An Energy Efficient Data Transmission in WSN Using Fuzzy‐Based Hybrid Philippine Eagle Tasmanian Devil Optimization

Wireless sensor networks (WSNs) are crucial for several applications. WSN nodes frequently function with constrained battery capacity, rendering energy efficiency a critical issue for clustering and routing. Moreover, a principal challenge of WSNs is ensuring the dependability and security of transmitted data in susceptible contexts to avert hostile node attacks. This study seeks to establish a secure and energy-efficient routing system for fault data prediction to improve the longevity and dependability of WSNs. This paper presents a sophisticated framework for intelligent fault prediction and energy-efficient data transmission in WSN, utilising bio-inspired optimisation and deep learning methodologies. The model initiates data fault prediction with Multi-Term Fourier Graph Neural Networks (MTFGNN), which examine temporal and spatial relationships to detect anomalies and defective nodes prior to clustering. Faultless nodes are subsequently categorised by Fuzzy C-Means (FCM) clustering, facilitating adaptive and efficient cluster creation. Quokka Swarm Optimisation (QSO) is utilised to improve energy efficiency by selecting ideal cluster heads (CH), thereby balancing energy usage and reducing intra-cluster communication expenses. A trust-based routing technique employs Proximal Policy Optimisation (PPO), a reinforcement learning method that dynamically identifies secure and energy-efficient pathways for data transfer, while reducing the influence of unreliable nodes. The experimental results indicate that it surpasses the rival methods across multiple performance parameters. The performance outcomes of quality of service (QoS) metrics are delineated as follows: energy consumption (0.204), throughput (0.701), packet delivery rate (94.24%), network lifetime (1310 rounds), and fault prediction accuracy (99.78%), precision (98.69%), recall (97.52%) and F1 score (97.83).

Wireless Sensor Networking is a sensor information gathering technology that has a wide range of applications in numerous fields. However due to the fact that sensor nodes have limited battery and are deployed in remote and harsh environments, energy efficient and real time transmission of the information are still open challenges. Since in a wireless sensor network data transmission is the most power consuming task, so far most useful techniques for the purpose of energy efficient and real time transmissions are based on data compression, the majority of them are based on wavelet transform. This paper give performance analysis of different wavelets for energy efficient and real time image data transmission in application like environment monitoring using wireless visual sensor networks. The performance evaluation shows that Haar wavelet is better in terms of energy efficiency and transmission time.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

In the networking research community, Wireless Sensor Networks (WSN) has achieved considerable significance in the past years and it can be used in extensive variety of domains. In recent years, the tremendous growth of WSN does not meet all the necessities of environmental monitoring. Wireless Multimedia Sensor Network (WMSN) is required to fulfill the requirements of environment monitoring. In this paper, WMSN is used to monitor the agriculture soil environment. In order to ensure optimal use of the sensor nodes in WMSN and accurate access to the collected data,the routing protocols with both energy efficiency and quality of service ( QoS) properties are necessary for the transmission of photo, video and scalar data.This paper proposes a priority aware energy efficient QoS based data transmission for agriculture soil environment and also detects the pest. It utilizes the available bandwidth and data rate in the best way and in order to be more energy efficient during data transmission.For performance analysis, the proposed system is simulated using Java and the results of the experiments suggest that the proposed algorithm is more efficient compared to other methods in terms of delay and energy consumption.

The shielding from and prediction of harmful activity in the wireless sensor network (WSN) are vital research problems. To solve these problems, an Energy-Aware Routing using a Variational auto-encoder Wasserstein generative adversarial network with a Nomadic People Optimizer (VAWGAN-NPOA-EAR-WSN) is proposed in this paper for secure data transmission in WSN. The proposed Variational auto-encoder Wasserstein generative adversarial network (VAWGAN) selects the Cluster Head (CH) based on multiple objective fitness functions such as delay, distance, energy, cluster density, and traffic rate. After that, energy-aware trust path selection is selected based on parameters such as trust, connectivity, and degree of amenity using the proposed Nomadic People Optimization Algorithm (NPOA). The data are transmitted from the cluster head to the base station and vice versa via a perfect trust path. The proposed VAWGAN-NPOA-EAR-WSN method is done in MATLAB and its efficacy is assessed with performance metrics, such as energy consumption, network lifetime, throughput, etc. compared with existing such as DNN-WSN, EESNN-WSN, and ANN-WSN. Finally, the proposed method provides a lower energy consumption of 2 mJ compared with existing methods.

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. For demonstrating the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.

