Wireless Sensor Network Applications Research Articles

Clock synchronization based on pulse with propagation delay eliminated in wireless sensor networks

AbstractClock synchronization is indispensable for numerous applications of wireless sensor networks (WSNs). When no common reference clock is available, the nodes must employ distributed synchronization techniques. This paper proposes, a distributed pulse‐based clock synchronisation approach, wherein the propagation delay is eliminated through signal ping‐pongs between neighbouring nodes. Such an approach can jointly estimate the clock skew and offset without requiring any reference clock. The whole synchronization process is completed at the physical (PHY) layer, effectively avoiding the random delay caused by packet queuing and retransmission. Simulation results show that the proposed approach can achieve higher synchronization accuracy compared with other existing methods.

Secure and efficient blockchain-based consensus scheme for MWSNs with clustered architecture

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.

Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.

A novel and distributed three phase consensus based secured data sharing in internet of things environment

The most essential part of any internet of things (IoT) model is the wireless sensor networks (WSN); latest technologies are combined with the applications of WSN results in fast, efficient, flexible as well as economical models. These networks are highly prone to attacks considering their characteristic nature, which includes self-organization, a topology that is dynamic, large-scale and constrained on the resources. Various models have been proposed for the detection of attacks in these wireless sensor networks. This research work proposes three-phase consensus based secured data sharing (TCSDS) in IoT environment. TCSDS adopts the consensus-based protocol for designing the security model in this research. Furthermore, TCSDS comprises three distinctive phases, each phase consisting of novel algorithm. First phase includes the setting up threshold value for sensor nodes. Second phase includes the efficient data packet transmission and third phase includes the efficient and secure routing, which tends to discard the unsecured nodes. TCSDS is evaluated considering the different parameter like energy consumption, malicious packet detection and throughput. Further comparison with the existing model is carried out based on the classified and misclassified packet; through the comparative analysis, it is observed that the TCSDS approach simply outperforms the existing model.

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

SummaryA wireless sensor network (WSN) is a network of tiny sensors deployed to collect data. These sensors are powered with batteries that have limited power. Recharging and/or replacement of these batteries, however, are not always feasible. Over the past few years, WSN applications are being deployed in diverse fields such as military, manufacturing, healthcare, agriculture, and so on. With the ever‐increasing applications of WSNs, improving the energy efficiency of the WSNs still remains to be a challenge. Applying fuzzy logic to the problem of clustering exploits the uncertainty associated with the factors that affect the lifetime of these sensors and enables the development of models that would improve their performance in real‐world applications. We present a comprehensive review of various fuzzy‐based techniques for clustering in WSNs whose main goal is to optimize energy usage in WSNs while simultaneously improving their overall performance.

Review study about Hierarchical Routing Protocol in wireless sensor networks

Wireless Sensor Networks (WSNs) are crucial in various applications, including smart homes, military surveillance, health monitoring, and environmental sensing. These networks consist of distributed sensor nodes that collect and transmit environmental data to a central location for processing. However, WSNs face challenges such as limited energy resources, scalability, complexity, security, fault tolerance, and quality of service. Researchers have proposed different routing protocols tailored to WSN characteristics to address these issues. This article comprehensively overviews WSN design issues, focusing on routing protocol classification and comparative analysis. Among the hierarchical protocols, LEACH (Low-Energy Adaptive Clustering Hierarchy) is highlighted as a prominent clustering-based protocol that reduces energy consumption by forming clusters and utilizing cluster heads for data aggregation. Each addresses specific challenges and enhances network performance. The comparative analysis evaluates the protocols based on energy efficiency, network lifetime, data delivery, scalability, and security. The findings help researchers, practitioners, and network designers select appropriate routing protocols for their specific WSN applications. This article offers valuable insights into WSN design issues and presents a comprehensive classification and comparison of routing protocols. By understanding the strengths and limitations of different protocols.

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN.

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

Enhancing Security in Wireless Sensor Networks With Key Management Techniques

In several fields, including environmental monitoring and healthcare, wireless sensor networks (WSNs) have emerged as a vital technology. Security is a critical concern for WSNs, as these networks are often deployed in hostile environments where attackers may attemptto compromise the integrity and confidentiality of data. Key management is a key component of WSN security because it is employed to create and maintain secure channels for communication between nodes. This paper provides an overview of key management techniques for WSNs, including symmetric and asymmetric key distribution, hash-based key distribution, and threshold cryptography. We also discuss the strengths and weaknesses of each technique, as well as their suitability for different types of WSN applications. Finally, we identify key research challenges in the field of WSNs' key management, including the development of lightweight and energy-efficient key management protocols and the integration of key management with other security mechanisms such as intrusion detection and secure routing.

