Wireless Sensor Network Nodes Research Articles

Reachability problems in interval-constrained and cardinality-constrained graphs

In this paper, we present two new graph structures that can be used to model information routing in heterogeneous communication networks. These are cardinality-constrained graphs (CCGs) and interval-constrained graphs (ICGs). In both cardinality- and interval-constrained graphs, the nodes in the graph are labeled. In a CCG, each label is associated with an upper bound. A path in a CCG is considered valid, if the number of nodes on the path with any particular label is at most the upper bound associated with that label. In an ICG, each label is associated with both an upper bound and a lower bound. A path in an ICG is considered valid, if the number of nodes on the path with any particular label is between the upper and lower bounds associated with that label. In this paper, we examine the computational complexities of finding valid paths in both CCGs and ICGs. In particular, we show that these problems are NP-hard. Additionally, we propose novel algorithms for finding paths in CCGs and a subset of ICGs. We also demonstrate the effectiveness of our algorithms on CCGs and ICGs of various sizes. The past decade has seen a surge in the degree of hardware-heterogeneous device interconnectivity, Additionally, there has been a proliferation of resource-constrained wireless sensor networks. Thus, the problems of finding valid paths in CCGs and ICGs are of interest. Nodes in wireless sensor networks can be faulty, vulnerable, energy-constrained, or untrusted. These nodes may also have differing degrees of performance or power efficiency. This can pose a significant challenge in routing the flow of information between nodes. The heterogeneity of nodes in such networks is captured through the use of CCGs and ICGs.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

With the increasing availability of high-resolution satellite and drone images and the Internet of Things (IoT) has begun transforming remote sensing of agriculture by improving accessibility and frequency of updates. Modern IoT-based smart agriculture systems use Wireless Sensor Networks (WSNs) to gather information from an ecosystem that regulates the quantity of water in agricultural fields could be one of these activities. The WSNs remained a challenge to transfer data to drones for analysis purposes. These are composed of tiny sensory architectures organized together to bring efficiency and scalability features to a network. WSN nodes are controlled and managed by a cluster. It is quite difficult to design an efficient leader election protocol. The computation power, storage space, and energy supply of sensor nodes make them unable to frequently switch to a different cluster. The WSN cluster-head election process requires a lot of energy (evaluation and computational process to select the most appropriate node with the least impact on network fragmentation in energy consumption of selected node). Then it is necessary to formulate a mechanism where WSNs utilize the least energy to coordinate with the remote sensing sources. This study presents a cluster election algorithm using the fuzzy logic inference system. It uses a coordinates system to map network nodes and map them based on prioritized scheduling. Lifetime augmentation in wireless sensor networks has always been of great interest. During data transmission from normal sensor nodes to the base station (sink), excess energy is dissipated. Optimizing the energy dissipation of WSNs through the selection of cluster heads is a powerful way to increase the lifespan. By electing more efficient nodes as cluster heads, the proposed method extends the network's lifetime by reducing the number of unimportant communications between nodes. With the utilization of network resources efficiently, the network's lifetime is extended. The proposed algorithm is evaluated with the LEACH (Low Energy Adaptive Clustering Structure) algorithm and FCA method based on the remaining energy and the number of active nodes. The simulation results show that the proposed algorithm utilizes less energy for communication with remote sensory equipment for intelligent agriculture. The performance of the method improved for remaining energy by 9%, the number of active nodes rate by 24%, and indirectly network resource utilization than other states of the art solutions.

Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks.

The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communication strategies between groups for different types of optimization problems. To verify the optimized effect of the proposed OPGTO algorithm, it is tested on the CEC2013 benchmark function set and compared with Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and Artificial Gorilla Troops Optimizer (GTO). Experimental studies show that OPGTO has good optimization ability, especially on complex multimodal functions and combinatorial functions. Finally, we apply OPGTO algorithm to 3D localization of wireless sensor networks in the real terrain. Experimental results proved that OPGTO can effectively reduce the localization error based on Time Difference of Arrival (TDOA).

