IoT-based Wireless Sensor Networks Research Articles

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization

ABSTRACT Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes’ constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques.

Environmental noise monitoring using distributed hierarchical wireless acoustic sensor network

Acoustic noise pollution is one of many problems people face as cities grow. Long-term noise exposure can result in a series of physical and mental health diseases that are highly harmful to foetuses and newborns. Hence, many IoT-based wireless sensor network systems have been proposed for automated monitoring for long-term operation. However, these systems suffer from weaknesses in functionality, power consumption, costs, and scalability, which hinder large-scale deployment. In this study, we propose a distributed hierarchical wireless acoustic sensor network for environmental noise monitoring to do sound classification and A-weighted sound-pressure-level measurement to address the shortcomings of existing systems. A series of tests and comparisons are performed in diagnosing the performance with respect to recording continuity, packet loss, recording quality, accuracy on A-weighted sound pressure level calculations, and costs. Results show that this proposed network structure is feasible as a part of hardware implementation in a large-scale, low-cost, and high-scalable environmental noise monitoring system to classify sound.

Retraction Note: OTP-ER: an ordered transmission paradigm for effective routing in IoT based wireless sensor networks

Retraction Note: OTP-ER: an ordered transmission paradigm for effective routing in IoT based wireless sensor networks

An Energy-Efficient improved Grey Wolf Optimization Algorithm-Based Cluster Head and Shamir Secrets Sharing-Based WSNs with Secure Data Transfer

Introduction: Due to its self-configurability, ease of maintenance, and scalability capabilities, WSNs (Wireless Sensor Networks) have intrigued plenty of interest in a variety of fields. To move data within the network, WSNs are set up with more nodes. The security of SNs (sensing nodes), which are vulnerable to malevolent attackers since they are network nodes, is a crucial element of an IoT (Internet of Things)-based WSN. This study's primary objective is to provide safe routing and mutual authentication with IoT-based WSNs. Methods: The basic GWO algorithm's imbalances between explorations and mining, lack of population heterogeneity, and early convergences are all issues that this paper addresses by selecting energy-efficient CHs (cluster Heads) using EECIGWO algorithm, an upgraded version of the GWO, is used. Mean distances within clusters, well-spaced residual energies, and equilibrium of CHs are all factors that influence the choices of CHs. The average intra-cluster distances, sink distances, residual energies, and CHs balances are some of the criteria used to choose CHs. Results and Discussion: The proposed EECHIGWO-based clustering protocol's average throughput, dead node counts, energy consumption, and operation round counts have all been evaluated. Additionally, mutual authentication between the nodes is provided through SSS (Shamir Secret Sharing) mechanism. PDR (Packet Delivery Ratio) analysis is used to assess how well the EECHIGWO-IOT-WSNs are performing. Conclusion: The suggested proposed approach is assessed against existing methods like HHH-SS (Hybrid Harris Hawk and Salp Swarm), ESR (Energy-efficient and Secure Routing) protocol, and LWTS (Light Weight Trust Sensing) approaches in terms of AEED (Average End-to-End Delay), network overheads, and PLR (Packet Loss Ratio).

An Energy-Optimized Artificial Intelligence of Things (AIoT)-Based Biosensor Networking for Predicting COVID-19 Outbreaks in Healthcare Systems

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today's environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices' limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model's efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems.

