Mechanism For Wireless Sensor Networks Research Articles

An artificial intelligence based authentication mechanism for wireless sensor networks using blockchain

Blockchain Networks (BNs) are widely used in a variety of applications around the globe. BN exchanges data and information using various inter-network and intra-network protocols. The sharing of this information across multiple platforms poses a significant security threat to the data sensed by the devices. This paper uses a combination of blockchain technology with AI algorithms to propose a novel security framework for BNs that acts as a security check gateway for any third party trying to access BN data. This paper proposed a hybrid security framework using blockchain. In order to provide a clear and efficient authentication scheme, global blockchain and local blockchain are separately used for BN authentication. The proposed work uses an artificial intelligence (AI) approach - Student Psychology Based Optimization (SPBO) is implemented in Matlab and Python. This approach is compared with some famous security algorithms of BNs, and it performs better than all the existing security algorithms used for BN.

Optimized Cluster Routing Protocol with Energy-Sustainable Mechanisms for Wireless Sensor Networks

Optimized Cluster Routing Protocol with Energy-Sustainable Mechanisms for Wireless Sensor Networks

An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications

The possible application of wireless sensor networks is hampered and the widespread use of this novel method is slowed, according to recent surveys conducted within the field of automation in industry, which identified that accuracy pertains indicate currently among the primary obstacles to the dissemination of wireless networking for recognizing and regulating applications. In order to overcome these constraints, it is necessary to raise public understanding of the reasons for dependability issues and the potential approaches to resolving them. Low-power communications of sensor nodes are, in reality, quite susceptible to adverse channel conditions and can readily be affected by transmissions of other co-located devices, making them seem unreliable. In this dissertation, I explore several strategies that may be used to either eliminate interference altogether or reduce its negative consequences. In this paper, we study the creation and modeling of a brand-new spectrum allocation mechanism for wireless sensor networks. Cognitive radio technology can detect spectrum holes in the environment, learn from its surroundings using artificial intelligence, adjust the system’s operating parameters in real-time, and use the secondary spectrum to increase efficiency. In this study, we present a reinforcement learning-based strategy for choosing the power of transmission and frequency that can help individual sensors learn from their prior decisions and those of their peers. Our suggested approach is multiple agents decentralized and adaptable to both the data needs from source to sink and the amount of energy that sensing devices in the network have left over. In comparison to different resource allocation algorithms, the results reveal a dramatic increase in the lifespan of the network.

Retracted: Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Retracted: Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Energy Efficient Trust Based Data Communication using AODV Protocol in MANET

MANET is a collection of mobile devices and small-scale infrastructure networks built without centralized control. Each node in these networks collaborates with the other nodes to serve as a host or router. Because the detection medium contains information about distributed communication transmitted through Internet resources, security is an essential aspect of the wireless sensor network. Due to security flaws in network routing attacks, most existing principles control power resources and topology structure responsibility in fewer security dependencies, resulting in various intrusion factors due to data breaches. This paper presents a secure routing mechanism for wireless sensor networks based on trust aware routing protocol. Most constraints the routing protocol is resilient in the presence of malicious nodes activities monitoring on the routing levels based on response portability and energy constraints. By concerning energy level-based security, we propose a Trust Aware Energy Efficient Routing Protocol (TAE2RP) using secure routing data transmission for improving lifetime maximization in WSN. Initially the Resistance Support Factor (RSF) was estimated based on the packet drop ratio by handling the data resemblance of route containing information. Then the routing levels behaviors are analyzed through Secure Surf-Channel Multicast Routing Protocol (SSCMRP). To evaluate the trust factor on packet transmission rate using TAE2RP. In addition, Redundant Array Shifting Encryption (RASE) was applied to secure the data packets. The authentication policy was enhanced through Master Node Digital signing authentication. This optimized the cost of security routing by handling energy balancing mechanisms to improve the higher security by concerning authentication to attain higher security.

Data Rate Aware Reliable Transmission Mechanism in Wireless Sensor Networks using Bayesian Regularized Neural Network approach

Wireless Sensor Networks consists of interconnected nodes that exchange information wirelessly enabling its deployment in innovative application areas. Network reliability streamline this exchange of information and communication technology to improve the overall performance of the network. The network reliability is categorized as: node reliability and link reliability. In this article, the node reliability is quantified with the help of packet reliability. The packet reliability is enhanced by optimizing the data rate using Bayesian Regularized Neural Network approach, which thus makes the network more reliable and sustainable. The optimization is carried out in three phases: network designing, data rate prediction and reliability evaluation. The network design includes the deployment of sensor nodes for gathering the communication data using NS-2.35. In the next phase, data rate prediction is carried out to enhance the reliability of the network. The reliability of a network is directly influenced by the packet loss ratio. According to research and the network experts, the acceptable threshold limit for the packet loss ratio is 5 percent. The data rate prediction is carried out to minimize the packet loss using the Bayesian Regularized Neural Network algorithm. The packet reliability is measured in terms of packet loss across the wireless network. Finally, a novel framework is presented for evaluating the packet reliability of the wireless network.