Tape-Like Vibrational Energy Harvesters Using Flexible Fluorinated Polyethylene Propylene (FEP) Piezoelectret as Transduction Material

Energy harvesting is a promising strategy utilizing renewable energy to power low-power-consumption electronics, such as wireless sensor network (WSN) nodes and wearable electronics. In this article, a tape-like vibrational energy harvester, using flexible fluorinated polyethylene propylene (FEP) piezoelectret films as transduction material, is reported. Due to a specific air-filled parallel tunnel structure, the FEP piezoelectret films in this study exhibit both significant longitudinal and transverse piezoelectric effects and could be made into tape-like flexible transduction devices. The results show that the experimental data are consistent with the theoretical prediction and the finite-element method (FEM) calculation. For a device with an effective area of 18 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times25$ </tex-math></inline-formula> mm and a seismic mass of 0.195 g on one edge, the output power at a resonant frequency of 16 Hz amounts to 3.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> for an acceleration of 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${g}$ </tex-math></inline-formula> . Owing to the tape-like and flexible transduction elements, the power generated by the device can be enhanced readily by either enlarging area of the transduction elements or multiplying a few transduction elements in series or parallel to meet the power requirement of electronic devices, which makes the practical application flexible.

An Improved Stacked Auto Encoder based Data Compression Technique for WSNs

Abstract: Because of the limited energy available to sensor nodes in wireless sensor networks (WSNs), data compression is critical in these networks. The majority of the time, data communication results in energy consumption; however, by minimizing data transmission and reception, the lifetime of sensor nodes may usually be extended significantly. To compress sensor data, we present a new Improved Stacked RBM Auto-Encoder model, which is built of two layers: an encode layer and a decode layer, which is described in detail in this work. Data from sensors is compressed and decompressed in the encode layer; data from sensors is reconstructed and compressed in the decode layer. The encode layer and the decode layer are both made up of four conventional Restricted Boltzmann Machines that are used throughout the system (RBMs). We also present an energy optimization strategy that, by trimming the parameters of the model, can further minimize the energy consumption of the model storage and calculation. We evaluate the model's performance by comparing it to the data acquired by Intel Lab in the environment. Assuming that the model's compression ratio is 10, the average Percentage RMS Difference value is 9.84 percent, and the average temperature reconstruction error value is 0.312 degrees Celsius. It is possible to minimize the energy consumption of node communication in WSNs by 92 percent. When compared to the traditional method, the proposed model achieves higher compression efficiency and reconstruction accuracy while maintaining the same compression ratio as the old method. The results of our experiments demonstrate that the new neural network model can not only be applied to data compression for WSNs, but it also has high compression efficiency and an excellent transfer learning capability. Keywords: Data Compression; Stacked-Autocoder; transfer learning; energy, consumption optimization

Node Replication Attack Detection in Distributed Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.

Connectivity Analysis of WSN Nodes using Neighborhood Search Technique (WSNNST)

A new approach to wireless sensor network (WSN) communication through multi-hop routing is investigated in this work. The proposed approach utilizes search radius expansion in order to find highest energy nodes within a WSN network and preserves the energy of the lowest energy nodes. This approach which is built on trust in terms of providing good quality communication channels between a source node and a destination node considers energy as one parameter of trust that will enable strong, reliable communication channels and shorter routes to be used in a WSN. This approach enables efficient energy consumption during data communication, but with higher level energy consumption due to expanded search radius, which can be minimized using search time limit. The work also relates through mathematical equations, number of discovered routes, maximum hops, and search time to the considered search radius. The overall objective of this work is to enable faster and efficient channel communication between WSN nodes, through gradual cost effective search in neighborhoods. This is achieved by uncovering shorter and less number of routes within an acceptable time limits. The proposed and simulated approach presented in this work differ in terms of the gradual expansion of search and the equal increments in search radius compared to other used techniques. The presented work also adds a unique feature of allowing re-charging of low energy nodes using the already established routes connecting high energy nodes through the registration of weak nodes locations. A final general expression relates search radius to other WSN variables is also presented.

Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.

Intelligent Supply Chain and Logistics Route Optimization Algorithm in Wireless Sensor Network.

This article first proposes the design of the ZigBee wireless sensor network node based on Arduino, from the design and implementation of the node hardware system to the design and implementation of the node software system, and expands on the design details. The node function was applied to the node design environment, and simulation tests were performed, and execution was tested to confirm the feasibility and practicability of the design. Based on this, the article proposes the service construction process of the software and hardware platform of the smart supply chain system, and then clarifies the basic mathematical problem of the distribution plan in line with the service strategy, that is, “the logistics route optimization algorithm with capacity and time constraint optimization,” which aims to study statically design, define, and model, and test two precise solutions representing heuristic genetic algorithms and analyze their results. In addition, additional hardware such as smart supply chain and cabinet boards are tested and designed according to system requirements, and the interaction mechanism is adapted to the user interface and supporting structure design so as to ensure the completion of user-related business processes. Combining the existing theoretical knowledge, the first priority is to meet customer needs, build a smart supply chain model under the premise of on-time delivery, and solve the model in time. Based on this point, this article tries to use hybrid branch algorithm link and tabu search algorithm to increase the consideration of traffic conditions, collect accurate algorithms, and modern heuristic algorithms to solve the actual situation of the case. At the end, the article proposes some related issues for the above problems. Suggesting through the research of wireless sensor network, this paper applies it to the research of intelligent supply chain and logistics route optimization algorithm and promotes the development of more intelligent supply chain in the future.