Edge Computing and Sensor-Cloud: Overview, Solutions, and Directions

Sensor-cloud originates from extensive recent applications of wireless sensor networks and cloud computing. To draw a roadmap of the current research activities of the sensor-cloud community, we first investigate the state-of-the-art sensor-cloud literature reviews published since the late 2010s and discovered that these surveys have primarily studied the sensor-cloud in specific aspects, security-enabled solutions, efficient management mechanisms, and architectural challenges. While the existing surveys have reviewed the sensor-cloud from various perspectives, they are inadequate for the three key issues that require urgent attention in the sensor-cloud: reliability , energy , and heterogeneity . To fill this gap, we perform a thorough survey by examining the origins of the sensor-cloud and providing an in-depth and comprehensive discussion of these three key challenges. We summarize initial designs of the new edge-based schemes to address these challenges and identify several open issues and promising future research directions.

A compact complementary split ring resonator (CSRR) based perfect metamaterial absorber for energy harvesting applications

A novel Compact Complementary Split Ring Resonator (CSRR) metamaterial unit cell with perfect absorption, beneficial for ambient power harvesting, is proposed to operate in the S- and C-band. The size of the structure is 15 × 15 × 1.575 mm3 with dimensions of 0.18λ0 × 0.18λ0 × 0.019λ0 at the lowest absorption frequency. The proposed compact-sized CSRR structure exhibits notable characteristics such as high absorption coefficients and polarization-insensitive behaviours across a wide range of angles. The results demonstrate a notably high absorption efficiency of 97%, 99%, and 98% at 3.7275 GHz, 4.7075 GHz, and 5.9325 GHz. A 3 × 3 array is designed and verified by experimental results. The proposed design exhibits superior performance compared to previously published designs, which suffer from significant limitations such as a large metamaterial structure, reduced absorption frequency bands, and a lower fraction of absorption bands. The proposed design exhibits potential for utilization in developing wireless sensor network applications that require compact and highly efficient harvesting systems.

Distributed Node Deployment Algorithms in Mobile Wireless Sensor Networks: Survey and Challenges

From the coverage and connectivity perspective, a wide range of applications in wireless sensor networks rely on node deployment algorithms to accomplish their functionalities. This is especially of great significance when the sensor nodes are mobile and can autonomously move to appropriate locations to provide optimal or near-optimal coverage. In this article, we review the state of the art of deployment algorithms proposed for blanket coverage in mobile wireless sensor networks. To this, we first discuss the node deployment challenges and their impact on network performance from different perspectives. Afterwards, currently available deployment algorithms in mobile sensor networks are categorised into two main categories: Force-based, and Geometrical deployment algorithms. The advantages and disadvantages of each category are discussed, and the latest advancements are then presented. Finally, we present a qualitative comparison of all the algorithms reported in the literature and also discuss some possible future directions.

ECKN: An Integrated Approach for Position Estimation, Packet Routing, and Sleep Scheduling in Wireless Sensor Networks.

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead.

A Wireless Sensor Network (WSN) Application for Smart Monitoring and Control System for Drought

The Philippine government persistently grapples with the challenges of low crop yield caused by drought, requiring efficient water management strategies. This becomes increasingly critical given the rising population and decreasing food supply. This study introduces an innovative precision agriculture system powered by the Internet of Things (IoT), designed to address this issue. This IoT-enabled system harnesses wireless technology, connecting environmental and soil sensors via a Wireless Sensor Network (WSN). These sensors provide real-time data on farmland stress conditions, enabling farmers to respond swiftly with appropriate interventions. The gathered data are stored in both local and remote database systems and relayed via WiFi to an automated device—a programmable circuit attached to a magnetic latch solenoid—to control field irrigation. The system was piloted in three 12x16 m2 plots in San Ramon, Zamboanga City, employing 24 sensor and automation boxes strategically deployed across the irrigation network. Each box, protected by a robust, 3D-printed ABS plastic casing, safeguards the intricate circuitry from harsh environmental conditions. MQTT and HTTP protocols facilitate the delivery of information to a dedicated Android Application, ensuring remote access. The data is further analyzed using a Neural Network model, optimizing the irrigation schedule intelligently. Our findings validate the efficacy of this smart farming system as a scalable and efficient solution for precision agriculture.

Approximation algorithms for vehicle-aided periodic data collection from mobile sensors with obstacle avoidance in WSNs

Data collection from the sensors in time is an integral part of many applications of wireless sensor networks (WSNs). Vehicles, referred to as Mobile Sinks (SNKs), may be used to collect data from the sensors by visiting them. Since the sensors have limited memory, the sensed data needs to be collected by the SNKs within a predefined time interval to avoid memory overflow. Periodic data collection by SNKs becomes even more challenging when the sensors are mobile. Moreover, the presence of obstacles in the area makes the path planning for SNKs even more complicated. In this paper, an optimization problem, referred to as Minimum Mobile Sink Aided Periodic Data Collection (MinSnkDC) problem, is formulated, where the objective is to determine the minimum number of SNKs that collect data for every time period from the mobile sensors in the WSN, while avoiding collision with the obstacles in the area. The problem is proved to be NP-complete. Two constant factor approximation algorithms, namely MinSnkDC and Modified MinSnkDC (M-MinSnkDC), are proposed to solve the problem. From the simulation results, it is evident that M-MinSnkDC can produce a better solution compared to the existing obstacle-aware SNK-based data collection algorithms, while using a small number of SNKs.