Mitigation of Black Hole and Selective Forwarding Attacks through Active Trust Mechanisms in Wireless Sensor Networks

In modern times, security remains a significant concern in wireless networks. Wireless sensor networks are widely employed in different practical situations. WSNs struggle with numerous internal and external threats, and detecting and defending against insider attacks is difficult. Insider attacks, in which intruders discard received data packets selectively, pose a significant risk to WSNs. The presence of black hole nodes within the network causes this issue. This research paper presents a secure and reliable route management methodology for WSNs that selects nodes and routing paths based on trust metrics. The proposed mechanism employs intelligent routing and node selection to defend against various types of assaults encountered during the routing process, including selective forwarding and black hole attacks. As a result, the paper identifies secure routes and establishes secure network routing. This scheme uses throughput, latency, and packet drop ratio as performance metrics. Experimentation demonstrates that the proposed system effectively increases the probability of secure network routing paths while extending the network lifecycle.

Energy aware optimized dynamic routing mechanism in wireless sensor networks

A trade-off between energy efficiency and optimized routing is massively recommended for transmission efficiency enhancement in wireless sensor networks (WSNs). Therefore, in this paper, graph-based energy optimized dynamic routing (GEODR) mechanism is introduced to set up a balance between energy consumption minimization and throughput enhancement using a dynamic and optimized routing mechanism in WSNs. A clustering scheme is employed based on graph theory, and cluster boundaries are formed using distance vectors. Cluster head (CH) selection is performed based on residual energy, the distance between CHs, and the mobility of the sink node. Each cluster is scattered with multiple tiny nodes, and event monitoring is performed. A model for graph-based dynamic routing to transmit data packets, cluster and cluster boundary formation, and optimization of routing problems is discussed. The performance efficiency of the proposed GEODR mechanism is determined by taking 100 sensor nodes, and 20 nodes are selected as CHs in a sensor network, and several other network parameters are also considered. A massive improvement in energy is observed by using sink node mobility. Experimental results are obtained using the proposed GEODR mechanism in terms of data packet transmission, alive nodes, dead nodes, and residual energy and compared against classical routing mechanisms such as low energy adaptive clustering hierarchy (LEACH) and stable election protocol (SEP).

Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs

With the explosion of big data, the implementation of distributed machine learning mechanisms in wireless sensor networks (WSNs) is becoming required for reducing the number of data traveling throughout the network and for identifying anomalies promptly and reliably. In WSNs, the above need has to be considered along with the limited energy and processing resources available at the nodes. In this paper, we tackle the resulting complex problem by designing a multi-criteria protocol CINE that stands for “Clustered distributed learnIng exploiting Node centrality and residual Energy” for distributed learning in WSNs. More specifically, considering the energy and processing capabilities of nodes, we design a scheme that assumes that nodes are partitioned in clusters and selects a central node in each cluster, called cluster head (CH), that executes the training of the machine learning (ML) model for all the other nodes in the cluster, called cluster members (CMs). In fact, CMs are responsible for executing the inference only. Since the CH role requires the consumption of more resources, the proposed scheme rotates the CH role among all nodes in the cluster. The protocol has been simulated and tested using real environmental data sets.

Data Aggregation Mechanisms in Wireless Sensor Networks of IoT: A Survey

Data Aggregation Mechanisms in Wireless Sensor Networks of IoT: A Survey

Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks.

A continuous-time exhaustive-limited (K = 2) two-level polling control system is proposed to address the needs of increasing network scale, service volume and network performance prediction in the Internet of Things (IoT) and the Long Short-Term Memory (LSTM) network and an attention mechanism is used for its predictive analysis. First, the central site uses the exhaustive service policy and the common site uses the Limited K = 2 service policy to establish a continuous-time exhaustive-limited (K = 2) two-level polling control system. Second, the exact expressions for the average queue length, average delay and cycle period are derived using probability generating functions and Markov chains and the MATLAB simulation experiment. Finally, the LSTM neural network and an attention mechanism model is constructed for prediction. The experimental results show that the theoretical and simulated values basically match, verifying the rationality of the theoretical analysis. Not only does it differentiate priorities to ensure that the central site receives a quality service and to ensure fairness to the common site, but it also improves performance by 7.3 and 12.2%, respectively, compared with the one-level exhaustive service and the one-level limited K = 2 service; compared with the two-level gated- exhaustive service model, the central site length and delay of this model are smaller than the length and delay of the gated- exhaustive service, indicating a higher priority for this model. Compared with the exhaustive-limited K = 1 two-level model, it increases the number of information packets sent at once and has better latency performance, providing a stable and reliable guarantee for wireless network services with high latency requirements. Following on from this, a fast evaluation method is proposed: Neural network prediction, which can accurately predict system performance as the system size increases and simplify calculations.

