Energy Efficient Data Aggregation Research Articles

A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Opportunistic data gathering in IoT networks using an energy-efficient data aggregation mechanism

Opportunistic data gathering in IoT networks using an energy-efficient data aggregation mechanism

Energy-Efficient Data Aggregation and Collection for Multi-UAV-Enabled IoT Networks

Energy-Efficient Data Aggregation and Collection for Multi-UAV-Enabled IoT Networks

Energy efficient and secure data aggregation based on rock hyrax swarm optimisation and deep recurrent auto encoder in WSNs

Energy efficient and secure data aggregation based on rock hyrax swarm optimisation and deep recurrent auto encoder in WSNs

Brief Overview on Wireless Sensor Network Technologies in the Internet of Things (IoT)

Wireless systems based on IoT-based has evolved rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has expanded across several different applications in real-time. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. Wireless Sensor Network technologies play a vital role in enabling the IoT. In this analysis, we delve deep into the essential elements, functionalities, and applications of WSN technologies within the context of IoT. We explore the diverse sensor nodes, communication protocols, and network architectures that constitute WSNs, highlighting their crucial function in gathering, transmitting, and processing data from the physical environment. The Internet of Things (IoT), which enables seamless connectivity among a variety of physical objects, has completely changed how we interact with the world around us. Wireless Sensor Network (WSN) technologies, which is the central nervous system of this interconnected ecosystem are a fundamental IoT enabler. This paper overview the essential elements, functionalities, and applications of WSN technologies in context of Internet of Things (IoT). We cover the various sensor nodes, communication protocols, and network architectures that make up WSNs in this thorough analysis, highlighting their functions in gathering, transmitting, and processing data from the physical environment. We explore the particular difficulties. WSN technologies find applications across various domains within the IoT landscape. From smart cities and precision agriculture to healthcare and industrial automation, WSNs play a crucial role in transforming these sectors. We showcase real-world case studies that illustrate the practical implementation and impact of WSNs in these domains, providing concrete examples of their effectiveness. Our goal is to provide you with engaging and content that adds value to your understanding of the Internet of Things and Wireless Sensor Networks.

A trust and optimal energy efficient data aggregation scheme for wireless sensor networks using QGAOA

A trust and optimal energy efficient data aggregation scheme for wireless sensor networks using QGAOA

Energy Efficient Data Aggregation with Dynamic Mobile Sink-Based Path Optimization in Large Scale WSNs Using Reinforcement Learning

Energy Efficient Data Aggregation with Dynamic Mobile Sink-Based Path Optimization in Large Scale WSNs Using Reinforcement Learning

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.

Status-aware and energy-efficient data aggregation for inter-tidal monitoring systems

The Inter-tidal area is an important habitat for marine organisms and the wireless sensor network is a good candidate for data collection and environmental monitoring, yet faces challenges. Due to the poor link condition, our observations in three deployed inter-tidal wireless sensor networks (IT-WSN) reveal that only around 40% of packets can be received with extremely long transmission delays. On the other hand, relay nodes run out of energy much early than leaf nodes, which significantly shorten the system lifespan. Based on these observations in real systems, we specifically design a compressive-sensing-based data aggregation (CS-DA) mechanism and an adaptive operation strategy (AOS) for the system to mitigate the above problems. The real-trace-driven evaluation demonstrates that we can collect all data packets with a 94.46% accuracy with only 40% of packets received, while the lifespan of the system can be extended by 54.9% with AOS.

