Within Wireless Sensor Networks Research Articles

Dual-stage machine learning approach for advanced malicious node detection in WSNs

Within wireless sensor networks (WSNs), a multitude of vulnerabilities can arise, particularly those originating from malicious nodes (MNs), which lead to compromised data integrity, network stability, and critical application reliability. Although security and energy efficiency remain critical, current MN detection methods are resource-intensive and time-consuming, rendering them unsuitable for constrained WSNs. Although machine learning-based methods excel at detecting MNs, they often incur significant time overhead owing to extensive data transmission and coordination, leading to increased latency and energy consumption within the network. This study introduces DSMND, a novel dual-stage MN detection scheme that harnesses machine learning to enhance MN identification in WSNs. The initial stage uses dynamic threshold detection and decision-tree algorithms at the cluster head (CH) level. This adaptive detection process optimizes CH resource levels, feature counts, and threshold values for efficient MN identification. When thresholds are exceeded, the second stage activates on the server side, employing an advanced MN detection model that seamlessly integrates a hybrid convolutional neural network and a random forest classifier to boost detection accuracy. Leveraging SensorNetGuard, a dataset with diverse node and network features, further enhances reliability. Extensive analysis shows that our scheme achieves up to 99.5 % detection accuracy at the CH level and nearly 100 % at the server side. The average execution time is 124.63 ms, making it 97 % faster than conventional methods. Additionally, DSMND reduces CH power consumption by up to 70 % and extends network lifetime by 2.7 times compared to existing methods. These results confirm the effectiveness of our approach for real-time detection and mitigation of MNs within WSNs.

Advanced authentication of IoT sensor network for industrial safety

As industries rapidly advance towards automation and intelligence, the Industrial Internet of Things (IIoT) plays a pivotal role in enhancing factory efficiency and production management. However, the advent of quantum computing introduces significant security challenges, particularly for data transmission and privacy protection, as its immense computational power can compromise the current cryptographic theories' safeguards. We introduce a novel lattice-based cryptographic authentication and key management scheme specifically designed for the IIoT within Wireless Sensor Networks (WSNs), focusing on establishing a secure industrial communication system capable of withstanding quantum threats. The proposed scheme utilizes Crystals-Kyber technology and is built around the Learning With Errors (LWE) problem, providing a robust foundation for security against both traditional and quantum threats. It features innovative methods to safeguard against password guessing, stolen verifier, and replay attacks, enhancing data confidentiality and system integrity. By integrating advanced cryptographic techniques resistant to quantum decryption, this research ensures the secure management and utilization of critical production data, supporting the ongoing transformation towards more intelligent manufacturing processes and sustaining global competitiveness for industries.

A Novel Approach for Mobility-Aware Energy-Efficient Clustering-Based Routing Using EANN With GA Algorithm on WSNs

Aim: This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture. Background: In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome. Objective: The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves. Methods: The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process. Results: Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs. Conclusion: The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

Enhancing Wireless Sensor Network Security Through Mutual Information Analysis for Intrusion Detection and Resilience

Intrusion detection is a critical aspect of network security, involving the identification and response to unauthorized access or malicious activities within a system. It plays a crucial role in safeguarding networks and ensuring data integrity and confidentiality. The Mutual Information (MI) analysis is a vital component in the realm of intrusion detection and security within Wireless Sensor Networks (WSNs). By assessing the information shared between pairs of sensor nodes, MI analysis reveals underlying patterns and relationships in network data. This process involves calculating the mutual information for each node pair based on statistical properties of observed values. Strong correlations or dependencies between nodes are identified, aiding in the detection of critical nodes or clusters vulnerable to compromise. Additionally, MI analysis informs feature extraction by guiding the selection of informative features that capture network structure. It also serves as a proactive tool for identifying anomalies or deviations from expected patterns, which may signal intrusion attempts or malicious activities. Through monitoring changes in mutual information over time, MI analysis facilitates prompt responses to emerging threats, enhancing the resilience and security of WSNs. The work exhibits high accuracy, recall rates, and detection rates across various attack scenarios, underscoring its efficacy in identifying and mitigating security threats. The algorithm's efficiency, effectiveness, and reliability make it a promising solution for enhancing WSN security. Through sophisticated methodologies and adaptive defensive strategies, the proposed algorithm strengthens the robustness and dependability of WSNs, minimizing the risk of potential security vulnerabilities and ensuring comprehensive threat detection in real-world deployment scenarios.