Driven by the critical importance of routing in Wireless Sensor Networks (WSNs) and the security vulnerabilities present in existing protocols, this research aims to address the key challenges in securing WSNs. Many current routing protocols focus on computational efficiency but fall short of providing strong security measures, leaving them vulnerable to malicious attacks. Reactive protocols, often preferred for their reduced bandwidth usage, heighten security concerns due to their limited resources for maintaining network routes, while proactive alternatives require more resources. Additionally, the ad hoc nature and energy limitations of WSNs make traditional security models, designed for wired and wireless networks, impractical. To overcome these limitations, this paper introduces the Secured Energy-Efficient Opportunistic Routing Scheme for sustainable WSNs. The proposed protocol is designed to enhance security by continuously updating neighbor information and verifying the validity of routing parameters, while also being power-aware, a critical factor given the energy constraints of WSNs. The protocol has been evaluated through simulation experiments, measuring key performance indicators such as throughput, average end-to-end delay (E2 delay), energy consumption, and network lifetime. The results demonstrate that the proposed protocol effectively strengthens WSN security while addressing the unique operational constraints of these networks.

Wireless sensor networks (WSN) has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. Now a days, many machine learning and deep learning algorithms are used for improving the real time data transmissions in WSNs. From the survey on existing methods, it is clear that using machine learning makes the computational methods more reliable, powerful and economical. This paper proposes a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work and confirm the accuracy of the proposed ML-MAC protocol strategy is higher than the existing work.

In this paper, we consider a Wireless Sensor Network (WSN) with Wireless Energy Transfer (WET) capability, and we focus on the data gathering in a multi-hop scenario. In fact, we assume that any wireless sensor that needs the help of relays to deliver the packet may transfer energy as a kind of reward or payment. We assume that only few sensors have direct reliable channel to the sink Base Station (BS), i.e. sensors that are close the BS. Hence, other sensors should relay on them to transmit their packets to the BS. We assume that the nodes of the WSN are selfish and aim to maximize their utility function, which mainly accounts for their energy consumption. We propose a multi-level multi-leader-follower Stackelberg game framework to analyse the competition between users. Particularly, we characterise the Stackelberg equilibrium (SE) for the three-hop WSN, and we propose a distributed algorithm to achieve the SE. Moreover, we show that the network lifetime is enhanced compared to other multi-hop data transmission in WSN.

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.

In this paper we consider usage of sparse random linear codes (RLC) for data transmission in wireless sensor networks, which allow efficient data transmission without need for ARQ procedures or complicated coding schemes, characteristic for algebraic codes. RLC codes are asymptotically optimal and automatically adjust to the channel characteristic. We examine bounds on the sparsity of generator matrix and present results obtained via practical implementation of sparse RLC coding on Iris platform using TinyOS environment.

Establishment of Light Weight Cryptography for Resource Constraint Environment Using FPGA

Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN

Wireless Sensor Networks (WSNs) have emerged as perhaps the most important networking paradigm of recent times, both in terms of their commercial potential and also from a scientific point of view. WSNs are important commercially because of their many applications, spanning from civil to military applications such as structural monitoring of bridges, monitoring of wildlife, tracking of contaminants in the soil and atmosphere. Some of these applications have already proved to be commercially viable and indeed financially very attractive. WSNs are also very important from a scientific point of view, because of their unique features with respect to other types of wireless networks: nodes are typically immobile, and are required to carry a specific type of traffic (which is advantageous), have extreme restrictions on the energy they consume, the processing power they have, and the antennas they can use (which is disadvantageous); in addition, the data created at different sensors may be strongly correlated, which can be beneficial but requires only good algorithms that take advantage of it. Therefore, in the last few years we have experienced an important shift in research activities, moving from general purpose wireless networks to more focused topics specifically on WSNs, taking into account their specific advantages and peculiarities. An important recurring theme in research in this field is the need for energy efficiency, which is the thread of this special issue. The papers which appear in this issue have been carefully selected from the best IEEE PIMRC 2008 conference papers addressing the issue of energy efficiency in WSNs. The respective authors have been asked to provide a significantly extended version of the conference paper, which has subsequently undergone a rigorous review process according to the IJWIN publication standards prior to acceptance. The final set of papers addresses issues at physical, medium access control and networking layers, and to some extent at the application layer. All of these papers take into account the unique peculiarities, advantages, and shortcomings of wireless sensor networks, and propose algorithms and analyses that significantly advance research in the field. We sincerely hope you will enjoy reading them as much as we did. The author of the first paper ‘‘Power Management and Data Rate Maximization in Wireless Energy Harvesting Sensors’’ by Chandra R. Murthy, deals with the problem of power management and throughput maximization for energy neutral operation when using increasingly important but so far rarely analyzed Energy Harvesting Sensors (EHS) to send data over wireless links. The EHS is assumed to be able to harvest energy at a constant rate, and use a fixed part of the energy harvested in a slot for measuring the channel state. The rest of the harvested energy is available for transmission, but it can also be stored in an Wireless Sensor Networks (WSNs) have emerged as perhaps the most important networking paradigm of recent times, both in terms of their commercial potential and also from a scientific point of view. The papers that appear in this issue have been carefully selected from the best IEEE PIMRC 2008 conference papers which addressed the issue of energy efficiency in WSNs. The final set of papers addresses issues at physical, medium access control, networking and to some extend application layers. All of these papers take into account the unique peculiarities, advantages, and shortcomings of wireless sensor networks, and propose algorithms and analyses that significantly advance research in the field. Note that this activity has been partly supported by the NoE-216715 NEWCOM++.