Energy Efficiency Of Sensor Nodes Research Articles

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.

Data Prediction-Based Energy-Efficient Architecture for Industrial IoT

This paper presents an energy-efficient industrial Internet of Things (IIoT) architecture that minimizes the data transmission process based on sensor data prediction. While current IIoT network implementations aim to improve lifetime and reduce maintenance costs, existing data prediction studies have primarily focused on prediction performance, disregarding computing time and energy efficiency. In this paper, we propose a data prediction approach on the base station side to maximize the energy efficiency of sensor nodes. A fast deep learning (DL) model is required to achieve low-latency network communications. Therefore, we exploited a DL-based multi-layer perceptron (MLP) with a deep concatenation method called DC-MLP to ensure data prediction reliability and fast computing time. To demonstrate its robustness, we evaluate the proposed DC-MLP model using six performance metrics with k-fold cross-validation. We varied the sampling rate for data prediction to demonstrate the effectiveness of prediction accuracy and energy efficiency. The performance evaluation results revealed that the proposed architecture successfully reduced energy consumption by up to 33% compared with traditional data transmission while maintaining reliable sensor data and achieving an 81% faster prediction time than existing DL models. Based on these findings, the application of the proposed DC-MLP has the potential to increase the sensor lifetime while satisfying the rigorous requirements of the industrial sector, such as fast prediction times, energy efficiency, and reliable prediction results.

Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.

A Full-Duplex Directional MAC Framework for Underwater Acoustic Sensor Networks

Underwater acoustic communication plays an essential role in underwater wireless information exchange, and nodes with such an ability constitute an underwater acoustic sensor network (UASN). However, UASNs face many challenges, such as high bit error rate and high propagation delay, significantly reducing network performance. Because of the challenging nature of underwater environments, the power consumption and battery energy efficiency of sensor nodes need to be considered. Directional communication technology can conserve energy by restraining beam directivity and diminishing interference between nodes by enabling peer-to-peer communication with intended neighbors. However, the technology suffers from the problems of neighbor discovery, deafness effect, and hidden/exposed terminal. To address these challenges, we propose a full-duplex directional medium access control (MAC) framework based on the vector hydrophone and multimodal transducer in this paper, along with a benchmark MAC protocol, which enables adaptive power control to achieve spatial reuse and energy conservation. Simulation results show that the benchmark MAC protocol outperforms existing representative protocols in terms of throughput, end-to-end delay, and energy efficiency.

Enhanced Communication in Void Region of Underwater Wireless Sensor Network using Vector-Based Void Avoidance Comparing with Void Aware Pressure Routing Protocol

The aim of the proposed work is to improve the energy efficiency of sensor nodes in underwater wireless sensor networks using Vector-based void avoidance (VBVA) in comparison with void-aware pressure routing (VAPR). The performance of vector-based void avoidance uses the vector shift and backpressure mechanisms compared with the void-aware pressure routing protocol. A total of 34 samples are collected and 17 samples for each group is taken to the data sets. The proposed algorithm is tested in an underwater virtual environment by Aquasim in the NS2 simulator. The throughput, energy consumption, and packet delivery ratio are calculated to evaluate the performance of the proposed VBVA Protocol. The energy efficiency of the proposed vector-based void avoidance protocol is improved by avoids of void nodes and forward data packets. The Throughput mean value is (1.3297), Energy consumption mean value is (0.9960) and Packet delivery ratio mean value is (91.1735) achieved using VBVA protocol. This work has obtained significance (<0.01). The simulation results show that reduction of packet loss and improve the energy efficiency of the sensor network using the proposed VBVA algorithm in comparison with VAPR.