Performance and QoS Assessment in WSN: Comparative Analysis of MBC, ECHERP, and PDCH Routing Methods

Objective: The objective of this research is to assess the performance and Quality of Service (QoS) provided by PDCH when compared with ECHERP and MBC routing protocols(RPs) within Wireless Sensor Networks (WSNs). Methods: Utilizing the NS2 Simulator, this paper provides an in-depth exploration of three hierarchical RPs viz. Equalized Cluster Head Election RP (ECHERP), Mobility Based Clustering (MBC), and PDCH (PEGASIS with Double Cluster head). A thorough examination of each protocol's performance is conducted, focusing on a range of QoS metrics such as Energy Consumption, Packet Drop Ratio, Throughput, and Delay. Findings: A comparative assessment is conducted between PDCH, ECHERP, and MBC, extracting QoS parameters. The examination and findings reveal that PDCH exhibits superior performance over ECHERP by 26.15%, 20%, 15.71%, and 37.73%. Similarly, PDCH outperforms MBC by 23.19%, 14.29%, 4.87%, and 15.13% across the mentioned parameters. Novelty: In this simulation, the NS2 software is utilized, with a specific emphasis on the suggested parameters. These metrics are crucial for evaluating the overall performance of different RPs in Wireless Sensor Networks (WSNs).

Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability

As the digital landscape expands rapidly due to technological advancements, cybersecurity concerns have become more prevalent. Intrusion Detection Systems (IDSs), which are crucial for identifying unusual network traffic indicative of malicious activity, have become a necessity. These systems can be either hardware or software-based. However, traditional IDS models often fail to adequately protect data privacy and detect complex, unique breaches, particularly within Wireless Sensor Networks (WSNs). To address these limitations, this paper proposes a novel Stacked Convolutional Neural Network and Bidirectional Long Short Term Memory (SCNN-Bi-LSTM) model for intrusion detection in WSNs. This model leverages Federated Learning (FL) to enhance intrusion detection performance and safeguard privacy. The FL-based SCNN-Bi-LSTM model is unique in its approach, allowing multiple sensor nodes to collaboratively train a central global model without revealing private data, thereby alleviating privacy concerns. The deep learning methodology of the SCNN-Bi-LSTM model effectively identifies sophisticated and previously unknown cyber threats by meticulously examining both local and temporal linkages in network patterns. The model has been specifically designed to detect and categorize different types of Denial of Service (DoS) attacks using specialized WSN-DS and CIC-IDS-2017 datasets. Compared to traditional Artificial Deep Neural Network (ADNN) models, our proposed FL-SCNN-Bi-LSTM model demonstrated superior detection rates for complex and unknown attacks, significantly improving IDS performance. The model achieved a notable classification accuracy of approximately 99.9% precision and recall on both datasets, substantially reducing false positives and negatives. Our research underscores the potential of federated learning and deep learning in enhancing the security and privacy of WSNs. The proposed FL-SCNN-Bi-LSTM architecture not only facilitates the identification of complex cyber threats but also exemplifies how deep learning techniques can be employed to bolster intrusion detection systems while preserving user data privacy.

A comprehensive decision assessment for trust evaluation in wireless sensor networks

Objective: This study endeavors to tackle the pressing issue of network security within Wireless Sensor Networks (WSNs) through the introduction of an innovative integrated Pythagorean fuzzy-based method for evaluating network nodes. The primary aim is to enrich decision making frameworks within WSNs by furnishing a robust methodology for identifying and eradicating malevolent nodes. Methods: investigation employs an integrated Pythagorean fuzzy-based methodology to appraise network nodes by scrutinizing specific trust attributes. Comparative assessment with alternative contemporary Multiple Criteria Decision Making (MCDM) trust models is conducted to gauge the effectiveness of the proposed strategy. Results: The results underscore the efficacy of the integrated Pythagorean fuzzy-based approach in bolstering network security through the precise identification and elimination of malicious nodes. Comparative analysis with other MCDM trust models highlights the superiority of the proposed approach in evaluating network nodes based on trust attributes. Recommendations: In light of the findings, it is advised to integrate fuzzy decision analysis methodologies into decision making systems for WSNs to enhance network security. Additionally, future research endeavors could concentrate on refining and expanding upon the proposed methodology to effectively address emerging security challenges within WSNs.