EWFAIGF: Design of an Efficient Model for Enhancing Energy Efficiency in IoT-Based Wireless Sensor Networks through Fuzzy AHP and Iterative Grey Wolf Jelly Fish Optimization

In the realm of Wireless Sensor Networks (WSNs), the quest for enhanced energy efficiency remains paramount, given their pivotal role in facilitating the Internet of Things (IoT) applications. Existing methodologies often grapple with the dual challenge of optimizing energy consumption while ensuring robust data transmission, thereby limiting their efficacy in dynamic, resource-constrained environments. This work introduces a novel paradigm that meticulously addresses these constraints, thereby heralding a significant leap towards optimizing WSNs' operational efficiency. At the core of our proposed model lies the integration of Fuzzy Analytic Hierarchy Process (Fuzzy AHP)-based clustering, which ingeniously segregates nodes by leveraging multi-criteria such as location, energy efficiency, and temporal performance. This clustering serves as a precursor to the application of an innovative Iterative Grey Wolf Jelly Fish Optimizer (GWJFO). The GWJFO stands out by its strategic prowess in delineating optimal routing paths, thus minimizing energy expenditure and enhancing data relay efficiency. Furthermore, our model is fortified with a Q Learning Method, ingeniously designed to identify and execute optimal alternate routing paths through a Make Before Break strategy. This addition not only mitigates potential faults but also significantly boosts computational efficiency and packet delivery performance. Empirical validation through real-time network simulations underscored the model's superiority, demonstrating a 9.5% improvement in communication speed, an 8.5% increase in energy efficiency, a 4.5% improvement in packet delivery performance under fault conditions, a 10.4% rise in throughput, and a 5.9% enhancement in network consistency over existing benchmarks. This groundbreaking work not only paves the way for more energy-efficient WSNs but also sets a new standard in achieving high-performance metrics essential for the next generation of IoT applications. The implications of these advancements extend beyond mere technical enhancements, offering a beacon for future research in the domain, potentially revolutionizing the way we deploy and manage sensor networks in an array of applications.

Improving Connectivity in Urban IoT-Based Wireless Sensor Networks: A Nonorthogonal Cognitive-Based Power Allocation

Improving Connectivity in Urban IoT-Based Wireless Sensor Networks: A Nonorthogonal Cognitive-Based Power Allocation

Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review

Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review

Prototype of Forest and Land Fire Monitoring and Detection System Using IoT-Based WSN Technology

Forest and land fires are a recurrent issue in several Indonesian regions, necessitating advanced technological solutions for early detection and monitoring due to their significant impact. This research project focuses on developing a prototype system utilizing IoT-based Wireless Sensor Network (WSN) technology to detect forest and land fires. The prototype offers real-time remote monitoring of temperature, humidity, smoke levels, and wind speed in the forest through an Android app or web interface. To validate its performance, the prototype was compared to existing BMKG tools, with results showing minor temperature errors (2.41%) and a humidity error of 17.68%. The anemometer sensor exhibited a slight 4-second data transmission delay. Importantly, the prototype excelled in fire detection, effectively identifying temperature and humidity changes within a 2-meter radius or more indicative of fire outbreaks. This pioneering prototype promises to significantly enhance early warning and response mechanisms for forest and land fires in Indonesia, contributing to more effective environmental conservation and disaster management.

Energy Efficient Cluster based Routing Scheme for WSN based IoT to Extend Network Lifetime

With the development and advancement of wireless sensor networks (WSN), the emergence of the Internet of Things (IoT) has achieved prominence in the modern era. With the increasing number of connected devices, WSN has become a key factor in the communication component of the IoT. In IoT-based WSN infrastructure, devices are equipped with intelligent sensors that sense the environment to collect data, process data, and deliver information to the sink or base station (BS). WSN-assisted IoT has become a key technology for various data-centric applications such as health care, smart cities, and the military. Sensor nodes in IoT devices are equipped with bound and irreplaceable batteries. An increased number of connected devices face serious issues of energy depletion, maintenance, and load balancing, which might result in device failure. Energy efficiency is considered a vital parameter in the design of an IoT based WSN, and this can be accomplished through clustering and multihop routing techniques. In this paper, we propose an energy-aware multihop routing scheme (EAMRS) for hierarchical cluster-based WSN-assisted IoT. EAMRS considers the improved low-energy adaptive clustering algorithm (I-LEACH) to select optimal cluster heads (CH). During data transmission, multihop routing is involved by considering routing metrics such as residual energy, distance to BS and optimal route choice to balance the energy load. However, conventional routing schemes fail to achieve the flexibility and adaptability prerequisites of load balancing mechanisms. EAMRS decreases computation overhead and restricts energy usage, resulting in a prolonged network lifetime.