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

There is continuous intensive research on image compression techniques in wireless sensor networks (WSNs) in the literature. Some of the image compression techniques in WSNs that exist in the literature include discrete cosine transform (DCT), discrete waveform transforms (DWT), set partitioning in a hierarchical tree (SPIHT), and embedded zero tree wavelet (EZW) coding. Research on image compression in WSNs is necessitated by the need to improve the energy efficiency of sensor nodes and WSNs&#x2019; lifetimes without compromising the quality of the reconstructed data. Several approaches have been developed centered around image compression and other factors in trying to limit the energy consumption of sensor nodes. Most of these approaches do not provide the error-bound mechanism that balances the rate of compression and distortion of the reconstructed image. Therefore, in this paper, a review and analysis of image compression techniques and approaches in WSNs are conducted. Available image compression approaches in WSNs in literature were then classified according to the image compression technique adopted, and their strengths and weaknesses were highlighted. In addition, a rate-distortion balanced data compression algorithm with error bound mechanism based on artificial neural networks (ANN) in the form of an autoencoder (AE) was coded and simulated in MATLAB before being evaluated and compared to the conventional approaches. The experimental results show that the simulated algorithm has less root mean square error (RMSE) and a higher coefficient of determination (<i>R</i>²) values on variable compression ratios as compared to the Principal Component Analysis (PCA), Discrete Cosine Transform, and Fast Fourier Transform (FFT) when using the Grand-St-Bernard metrological dataset. Furthermore, it presented less RMSE, and higher compression ratio values compared to the Lightweight Temporal Compression (LTC) algorithm on variable error bounds when using the LUCE metrological dataset. Therefore, it was found that the simulated algorithm presents better compression fidelity as compared to the conventional approaches without an error-bound mechanism. Moreover, the algorithm analyzed presents a significant approach to balancing the compression ratio and reconstructed data quality through its error-bound mechanism.

EMSNDP: Energy Minimized Optimal Zone Selection of Sensor Network to Build the Data Path for IoTN

Wireless sensor network contains a huge volume of sensors that are capable of observing environmental data and conveying that information to the base station. The zone routing is a way to group sensors into multiple clusters in sensing field. In general, each zone has a single co-ordinator that operates as a zone head and accountable to collect the data from all sensors inside the zone and then forward it to the base station. Clustering and energy efficient path construction to base station is a major issue in sensor networks. This paper concentrates on clustering the sensing field into zones and further splitting it into zone quadrants to maximize the energy efficiency of sensor nodes with minimum delay transmission time. An “Energy Minimized optimal zone selection of Sensor Networks to build the Data Path” protocol is proposed in this paper to minimize the load on zone heads by using the zone coordinators to gather the information from different quadrants of zone. Distance-based communication with the minimum hop is opted to minimize the energy cost. A critical evaluation is carried out on the proposed protocol in the context of total energy consumption of network and average energy consumed per node, lifetime of the Network, delay in reception of packet and packet delivery ratio. The results of our research work and analyzed and benchmarked against similar protocols using Network Simulator 2. The outcome of this research is a reduced delay in packet processing with low energy consumption which significantly increases lifetime of sensor network.

Energy Efficient Optimized LEACH and SEP Routing Protocol for WSN

We all know how Wireless Sensor Network (WSN) is making its way in the modern world and how its application is growing effectively. It has been useful technology which helps to transmit and receive the data. In WSN, all information of physical parameter is sensed and processed by the sensor nodes. Along with the growth of technology in WSN, growth of sensor node is also in progress, it means that size of sensor node is getting reduced. Due to this dimension of the battery of the sensor node is also decreases. Hence Power storage of the battery is also reduced which is a demerit in WSN. But in sensor network replacement of battery is not possible. So we can increase the energy efficiency of sensor node by using LEACH protocol. This protocol helps us to increase life span of network. For heterogeneous network, LEACH produces greater unstable region. To the network stable the paper proposes another protocol is Stable Election protocol (SEP). In this paper LEACH and SEP protocol are tested with MATLAB simulation and comparison of both has done.