Application of Artificial Intelligence System Based on Wireless Sensor Network in Enterprise Management.

With the improvement of the ability to acquire natural information, wireless sensor networks also need to transmit corresponding information in terms of collecting information. Wireless sensor nodes have great application prospects as a key component of wireless sensors. Therefore, different wireless sensors play an important decisive role in the operation of wireless network applications. With the continuous development of wireless sensor networks, existing wireless sensor network nodes exhibit limitations and shortcomings such as inflexible structure, low variability, and low versatility. Specifically, the learning and neural networks obtained by different artificial intelligence expert systems in computing technology are different. On the one hand, it can meet the needs of users for information systems to a certain extent, and on the other hand, it can also help accelerate the development of computer science. At present, the new generation of information technology industry is listed in the seven emerging strategic industries of the country. The new cloud computing technology has gradually expanded to important corporate governance capabilities in terms of information technology. The intelligent application of cloud computing technology replaces traditional enterprise management technology. Efficiency management and risk management can improve the quality and business capabilities of the entire enterprise, improve system applications according to the actual situation of the enterprise, improve system applications, and implement health and the sustainable development of the enterprise, thereby promoting the sustainable development of the computer technology industry.

Interference-Resistant Communication framework for Sensor Nodes in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are becoming increasingly important for various aspects of modern communication. However, the lack of reliable and securecommunication between nodes of the WSN often hinders the realization of many of its potential applications. In order to overcome these issues, we propose an interference-resistant Communication Framework for Sensor Nodes in Wireless Sensor Networks (IRCF-WSN) which is based on the idea of combining different types of compatible transmission schemes to establish a secure and reliable communication link between nodes. The proposed framework aims to provide a secure and reliable connection between nodes of the WSN irrespective of the interference environment. In order to accomplish this goal, the framework combines several interference-resistant techniques like frequency hopping, spread spectrum, and beamforming to form multiple channels of connectivity whichare subsequently used for communication. Additionally, the framework also utilizes a novel routing algorithm to ensure that the most secure and reliable path is chosen for the communication. Simulation results indicate that using the proposed framework, the communication performance of the WSN can be improved significantly compared to existing solutions.

Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks

Wireless Sensor Networks (WSN) remain a hot research topic due to its wide range of application areas like environmental monitoring, military surveillance, healthcare, and disaster management. Network survivability is one of the challenging issues in WSN that needs to be maximized for long-run operation of the network. It can be proficiently improved by effective utilization of available energy since the nodes in WSN operate only via in-built batteries. The studies conducted earlier have mainly focused on the design of clustering and routing protocols to reduce energy consumption in the network. With this motivation, the current research paper presents a novel Energy-Aware Fault Tolerant Clustering with Routing for Improved Survivability (EAFTC-RIS) technique in WSN. The proposed EAFTC-RIS technique intends to select the Cluster Heads (CHs) and optimal routes to the destination in an optimal manner with fault tolerant mechanism. The proposed EAFTC-RIS technique primarily designs Moth Flame Optimization (MFO)-based clustering scheme for CH selection and cluster construction. Also, fault tolerant mechanism is considered to further enhance the survivability of the network. In addition, Social Spider Optimization (SSO)-based routing approach is also employed for optimal selection of routes in WSN. Clustering, routing, and fault tolerant mechanism designs, with multiple input variables, result in enhanced network survivability. The proposed EAFTC-RIS technique was validated for its performance and the outcomes were inspected under various aspects. The simulation outcomes demonstrate the superiority of EAFTC-RIS technique over recent approaches namely, EAFTC-RIS, FUCCAR-GWSO, FBECS, MLSLEEP, and Bee Cluster.