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

In this current era of communications and networking, The Internet of things plays the main role in the making of smart communication and networking. In this article, we have focused on the literature survey on wireless sensor networks which are energy efficient. Various standard protocols are reviewed along with some enhanced protocols which makes the network energy efficient. The comparison of the standard and enhanced protocols with respect to various applications in wireless sensor networks is thoroughly done in this article. The outcomes of the enhanced protocols are also briefly discussed. For easier analysis to future researchers, a comparative table which lists the enhanced protocols which are compared with standard counterparts along with the factors for energy efficiency of the protocols. This article also comments on the issues and challenges of the protocols which can be further analyzed for making the wireless sensor network more energy efficient.

EASND: Energy Adaptive Secure Neighbour Discovery Scheme for Wireless Sensor Networks

Wireless Sensor Network (WSN) is defined as a distributed system of networking, which is enabled with set of resource constrained sensors, thus attempt to providing a large set of capabilities and connectivity interferences. After deployment nodes in the network must automatically affected heterogeneity of framework and design framework steps, including obtaining knowledge of neighbor nodes for relaying information. The primary goal of the neighbor discovery process is reducing power consumption and enhancing the lifespan of sensor devices. The sensor devices incorporate with advanced multi-purpose protocols, and specifically communication models with the pre-eminent objective of WSN applications. This paper introduces the power and security aware neighbor discovery for WSNs in symmetric and asymmetric scenarios. We have used different of neighbor discovery protocols and security models to make the network as a realistic application dependent model. Finally, we conduct simulation to analyze the performance of the proposed EASND in terms of energy efficiency, collisions, and security. The node channel utilization is exceptionally elevated, and the energy consumption to the discovery of neighbor nodes will also be significantly minimized. Experimental results show that the proposed model has valid accomplishment.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

Consensus-Based Time Synchronization Using Bayesian Estimation in Wireless Sensor Networks Under Communication Delays

Time synchronization is crucial for various applications in wireless sensor networks. Consensus-based time synchronization is an essential distributed strategy for its high precision, robustness, and scalability. In practical networks, however, communication delays are not ignorable and make time messages received inaccurate, which is the fundamental limitation for time synchronization. It was pointed out that if a consensus-based time synchronization algorithm does not consider the influence of delays, it may be divergent under the scenario with delays. Making use of the knowledge of statistical signal processing to develop an efficient relative skew estimator is a viable method to restrain communication delays. In this article, we propose a relative skew estimator based on Bayesian estimation theory to counteract the impact of time delays, which improves the estimation accuracy while reducing the memory requirement. By applying the presented estimator to average consensus protocol, precise network-wide synchronization is achieved in the presence of delays. In addition, the proof of convergence of the proposed scheme is given by theoretical analysis. Simulation results demonstrate the effectiveness of the proposed scheme and show that our algorithm outperforms other similar algorithms in terms of synchronization accuracy under random delays.

A novel adaptive deployment method for the single-target tracking of mobile wireless sensor networks

Single-target tracking is an important application of mobile wireless sensor networks. It is imperative to adaptively adjust the network deployment to achieve a satisfactory comprehensive tracking performance incorporating reliability, energy efficiency, connectivity, and tracking accuracy, which are all significant aspects for target tracking. However, the existing methods cannot ensure the optimization of the comprehensive tracking performance. Moreover, they are inapplicable to the realistic scenario with the common uncertainties, including the randomness of sensing, the randomness of received signal power, and the correlation between received signal powers. To solve these problems, a network model is proposed by modeling the common uncertainties to properly capture the characteristics of sensing and wireless communication. The reliability-energy index is defined to assess the comprehensive tracking performance. Furthermore, an adaptive deployment method is proposed to dynamically optimize the comprehensive tracking performance. Especially, to determine the optimal deployment at a sensing instant, an optimization algorithm is proposed by analyzing the monotonicity of the reliability-energy index and the impact of the positional relationship of nodes. Finally, a scenario of habitat monitoring is considered, and simulations are conducted to compare the proposed method with the existing methods to demonstrate the effectiveness of the proposed method.

Application of wireless sensor network in telemedicine monitoring

Wireless sensor networks have developed rapidly and have been applied to various fields of life, especially in the medical field, which has made great progress, especially in the telemedicine monitoring system. This paper analyzes and summarizes the application characteristics of wireless sensor in telemedicine detection. Then it points out the challenges faced by the promotion and popularization of telemedicine based on wireless sensor networks, and analyzes the energy consumption and security of wireless sensor networks in detail. Finally, the development of telemedicine monitoring system based on wireless sensor network is summarized and prospected.