Effective Data Aggregation Moel for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment

Data aggregation is the process of collecting and combining information from multiple sources to provide a unified view or summary. In various fields such as statistics, economics, and data analysis, aggregating data helps reveal patterns, trends, or general insights that may not be apparent when examining individual data points. Aggregated data can provide a more comprehensive perspective, facilitating decision-making and strategic planning. This paper explores the application of Sugeno Fuzzy Model Monkey Swarm Optimization (SFMMsO) in healthcare, specifically focusing on data aggregation and security within Wireless Sensor Networks (WSN). The study demonstrates SFMMsO’s efficacy in aggregating patient health data, generating comprehensive Aggregated Health Indices. It also highlights SFMMsO’s optimization capabilities, refining decision variables to enhance healthcare algorithm performance. The paper addresses the critical dimension of security, showcasing SFMMsO’s adaptability in optimizing security parameters and assessing its vulnerability to various attack types. The findings underscore SFMMsO’s potential as a robust tool for healthcare applications, emphasizing the need for proactive security measures to fortify its resilience against potential threats. This study contributes valuable insights into the intersection of optimization algorithms, data aggregation, and security in healthcare, paving the way for advancements in utilizing SFMMsO for secure and comprehensive healthcare systems.

Development of a novel 6LoWPAN‐based multipurpose sensor monitoring and notification system

SummaryIn the area of wireless communication technologies, 6LoWPAN leverages the extensive capabilities of IPv6, even within the constraints imposed by resource‐limited devices, particularly within wireless sensor networks (WSNs). The integration of 6LoWPAN into modern solutions for implementing the IPv6‐based Internet of Things (IoT) is becoming increasingly important. In this paper, a sensor monitoring and notification system specifically designed for deployment over 6LoWPAN has been proposed. Multipurpose capability, scalability, and ease of deployability are the main features of the proposed system. Its architecture reflects the highest degree of flexibility and allows for a variety of use cases encountered in practical scenarios. In addition, a web interface has been developed as part of the comprehensive system architecture. This interface enables efficient management of the entire system and facilitates connection for new users and seamless integration of additional sensors. By encapsulating complex functions in a user‐friendly interface, the system promotes accessibility, convenience, and an enhanced user experience. The proposed system overcomes the limitations of current approaches, thereby creating new opportunities. The flexibility of the proposed system allows it to be applied to various use cases.

Intelligent Mesh Cluster Algorithm for Device-Free Localization in Wireless Sensor Networks

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target’s location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization.

IoT Edge-Computing-Enabled Efficient Localization via Robust Optimal Estimation

Source localization within wireless sensor networks (WSNs) is one of the critical technologies in the Internet of Things (IoT). As the number of network nodes increases, so does the amount of data and computational requirement. It is imperative to introduce edge computing. However, there are still two issues when running existing wireless location algorithms on edge nodes: 1) conventional low-complexity approaches are easily affected by the bias generated in complex environments, leading to low locating accuracy and 2) the optimization algorithms considering the bias have good performances, but they are calculation-efficiency low on edge nodes. This study proposes a computationally efficient and high-precision location method to tackle the troubles. Precisely, we first introduce our previous research to construct a bias-considered nonconvex problem with a linear objective. Then, we propose an angle-assisted Taylor series with zero truncation error to linearize the second-order cone (SOC) constraint in the established problem. Next, we resort to the mini-max criterion to eliminate the angular uncertainty and get a robust linear programming (LP) problem with an optimal solution. So far, we have obtained a convex problem of low complexity. To ensure the calculated efficiency of the proposed problem on edge nodes, we proceed to give the solving process of the problem. Moreover, we provide a constraints tracking mechanism to reduce the number of iterations in the solution procedure, improving computational efficiency. Simulations and experiments demonstrate that the proposed method with similar locating accuracy to state-of-the-art optimization algorithms exhibits much higher computing efficiency on edge nodes.

Lab-Based Evaluation of Device-Free Passive Localization Using Multipath Channel Information.

The interconnection of devices, driven by the Internet of Things (IoT), enables a broad variety of smart applications and location-based services. The latter is often realized via transponder based approaches, which actively determine device positions within Wireless Sensor Networks (WSN). In addition, interpreting wireless signal measurements also enables the utilization of radar-like passive localization of objects, further enhancing the capabilities of WSN ranging from environmental mapping to multipath detection. For these approaches, the target objects are not required to hold any device nor to actively participate in the localization process. Instead, the signal delays caused by reflections at objects within the propagation environment are used to localize the object. In this work, we used Ultra-Wide Band (UWB) sensors to measure Channel Impulse Responses (CIRs) within a WSN. Determining an object position based on the CIR can be achieved by formulating an elliptical model. Based on this relation, we propose a CIR environmental mapping (CIR-EM) method, which represents a heatmap generation of the propagation environment based on the CIRs taken from radio communication signals. Along with providing imaging capabilities, this method also allows a more robust localization when compared to state-of-the-art methods. This paper provides a proof-of-concept of passive localization solely based on evaluating radio communication signals by conducting measurement campaigns in an anechoic chamber as a best-case environment. Furthermore, shortcomings due to physical layer limitations when using non-dedicated hardware and signals are investigated. Overall, this work lays a foundation for related research and further evaluation in more application-oriented scenarios.