Energy Harvesting in Immersed Tunnel for Powering Wireless Sensor Nodes for Corrosion Monitoring

Collecting data from corrosion sensors in reinforced concrete structures demands intensive labor work, therefore measurements are only performed occasionally. A low-cost Wireless Sensor Network/Node (WSN) will eliminate the need for manual work and allow for continuous measurements if needed. For a distributed WSN over a large area, a concerning issue is how to power such devices. This paper, therefore, investigates the use of a reinforced concrete battery for powering WSNs. Results obtained from a real-world immersed tunnel, which has hundreds of electrochemical based corrosion sensors embedded into the reinforced concrete structure, shows that sufficient energy can be harvested from the sensors, to perform wireless transmission of corrosion data to a base-station. With an open circuit potential of 975 mV, the maximum harvested output power is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12.4~\mu \text{W}$ </tex-math></inline-formula> , while the minimum measured output power is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1~\mu \text{W}$ </tex-math></inline-formula> . The results also show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.82~\mu \text{W}$ </tex-math></inline-formula> , with an open circuit potential of 727 mV, can be directly harvested from the reinforcement steel itself. Due to the fact that the steel volume can work as a large battery anode, a longer lifetime for the energy source is expected. Furthermore, we show that the harvested energy from the corrosion sensors will induce a galvanic corrosion rate of only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.7~\mu \text{m}$ </tex-math></inline-formula> /year, when transmitting the corrosion data once a month. This indicates that there is no risk to the structural integrity when using this energy source.

FL-Sleep: Temperature adaptive multi-attribute sleep-scheduling algorithm using hesitant fuzzy logic for Wireless Sensor Networks

The sustainable operation of sensor nodes in Wireless Sensor Network depends on the nodes’ adaptability with the environment. A sensor node strives to live longer using periodic sleep/awake activity. But it fails to achieve considerable success due to the node’s inability to make the sleep/awake strategy adaptive to the environment. To this end, we propose an algorithm, ‘FL-Sleep’ which makes every node in the network to observe the ambient temperature and status of their parameters after every round of operation. Depending on their perception of the parameters, the nodes execute a sleep-scheduling strategy in the subsequent round. It makes the node evaluate its current state and decide the required action (’Active’, ‘Listen’ or ‘Sleep’) to perform. A node working in a favorable condition would decide the action with an optimistic attitude towards the parameters. In contrast, a critical condition of a node compels it to decide pessimistically. This qualitative measurement provides a precise understanding of the environment. ‘FL-Sleep’ works on hesitant fuzzy logic-based Multi-Criteria Decision Making method and is found to improve the network’s lifetime by 247.11% compared to BMAC, by 68.56% compared to SOPC, and by 77.2% compared to RL-Sleep. The best lifetime of nodes is obtained when the network is organized in spiral topology. ‘FL-Sleep’ shows better performance in terms of packet-delivery-ratio, energy efficiency, and the number of active nodes in the network compared to BMAC, SOPC, and RL-Sleep.

An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for Wireless Sensor Networks

In wireless sensor networks (WSNs), energy efficiency is a significant design challenge that can be resolved by clustering and routing approaches. They are considered as Non-deterministic Polynomial (NP)-hard optimization problems, and the optimal or near-optimal solutions can be determined by using Swarm-Intelligence (SI) based algorithms. With this inspiration, this study focuses on two approaches, viz. the Hybrid Butterfly and Ant Colony optimization algorithm along with Static sink node (HBACS) and HBAC along with Mobile sink node (HBACM), which is a hybridization of Butterfly Optimization (BOA) and Ant Colony Optimization (ACO) algorithm. BOA determines the optimal cluster head, and ACO performs energy-efficient routing, thereby minimizing the energy consumption and maximizing the network’s lifetime. Furthermore, in this study, mobility of the sink node is used to eliminate the multi-hop communication between cluster heads and sink nodes, hence addressing the hot-spot issue and further extending the network lifetime. The proposed HBACS and HBACM approaches are implemented in the NS2 simulator. The simulation findings reveal that the HBACS shows percentage improvement regarding residual energy by 24.23%, 41.98%, and 66.67%; an improved number of alive nodes by 28.19%, 37.81%, and 53.12%; and improved throughput by 8.11%, 14.29%, and 17.65% over CRWO, ERP, and IHSBEER algorithms respectively. Moreover, the HBACM approach performs better in LDN by 18.76%, 63.66%, and 66.28%; HDN by 8.35%, 56.26%, and 58.15%, and FDN by 7.77%, 52.76%, and 74.29% over HGWSFO, SFO, and GWO based approaches, respectively.

Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks

Wireless networking is popular due to the “3 any” concept: anyone, anytime, anywhere. Wireless communication technology advancements have covered the opportunities for sustainable development of low-power, low-cost, multipurpose sensor nodes in wireless sensor networks. In sensor networks, the network layer handles routing problems. Since radio transmission requires a significant amount of energy, it is essential to investigate power efficiency and optimization. As a result, the conservation of energy is a critical concern in wireless sensor networks. Recent research is focused on developing routing algorithms that use less amount of energy during communication, thereby prolonging the network’s life. Wireless sensor networks with energy recovery nodes use nodes that can extract energy from their environment. The fuzzy-GWO method and the energy-saving routing algorithm are proposed and analyzed in this research work. For simulation, the MATLAB 2021b working environment is used. The LEACH, HEED, MBC, FRLDG protocols, along with the proposed protocol F-GWO, are compared. From the obtained results, it is found that the network lifetime is increased by 20%, 14.8%, 12.5%, and 3.8%, respectively. In addition, the proposed method has a 37.5%, 33.3%, 16.6%, and 6.25% reduction in average energy consumption when compared with the conventional algorithms. According to the experimental data obtained through simulation, the proposed F-GWO algorithm outperforms the LEACH, HEED, MBC, and FRLDG in network lifetime, packet delivery ratio, throughput, bit error rate (BER), buffer occupancy, time analysis, and end-to-end delay.

Optimization of a vibrating MEMS electromagnetic energy harvester using simulations

Nowadays, wireless sensor networks (WSN) are becoming essential in our daily life. However, a major constraint concerns the energy power supply. Indeed, batteries need to be recharged or replaced often which implies a limited lifetime for WSN nodes. One alternative consists in harvesting mechanical energy from surrounding vibrations of the environment. Using finite element simulations, we report here a complete guideline to optimize a MEMS electromagnetic energy harvester consisting of an in-plane vibrating silicon frame supporting an array of micromagnets that faces a static 2D micro-coil. The dimensioning of the magnet array and the specific design of the coil are addressed, considering patterned 50 \(\upmu \)m thick NdFeB films with out of plane magnetization. The optimization of the electromechanical coupling which allows to efficiently convert the energy results from a trade-off between the high magnetic flux gradients produced by the micromagnets and the maximum number of turns of the facing coil.

A Majorization-Minimization Algorithm for Hybrid TOA-RSS Based Localization in NLOS Environment

This letter investigates the problem of accurate localization of a target node in wireless sensor networks using time of arrival (TOA) and received signal strength (RSS) measurements in an adverse non-line of sight (NLOS) environment. The proposed methodology works without any requirement of the NLOS path identification or the prior knowledge of NLOS bias distribution. A non-linear weighted least squares (NLWLS) problem is formulated through general approximations on the hybrid data model. The formulated NLWLS problem is solved using a computationally efficient majorization-minimization (MM) algorithm in which the NLWLS objective is iteratively minimized via simple update steps. The proposed MM algorithm is guaranteed to converge to a stationary point of the NLWLS objective. Simulation results and computational complexity analysis validate that the proposed MM algorithm attains fast convergence with lower latency. Moreover, the proposed hybrid localization algorithm outperforms the state-of-art methods in terms of estimation accuracy and computational complexity.

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.

A novel hybrid range-free approach to locate sensor nodes in 3D WSN using GWO-FA algorithm

The precise node location of the sensor nodes is an essential requirement in wireless sensor networks (WSNs) to determine the place or event occurring at a particular instant of time. In WSN, existing localization schemes consider two-dimensional (2D) space, while in actual life, sensor nodes are placed in three-dimensional (3D) space. In 3D localization, there are many research challenges, such as higher computational complexity, poor location prediction, lesser coverage, and depending only on fewer anchor nodes. To address various research issues in a 3D environment we propose a range-free technique applied in an anisotropic scenario having degree of irregularity (DOI) as 0.01 using the concepts of a fuzzy logic system (FLS). Anisotropic properties of nodes are considered to determine the efficiency of Grey wolf with the Firefly algorithm. In our proposed scenario, the received signal strength (RSS) information is necessary among the target nodes and their corresponding anchor nodes for determining the location of target nodes using the information based on edge weights. These edge weights are further modeled using Hybrid Grey Wolf Optimization with Firefly Algorithm (GWO-FA) to estimate the location of target nodes. The proposed algorithm is energy efficient as a single location-aware node is used for localization. Further, the concept of virtual anchors is introduced that helps the algorithm to determine 3D positions.