Multirate Data Collection Using Mobile Sink in Wireless Sensor Networks

Data collection is one of the most fundamental and important operations in applications of wireless sensor networks (WSNs). Recent research has shown that using mobile sink to collect data brings many advantages to WSNs, such as improving energy efficiency of sensor nodes and prolonging network lifetime. However, the limited speed of mobile sink leads to larger latency, which cause that the data is not fresh or the buffer overflow problem of static sensor nodes. Based on the adaptive rate control, this paper proposes a data collection algorithm which aims to reduce the length of path along which mobile sink collects data from static sensor nodes. The proposed algorithm not only guarantees the completeness of data collection from each static node but also reduces path length by adopting appreciate transmission rate. Through extensive simulations, experimental results show that our approach can notably reduce the path length of mobile sink, while the data of every sensor can be collected completely and efficiently.

Data Transmission Direction Based Routing Algorithm for Improving Network Performance of IoT Systems

With the continuous development of wireless communication technology, the Internet of Things (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts of data with objects, either tangible or intangible, such as sensors or physical devices, connected to the Internet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in various IoT systems, such as monitoring, tracking, and detection systems, to extract relevant information and deliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions. Because sensor nodes have limited resources, such as power and memory, a reduction in the energy efficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance and an increase in packet loss, which affects IoT system performance. Therefore, this study aimed to find an energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery use of sensor nodes to improve the network performance of IoT systems. Conserving energy from sensor nodes and increasing network throughput in WSNs involves having protocols. The low-energy adaptive clustering hierarchy (LEACH ) protocol is a well-known hierarchical routing protocol in WSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmitting data from sensor nodes to the base station (BS) through the cluster head. It is widely adopted in the WSN network field, and many protocols are being studied to improve cluster header selection and data transmission to increase the energy efficiency of sensor nodes. In this study, we attempted to improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when the sensor node is located between the BS and the cluster head, the sensor node transmits data to the cluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in the direction of the BS. Thus, transmission in the opposite direction consumes unnecessary energy and affects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-based LEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiency of transmission energy considering the data transmission direction of sensor nodes. D-LEACH aims to balance the energy of the sensor nodes and improve the performance of WSNs in the IoT system by reducing unnecessary energy consumption caused by reverse transmission considering the data transmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocol of WSNs to improve network performance and describe LEACH. We also explain the D-LEACH protocol proposed in this paper and confirm the performance improvement of WSNs in an IoT system through simulation.

RETRACTED ARTICLE: Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks

Now-a-days, wireless sensor node acts as a basement for a higher level industrial internet of things application. Any kind of real time IoT applications sense the data using already in-built sensors. In real time physical environment, sensors are having minimum amount of power for their operations such as sensing, communicating and processing the data. Many researches are carried out to improve the energy efficiency of sensor nodes and improves their network lifetime. More concentration is needed on clustering and routing part of the communication to go for energy saving. We have proposed an algorithm based on fuzzy and unequal clustering (FBUCA) and a three tier multi-hop optimized routing technique (TM-ORT) to improve the efficiency of the network. Energy consumption and data reliability are stabilized by QOS based multi-hop routing protocols. Whenever clustering plays a main role, load balancing among the clusters has to be important to concentrate. Load balancing has been achieved by choosing best parameters for fuzzy based clustering to select an efficient cluster head for data aggregation and routing. We have done experimental simulations and concluded the results in terms of performance metrics such as standard deviation (SD), First node Die (FND), total energy consumption (TEC) and average energy consumption (AEC).

Query Privacy Preserving for Data Aggregation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and energy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs makes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the abovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce energy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by employing a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and the experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the private data from being compromised.