Modified squirrel search algorithm based data aggregation framework for improved network lifetime in wireless sensor network

WSNs (Wireless Sensor Networks) have various applications that include environments and traffic monitors, surveillance of battlefields, innovative agriculture because of their communication effectiveness. Individual sensor nodes in WSNs have restrictions in energy consumptions, packet deliveries, network longevities, and latencies. In previous work, an energy efficient data aggregation is achieved by developing an EADSLPBNC-DA framework for sensor network. Data aggregation performance is enhanced with minimum time through classifying the sensor nodes. But it has issue with data aggregation time, energy level of nodes will be changes periodically which is not concentrated in the previous work. The goal of this study is to address aforementioned difficulties and hence MSSA (Modified Squirrel Search Algorithm) which can improve data aggregations is proposed. Data aggregations based on CHs (cluster heads) improves energy efficiencies and network longevities. Aggregations of CHs related data is critical for minimizing sensor node energy consumptions in WSNs and their selections enhance WSNs lifespan. The MSSA-DA generates best fitness values using objective function which selects optimal sensor nodes as CHs to aggregate data optimally. The sensor nodes are chosen depending on their proximities, energies, and bandwidths. The suggested MSSA-DA system was simulated for assessing network lives, energy consumptions, accuracies, and data aggregation times. Experimental results suggested MSSA-DA model enhances data aggregation accuracies and network lives while consuming lesser energies and times.

Improved buffalo optimized deep feed forward neural learning based multipath routing for energy efficient data aggregation in WSN

Wireless sensor networks comprised inadequate computational power and memory for application developers.Data aggregation is the method of gathering the useful information. Data aggregation is the new process for reducing the energy consumption. In WSN, data aggregation is an efficient method with limited resources. Many classification methods were introduced for performing data aggregation in WSN. But, the energy consumption and delay was not reduced by existing techniques. In order to address these problems, an Improved Buffalo Optimized Route Selective Deep Feed Forward Neural Learning (IBORSDFFNL) Model is introduced. The main objective of the IBORSDFFNL Model is to construct multiple paths for efficient data aggregation in WSN. IBORSDFFNL Model carried out route path discovery process and optimal route path selection process. The multiple route paths are constructed from distributed sensor nodes. Among the constructed route paths, an optimal route path is chosen by using improved buffalo optimization for performing efficient data aggregation in WSN. After selecting efficient optimal route path, data aggregation process is carried out with minimal delay and higher packet delivery ratio. Experiments are carried out to determine the performance in terms of end-to-end delay, energy consumption and packet delivery ratio.Simulation results show that the proposed model attained lesser energy consumption and end-to-end delay for data aggregation and increases the packet delivery ratio in WSNs.

FC-SEEDA: fog computing-based secure and energy efficient data aggregation scheme for Internet of healthcare Things

FC-SEEDA: fog computing-based secure and energy efficient data aggregation scheme for Internet of healthcare Things

Energy-efficient Data Aggregation in Low-power Wireless Networks with Sensors of Discrete Transmission Ranges: A Mathematical Framework for Network Design

The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and monitoring applications. The volume and velocity of the data generated by a large number of sensors and monitors used in such applications are huge. The harnessing of such big data is critical to the real-time control of underlying real-world processes. Future generation networks favor the deployment of sensors with different discrete transmission ranges. Such multiple transmission ranges introduce different connectivity constraints in the network. Advanced strategies are under investigation for designing energy-efficient data aggregation schemes in such connectivity-constrained networks. To aid such works, we introduce a mathematical framework that captures the salient features of low-power networks with sensors of multiple transmission ranges. The proposed framework can serve as a baseline platform to investigate different networking problems in such systems. Further, we consider a cluster-based multihop network with fixed intra-cluster and inter-cluster transmission ranges and address the problem of designing an optimal network configuration for minimizing the data transmission in the network. We model the problem as an integer linear program, which can be applied only to networks of small sizes because of the hardness of the problem. To address the problem in large-scale networks, we design a polynomial-time approximation method using the proposed mathematical framework. The performance of the proposed methods is evaluated under compression schemes based on compressive sensing.

