastructure-less network that is comprised of a set of nodes that move randomly. In MANET, the overall performance is improved through multipath multicast routing to achieve the quality of service (quality of service). In this, different nodes are involved in the information data collection and transmission to the destination nodes in the network. The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes. The route identification and routing are established based on the data broadcast by the network nodes. In transmitting the data packet, evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network. Furthermore, energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate. In this paper, a Gradient-Based Energy Optimization model (GBEOM) for the route in MANET is proposed to achieve an improved data delivery rate. Initially, the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing (WMC-SMLB) technique is utilized for obtaining energy efficiency and load balancing routing. The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET. Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation. Next, the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency. The proposed GBEOM exhibits ∼4% improved performance in MANET routing.

Energy-Efficient Pairing and Power Allocation for NOMA UAV Network Under QoS Constraints

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

Energy efficient data transmission in intelligent transportation system (ITS): Millimeter (mm wave) based routing algorithm for connected vehicles

In the context of the Intelligent Transportation System (ITS), smart urban communication provides both significant potential and formidable problems for linked cars. A high rate of data transfer in vehicular communication is an exciting problem for academics because of the frequent disconnection between linked cars. Recent years have seen the development of millimetre wave (mm Wave) technologies as a solution to the problem of high data transfer rates. In this research, a mm Wave-incorporated routing algorithm and an appropriate channel selection are presented in order to facilitate the transmission of a big quantity of data in an urban setting in an effective manner. Beam formation and alignment, in particular, are responsible for overcoming barriers and finding alternative routes to reach a goal without incurring path loss. Using this stochastic technique, we attempt to transmit data in Line-of-sight (LoS) and non-line-of-sight (NLoS) environments by taking into account a city scenario, narrowing the bandwidth from 90 degrees to 45 degrees, and therefore lowering the risk of signal interference and noise. The suggested system is evaluated for a vehicle range of 20, 30, 40, and 50 with regard to characteristics like Packet Delivery Ratio (PDR), Latency, and throughput.

Energy efficient and secure healthcare data transmission in the internet of medical things network

Energy efficient and secure healthcare data transmission in the internet of medical things network

Performance of History-based Water-Filling Algorithm for Energy-Efficient Data Transmission over Different Fading Channels

In this paper, we tackle a resource allocation problem over multiple fading channels in wireless networks. This problem is investigated in two ways. First, we consider the problem over the whole multiple fading channels altogether with no power constraint. We look for an optimal solution for this problem by considering an offline waterfillling algorithm. Then, we also propose history-based online waterfilling algorithms for this problem. This online algorithm uses the history partially in order to determine a waterfilling level based on that part of history. Then, the online policy applies this history-based determined waterfilling level to transmit data over the time horizon of the problem. The relative performance of the online and offline policies is evaluated for various types of fading channels (Rayleigh, Rician, Nakagami, Weibull) over various time horizons. The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons and by using larger portions of history.

Improved Deep Convolutional Neural Network Based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

Wireless Sensors Networks (WSN) is the self-configured Wireless Ad hoc Networks (WANET) for Internet of Things (IoT) which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes’ (MNs’) presence. Thus, to shun security threats, it is vital to find and isolate those MNs. Consequently, this work proffered a solution for detecting MNs in WSN utilizing every SN's parameters. This work not only regards the security but also rendered energy-efficient data transmission (DT) by means of choosing the Cluster Head (CH) centred on the sensor's residual energy. The Improved Deep Convolutional Neural Network (IDCNN) identifies the MN and then isolates them into the malicious list box in the Malicious Nodes Detection (MND) phase. In the energy-efficient DT phase, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Extended K-Means (</i> EKM) algorithm clusters the Trusted Nodes (TN), and the t-Distribution based Satin Bowerbird Optimization (t-DSBO) algorithm selects an individual CH for each cluster centred on those nodes’ residual energy. The data of that cluster is transmitted to the Base Station (BS) through the CH. The t-DSBO selects an alternate CH if the current CH loses its energy. The proposed techniques effectively detect the MN and render energy-efficient DT, which is experimentally proved by comparing it with existing techniques.