A Hierarchical Information Acquisition System for AUV Assisted Internet of Underwater Things

As an extension and novel category of the Internet of Things (IoT), the Internet of Underwater Things (IoUT) attracts growing interest in sensing and exploiting ocean. Underwater Sensor Networks (UWSNs) are the existing application to support the concept of IoUT but faced with many challenges in information acquisition as well. To tackle these issues, this paper proposes an autonomous underwater vehicle (AUV) assisted hierarchical information acquisition system composed of a marine stationary sensor layer and an AUV motion layer. Different from the most existing data-gathering schemes that ignored the energy management of underwater sensor nodes and the angle control in AUV operation, this work designs an energy-aware clustering protocol based on the improved K-Means algorithm (ECBIK) to achieve energy efficiency of sensor nodes and proposes a novel Ant Colony (ACO) algorithm integrating with Markov Reward Process (R-ACO) to optimize the distance and angle in AUV path planning. Specifically, in the sensor layer, we first calculate the accurate number of clusters according to the Elbow method, and then introduce a cluster head dynamic conversion mechanism by considering energy load and node survival rate. In the AUV motion layer, we establish a reliable AUV trajectory model to quantify the angle change during its operation. Meanwhile, we apply far-sighted feature of MRP to path optimization. Finally, simulation results validate the performance of our designed algorithms. Compared with the traditional clustering methods of K-Means and LEACH-L, the node survival rates of the proposed ECBIK are increased by 26% and 129.1% respectively. And in the aspect of path planning, the distance and angle under R-ACO are reduced by 4% and 18% respectively compared with ACO.

Energy Efficient Collaborative Beamforming for Reducing Sidelobe in Wireless Sensor Networks

Collaborative beamforming (CB) in wireless sensor networks (WSNs) based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of sensor nodes. However, a VNAA cannot be pre-designed like the conventional antenna arrays due to the randomly deployed sensor nodes, thereby causing a high sidelobe level (SLL) which increases the interferences. In this article, we formulate a hybrid discrete and continuous optimization problem (HDCOP) for reducing the maximum SLL. HDCOP requires to solve both the discrete and the continuous problems simultaneously, and we propose both centralized and consensus-based distributed CB strategies for solving HDCOP. For the centralized strategy, we convert HDCOP into two sub-optimization problems, and propose a discrete cuckoo search (CS) algorithm for the node location selection optimization and a continuous CS algorithm to optimize the excitation current weights of the selected nodes. For the distributed strategy, we propose a parallel distributed CS algorithm to solve the discrete and continuous parts of HDCOP simultaneously. Moreover, we propose two operating mechanisms based on these two algorithms. Simulation results verify the effectiveness of the proposed strategies for reducing the maximum SLL of CB in WSNs. Moreover, the proposed CB strategies have better performance in terms of the energy efficiency compared with other approaches such as the cross-entropy optimization-based method.

Energy efficient service differentiated QoS aware routing in cluster-based wireless sensor network

In today's technological era, WSNs has gained attention worldwide with its miniature size and low cost. Data transmission demands both energy and quality of service (QoS) to ensure efficient use of the sensors and adequate access to collecting information. Lot of research work in literature has been focused on QoS provisioning by differentiated service technique that differentiates and priorities different traffic classes to meet the user requirements. Our work toward scheduling based on packet type providing high priority to emergency data to facilitate reliable transmission of emergency data in critical situations. We proposed an approach for effective sensing by use of stochastic scheduling to increase the energy efficiency of sensor nodes for intracluster communication. The proposed technique in this work outperforms when compared with the existing protocol in the literature in terms of minimised energy consumption, delay and high throughput by offloading of the energy-intensive tasks.

Enhanced Ant Colony System for Reducing Packet Loss in Wireless Sensor Network

Routing packets from source nodes to destination nodes in Wireless Sensor Network (WSN) is complicated due to the heterogeneous nature and distribution of sensor nodes. Packet loss problem in WSN may occur when the sensor node carrying more packets than its capacity. This can affect throughput, energy consumption and success rate of the WSN system. This paper proposes an improved Ant Colony Optimization (ACO) algorithm for packet routing to solve packet loss problem in wireless sensor network. The proposed algorithm is inspired from a variant of ACO which is Ant Colony System (ACS) that consists of local and global pheromone updates to enhance routing path exploration and exploitation. Experimental results showed that the proposed algorithm improved the performance of the proposed ACS algorithm in terms of reducing packet loss and increasing the energy efficiency of sensor nodes.