Energy-efficient data-aggregation for optimizing quality of service using mobile agents in wireless sensor network

<p>Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.</p>

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

This paper deals with energy consumption and security limitations in the reconfigurable WSN’s in order to improve the network lifetime with end-to-end data privacy. The network consists set of nodes placed in distributed environment such that each node performs a reconfiguration task to support users requirements. We proposed novel solutions that improved network lifetime through efficient reconfigurable routing and less network traffic. We proposed an in-network task to eliminate duplicate packets at the node level using hashing distance computation (HDC). Further, we introduced power-efficient reconfigurable cell-by-cell golden sector-based emperor penguin colony (CbC GSEPC) for trust-based routing. In terms of data confidentiality in the energy-constrained environment, a lightweight key expandable cryptography method was proposed for end-to-end confidentiality. Additionally, a reading-based dual validation (RbDV) audits the information at sink level for intrusion detection and isolates suspicion nodes. The proposed and existing works simulated with NS-3.26 and the results show that the proposed work’s average energy consumption is 5.82% lower than the existing 2D-WSN while offering end-to-end confidentiality and node reconfiguration opportunity.

Energy Efficient Data Aggregation and Density-Based Spatial Clustering of Applications with Noise for Activity Monitoring in Wireless Sensor Networks

Energy Efficient Data Aggregation and Density-Based Spatial Clustering of Applications with Noise for Activity Monitoring in Wireless Sensor Networks

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

3S-hierarchical cluster-based energy-efficient data aggregation protocol in wireless sensor network

Energy utilisation is one of the most common challenges in wireless sensor networks, as frequent communication between the sensor nodes results in a huge energy drain. To overcome this challenge, a 3S-hierarchical cluster-based energy-efficient data aggregation protocol in wireless sensor network is proposed, where 3S represents smart sensing, selective cluster head nomination, and data aggregation schedule which reduce the energy consumption for prolonging network lifetime. The proposed technique has three phases. Smart data sensing which uses an unbounded buffer to store data values and reduces transmissions. Cluster selection where high value nodes are treated as extended nodes to become candidate for cluster head selection. An advance aggregation schedule, which alternatively demands sensor nodes to send data buffer based on node identification value. Simulation analysis and the results proved that the proposed algorithm could effectively enhance the network lifetime and reduce energy consumption while maintaining acceptable packet delivery ratios and delay values.

Hybrid and dynamic clustering based data aggregation and routing for wireless sensor networks

In Wireless Sensor Networks (WSNs), effective transmission with acceptable degradation in the power of sensor nodes is a key challenge. In a large network, holdup is bound to occur in communicating superfluous data. The aforementioned issues namely, energy, delay and data redundancy are interdependent on each other and a tradeoff needs to be worked out to improve the overall performance. The extant methods in the literature employ either centralized or distributed approach to select a cluster head (CH). In this paper, sink originated hybrid and dynamic clustering with routing technique is proposed. The proposed routing algorithm works based on node handling capability of each sensor node in the selection of CH and also helps in identifying the forwarder node. In addition, processing load of a sensor node is also considered for selecting the forwarder. Both space and time correlation is used to collect data from the clusters and then aggregated to provide a proficient communication. The introduced method is evaluated with the performance of the previously available techniques like, Data Routing for In-Network Aggregation (DRINA), Efficient Data Collection Aware of Spatio-Temporal Correlation (EAST), Cluster-Based Data Aggregation (CBDA), Energy-Efficient Data Aggregation and Transfer (EEDAT), and Distributed algorithm for Integrated tree Construction and data Aggregation (DICA). Simulation parameters considered for assess ing the performance of the proposed algorithm are aggregation ratio, routing overhead, packet delivery fraction, throughput, packet delay and consumed energy. The experimental analysis of the introduced algorithm generates paramount outcome of finest aggregation quality with diverse key characteristics and circumstances as required by a sensor network.

A review paper on wireless sensor network techniques in Internet of Things (IoT)

In recent times, it has been witnessed that wireless systems based on IoT-based have developed rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. The IoT and WSNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WSN nodes are usually small and battery-driven machines. Thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.