RETRACTED ARTICLE: A provenance based defensive technique to determine malevolent selective forwarding attacks in multi-hop wireless sensor networks

Multi-hop wireless sensor networks are being implemented in various domains. The collected data are used to make important decisions for censorious infrastructures. Data are processed by a huge quantity of sensor nodes as well as handled on intermediate nodes which are known as hops on the way to the Base Station (BS) which performs the resolution accomplishing. Information have been collected from various sources and intermediate hopes will process and aggregate information. A malevolent opponent may interfere through the information via proposing extra node within the web infrastructure. There are chances to compromising the existing nodes. Malevolent Selective Forwarding attacks are most important protection problem toward the information forwarding within wireless sensor networks (WSN), as this might hinder broadcast of susceptible information as well as it reduces network throughput. Assuring data trustworthiness in this situation has been critical in support of accurate resolution accomplishing. Information provenance signifies a main aspect into analyzing the trustworthiness of wireless sensor data. In this paper we introduce a provenance based technique to determine the attack. Three phases are there in this scheme, detecting packet-loss in the first phase, second phase dealing with identifying attacked sensor node and isolating malevolent sensor node in the third phase. We have included the detailedanalysis of the investigational outcomes for showing the accurateness as well as effectiveness of the proposed system.

Robot Control Strategies for Task Allocation with Connectivity Constraints in Wireless Sensor and Robot Networks

Mobility within Wireless Sensor Networks (WSNs) has been widely considered for data collection tasks, where mobile robots physically collect the data from the sensors and return to the base station. Although this approach has proven to be useful in prolonging the lifetime of these networks, it cannot meet the requirements of real-time data collection tasks. For such tasks, we need to utilize mobile robots to create a connected path from the base station to the event, as well as use in-network forwarding through that path. This will provide a longer lifetime while addressing efficiency and scalability issues because mobile robots have a larger and renewable energy reserve, a longer transmission range, and capacity. One of the fundamental problems is how to coordinate robots to establish a connected path from the event location to the base station. We consider this fundamental problem with two objectives, namely minimizing distance traveled by the robots and minimizing hop count (the number of robots used on paths) under the constraints of satisfying the path and/or network connectivity. After mathematically formulating the underlying problems and discussing their NP-hardness, we propose various heuristic solutions. We then demonstrate the efficacy of our proposed solutions through extensive simulations.

Indoor Localisation of Wireless Sensor Nodes Towards Internet of Things

Abstract: Internet of Things (IoTs) is an emerging technology that is envisioned to revolutionise the enabling environment and to provide autonomous, ubiquitous and pervasive computing within Wireless Sensor Networks (WSNs). IoTs are usually associated with the acquisition of sensor node information and controlling of “things”. However, the absence of location information of these sensor nodes compromises the intelligence of the IoT network. Therefore, this work is motivated by the recent advances in the two important areas of WSNs namely, indoor localisation and IoTs. This paper therefore presents a framework that integrates indoor localisation of sensor nodes and IoTs in a real world scenario. The focus is mainly on the implementation issues regarding localisation algorithm complexity, hardware computational capabilities and Internet/Intranet enabled connectivity for access to the sensor nodes’ location information. A sensor node prototype is developed using specialised electronic components and proprietary protocols to provide a capable platform for embedding a distributive online localisation algorithm based on Received Signal Strength (RSSI) and Gauss-Newton Algorithm (GNA). The algorithm is first simulated before porting it to the sensor node prototypes. A gateway device and an IoT framework are also proposed and implemented based on Linux, Apache, MySQL, PHP (LAMP) server to provide global and local access to sensor nodes’ location information. The Root Mean Square Error (RMSE) of the IoT logged estimated coordinates from the prototype nodes and the estimated coordinates from the simulation are computed and compared. The computational power of the hardware is analysed based the time it takes to perform the GNA based localisation process.