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

In recent scenario of Wireless Sensor Networks (WSNs), there are many application developed for handling sensitive and private data such as military information, surveillance data, tracking, etc. Hence, the sensor nodes of WSNs are distributed in an intimidating region, which is non-rigid to attacks. The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats. In traditional models, the solution has been made for defending the networks, only to specific attacks. However, in real-time applications, the kind of attack that is launched by the adversary is not known. Additionally, on developing a security mechanism for WSN, the resource constraints of sensor nodes are also to be considered. With that note, this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism (IDRM) for defending the sensor network from various attacks. Moreover, for efficient model design, the work includes the part of feature evaluation of some general attacks of WSNs. The IDRM also includes determination of optimal secure paths and Node security for secure routing operations. The performance of the proposed model is evaluated with respect to several factors; it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications. It is proven that the proposed IDRM produces 74% of PDR in average and a minimized packet drop of 38% when comparing with the existing works.

Practical and scalable access control mechanism for wireless sensor networks

Practical and scalable access control mechanism for wireless sensor networks

Practical and scalable access control mechanism for wireless sensor networks

Practical and scalable access control mechanism for wireless sensor networks

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks.

Review on Security Issues and Applications of Trust Mechanism in Wireless Sensor Networks.

The scale of wireless sensor networks changes depending on specific application tasks. How to design a relatively simple security mechanism that can extend with the expansion of network scale is an arduous task. As the wireless sensor network mostly adopts the decentralized organization form, once the network nodes are distributed adequately, it is difficult to find out the failure of any node. In addition, the node has weak resistance to physical damage, so any node in the network may potentially become a cause of security vulnerability. In this study, firstly, the security standards that wireless sensor networks should have and the current security challenges faced by the network are discussed in detail, and the importance of security issues in wireless sensor networks is pointed out. Secondly, this paper studies and analyzes the current situation of wireless sensor network security problems including the internal and external attacks and the active and passive attacks. Thirdly, this paper also discusses the significance, concept, and characteristics of reputation and trust, the division and composition of reputation and trust system, and the common applications of the reputation and trust mechanism in wireless sensor networks.

Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks

The clustering mechanism in wireless sensor networks (WSNs) is the ideal strategy for constructing an energy efficient protocol for achieving extended network lifetime, energy efficiency, and scalability. The design of energy efficient protocols in WSNs is essential, as the sensor nodes are battery powered, and may be completely drained, resulting in deterioration in network lifetime. Energy efficient protocols aim to maximize network lifetime while minimizing overall energy consumption to avoid interruptions to the wireless sensor nodes deployed for monitoring and recording physical information from the environment. Moreover, hybrid metaheuristic-optimization based clustering and routing protocols are widely used for attaining energy stability and network lifetime. In this study, the golden eagle optimization algorithm (GEOA) and improved grasshopper optimization algorithm (IGHOA) based on the energy efficient cluster-based routing protocol (GEIGOA) are proposed for sustaining energy stability and augmenting network lifetime longevity by overcoming challenges in the cluster head (CH) selection process. In particular, by optimizing the node centrality, node degree, distance to the base station, distance to the neighbors, and residual node energy, GEOA selects an optimal CH from the deployed group of sensor nodes in the network. Furthermore, IGHOA is utilized to determine a reliable and optimal route between the CH and base station (BS) by assessing node degree, residual energy, and distance parameters. Moreover, the proposed GEIGOA was confirmed to be sufficiently capable in improving the probability of preventing the worst nodes from being selected as CHs with different number of sensor nodes, which was predominantly enhanced by 12.64%, 15.82%, 18.96%, and 20.98% compared with the competitive CH selection schemes. In addition, the computational cost incurred by the proposed GEIGOA with different number of sensor nodes was also confirmed to be minimized by 14.98%, 17.21%, 19.76%, and 21.62% compared with the competitive CH selection schemes.

Multimodal Biometric-Based Secured Access Mechanism for Wireless Sensor Networks

Wireless Sensors Network’s (WSN) security is a critical concern owing to their unattended and intimidating distribution. Providing authentication to these networks with limited resources is a challenging task. Biometric based user authentication schemes with light computations and easy operations provide potential solutions. Several unimodal biometric techniques are proposed to secure WSN. Unimodal is susceptible to limited accuracy and spoofing. However, Multimodal biometric technology offers greater security. This paper proposes an algorithm combining fingerprint and iris biometric features for authentication. Registration and authentication processes using limited resources that are available at the WSN nodes are implemented. Experimentation results calculate computational overhead and are compared with existing methods.

Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks

Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks

Security threats Detection and Handling Mechanism in Wireless Sensor Networks using Machine Learning

Security threats Detection and Handling Mechanism in Wireless Sensor Networks using Machine Learning