Delay constraint energy‐efficient routing based on Lagrange relaxation in wireless sensor networks

Most of researches on wireless sensor network (WSN) are focused on how to reduce energy consumption to increase the network lifetime. There are few researches solving the problem of energy optimization ensuring delay constraint. Whereas, the delay is important factor for applications that require delay-sensitive data. Several works have been published to balance the energy consumption and delay. They achieved many different results but each proposal has certain limitations. Some proposals have high computational and messaging complexity. Some others have not found the optimal solution. In this study, we investigate in finding solution to improve the energy efficiency of sensor nodes satisfying end-to-end delay to transmit data from sensor nodes to sink in multi-hop WSNs. Based on Lagrange relaxation method, we propose an aggregate cost function between energy consumption and delay as well as an efficient method to find the optimal multiplier for that objective function. We provide two algorithms to find paths with least energy consumption while maintaining end-to-end delay requirement from any sensor node to sink. Besides analyzing the complexity and convergence of the algorithm, the simulation results also show that the proposed algorithm achieved good balance between energy consumption and delay compared with the previous proposals.

Improvement on LEACH protocol of a wide-area wireless sensor network

This paper proposes to increase the efficiency energy of nodes which rapidly drops during the transmission of the LEACH (Low Energy Adaptive Clustering Hierarchy), using the method of the dual-hop layered in the sensor field. By introducing dual-hop method in the data transmission, the proposed single-hop method for short-range transmission and multi-hop transmission method between the cluster heads for remote transmission was introduce. By introducing a partial multi-hop method in the data transmission, a single-hop method for short range transmission method between the cluster heads for remote transmission was introduces. In the proposed DL-LEACH, the energy consumption of cluster head for remote transmission reduces and increases the energy efficiency of sensor node by reducing the transmission distance and simplifying the transmission routine for short-range transmission. As compared the general LEACH, it was adapted to a wider sensor field.

Dual-hop Routing Protocol for Improvement of Energy Consumption in Layered WSN Sensor Field

This paper proposes to increase the node energy efficiency, which rapidly drops during the transmission of L-TEEN (Layered Threshold sensitive Energy Efficient sensor Network protocol), using the method of DL-TEEN (Dual-hop Layered TEEN). By introducing dual-hop method in the data transmission, the proposed single-hop method for short-range transmission and multi-hop transmission method between the cluster heads for remote transmission was introduce. By introducing a partial multi-hop method in the data transmission, a single-hop method for short range transmission method between the cluster heads for remote transmission was introduces. In the proposed DL-TEEN, the energy consumption of cluster head for remote transmission reduces and increases the energy efficiency of sensor node by reducing the transmission distance and simplifying the transmission routine for short-range transmission. As compared the general L-TEEN, it was adapted to a wider sensor field.

Dynamic Reliable Location Aware Routing Protocols for Mobile Wireless Sensor Network

Energy efficiency, network lifetime, data transmission, endto-end delay, reliable routing protocols for mobility centric and mobility of Cluster Head are the major parameters used for wireless sensor network (WSN), and are maintained by various of protocols. Existing cluster-based mobile routing protocols such as LEACH, LEACH-Mobile, LEACH-Mobile Enhanced, CBR-Mobile, LFCP-MWSN consider energy efficiency of sensor node. These protocols allocate the extra timeslots using time division multiple access (TDMA) scheme to accommodate nodes that enter a cluster because of mobility and thus increase the end-to-end delay. In this study a new ILFCP-MWSN protocol is proposed based on mobility of the cluster head. The results revealed that ILFCP-MWSN protocol is more efficient in terms of Energy Consumptions, Remaining Energy and Approximate Number of Data Transmissions than those of the existing LEACH-M, LEACHME, LFCP-MWSN protocols.