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

Dynamic skyline query is one of the most popular and significant variants of skyline query in the field of multi-criteria decision-making. However, designing a distributed dynamic skyline query possesses greater challenge, especially for the distributed data centric storage within wireless sensor networks (WSNs). In this paper, a novel Enhanced Distributed Dynamic Skyline (EDDS) approach is proposed and implemented in Disk Based Data Centric Storage (DBDCS) architecture. DBDCS is an adaptation of magnetic disk storage platter consisting tracks and sectors. In DBDCS, the disc track and sector analogy is used to map data locations. A distance based indexing method is used for storing and querying multi-dimensional similar data. EDDS applies a threshold based hierarchical approach, which uses temporal correlation among sectors and sector segments to calculate a dynamic skyline. The efficiency and effectiveness of EDDS has been evaluated in terms of latency, energy consumption and accuracy through a simulation model developed in Castalia.

English

The Wireless sensor network is used to monitor the temperature, humidity, Sound, pressure, etc. Work within wireless sensor networks (WSNs) Quality of service (QoS) has been isolated and specific either on certain functional layers or application scenarios. However the area of sensor network quality of service (QoS) remains largely open. In this paper we define WSNs QoS requirements within a WSNs application, and then analyzing Issues for QoS Monitoring. In this we define IEQGOR, that integrates awake/asleep schedules, MAC, routing, traffic load balancing. A promising routing scheme in wireless sensor networks (WSNs), is shifting toward duty-cycled WSNs in which sensors are sleep scheduled to reduce energy consumption.

Pallier Based Homomorphic Encrypted Data Aggregation in Wireless Sensor Networks

Tiny autonomous embedded electronics (sensor nodes) devices able to communicate through wireless channels are ensuring the emission and reception of data through a communication radio between two sensors grouped by hundreds and thousands within Wireless Sensor Networks (WSNs). These amazing new technology with ongoing research worldwide, are merging networking, systems hardware, systems software and programming methodologies thus enabling applications that previously were not practical. Hence numerical simulations on computers can now visualize the physical world phenomena that could be observed through empirical means, as sensors are deployed in a dedicated environment, to fulfill their aim of sensing for any occurrence of the event of interest. The data sensed by these wireless sensors are now very sensitive, thus need to be fully protected by all means, which is why T. D. Engouang et al., argued that securityand reliability and also durability are mandatory when deploying any sensor nodes or hard device. The Pallier based homomorphic encryption data aggregation is proposed with security measures preserving data integrity and privacy.

Data aggregation at the gateways through sensors’ tasks scheduling in wireless sensor networks

The authors present a novel graph-based model for aggregating sensors’ data at the gateways within wireless sensor networks (WSNs), where the proposed graph can act as a mean of guiding and assessing the needed resources for data aggregation. In this study, the authors have modelled all sensors’ tasks in a graph so that their collected data are to be smoothly aggregated (scheduled) at the gateway without losing or overlapping the collected data at the gateways. A typical WSN comprises of hundreds of sensors and few gateways; moreover, each sensor executes periodically and sequentially three tasks, which are sensing, processing and transmitting. The three tasks are modelled as a directed acyclic graph (DAG) per sensor, and then all DAGs are grouped into a super task-flow-graph (STFG). The data aggregation problem is solved by scheduling the tasks within STFG, where the authors have utilised three scheduling algorithms: as soon as possible, as late as possible and branch-and-bound. The simulation results for 50 sensors covering an area of 10 000 m2 utilising deterministic and stochastic execution models show a requirement of eight and six gateways, respectively, with minimal waiting time for aggregating the collected data from sensors to the gateways.

Analysis of Sensors’ Coverage through Application-Specific WSN Provisioning Tool

This paper presents an automated provisioning tool for the deployment of sensors within wireless sensor networks (WSN) where we have employed evolutionary approach as a search technique to find the maximal coverage under minimal deployment cost. The coverage area is partitioned into M by N cells to reduce the search space from continuous to discrete by considering the placement of sensors at the centroid of each cell. The author has explored the relationship between various cell’s sizes versus the total number of deployed sensors. The experimental results show that when the number of cells to cover the service area from X by X cells to 2X by 2X cells is increased, on average this increases the cost by 3 folds. In this regard, it is due to the increase of the number of required sensors by an average of six folds, while improving the coverage ratio by only 9%. A custom-made graphical user interface (GUI) has been developed and embedded within the proposed automated provisioning tool to illustrate the deployment area with the placed sensors at step of the deployment process.