Secure and Lightweight Authentication Protocol Using PUF for the IoT-based Wireless Sensor Network

The wireless sensor network (WSN) has been gaining popularity for automation and performance improvement in different IoT-based applications. The resource-constrained nature and operating environment of IoT make the devices highly vulnerable to different attacks. However, the Physically Unclonable Function (PUF) helps to implement secure and lightweight authentication protocols for IoT. In this context, few computation-intensive authentication protocols are found in the literature that have addressed secure IoT communication in WSN. Besides, these protocols depend on the local storage of PUF-CRP, which is susceptible to security attacks. This work proposes a lightweight and secure authentication protocol for the IoT devices in WSN. A PUF and its machine learning (ML)–based soft model is integrated to ensure secure authentication and lightweight computation in WSN. PUF prevents physical attacks while carrying much less hardware fingerprints, and the ML-based PUF provides the desired resiliency against PUF identity-based attacks by eliminating the requirement of CRP-based storage. The proposed mechanism delivers two-way authentication while nullifying the attacks on IoT. The proposed protocol is implemented on Xilinx Artix-7 FPGA and Raspberry Pi for testability and performance evaluation. Experiment results and analysis signify its low-cost computations and lightweight features desired for IoT.

Self-Configuration and Self-Healing Framework Using Extreme Gradient Boosting (XGBoost) Classifier for IoT-WSN

In most Internet of Things (IoT) systems, Quality of service (QoS) must be confirmed with respect to the requirement of implementation domain. The dynamic nature of the IoT surroundings shapes it to complicate the fulfilment of these commitments. A wide range of unpredictable events endanger the quality of service. While execution the self-adaptive schemes handle with system’s unpredictable. In IoT-based Wireless Sensor Networks (WSNs), the significant self-management objectives are self-configuration (SC) and self-healing (SH). In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed. In this framework, the IoT traffic classes are categorized as several types under XGBoost classifier. In SC phase, the IoT devices are self-configured by allocating various transmission slots, contention access period (CAPs) on the basis of its categories with priorities. In SH phase, the source node cardinally establishes a confined route retrieval method if the residual power in-between node is truncated or the node has displaced far away. The proposed framework is executed in NS-2 and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost. Therefore, the proposed approach has attained 24.7%, 28.9%, 12.75% higher PDR, and 16.8%, 19.87%, and 13.7% higher residual energy than the existing methods like Self-Healing and Seamless Connectivity using Kalman Filter among IoT Networks (SH-SC-KF-IoT), Provenance aware run-time verification mechanism for self-healing IoT (PA-RVM-SH-IoT), and Fully Anonymous Routing Protocol and Self-healing Capacity in Unbalanced Sensor Networks (FARP-SC-USN) methods, respectively.

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications.

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"-a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Smart Agriculture Application Using Secured and Energy-Efficient IoT-Based WSN Framework

The use of wireless sensor networks (WSNs), in several sectors, including communication, agriculture, manufacturing, smart health, monitoring and surveillance is increasing in R&D. An IoT-based WSN in agricultural production has been effective in detecting yield conditions and automating agriculture precision by using multiple sensors. To collect data on crops, plants, temperature estimates and stickiness as well as to boost yields by making smart agriculture decisions, they are deployed in rural regions. Sensors, on the other hand, are constrained by their inability to handle, store, communicate, and process large amounts of data due to a lack of available resources. Additionally, the safety and security of the IoT-based agricultural sensors against damaging competitors are crucial factors, as is their efficacy. One idea put up in this article is to employ a WSN structure based on the Internet of Things (IoT) for smart agriculture. The selection of group leaders is also based on data collected by rural sensors and multi-rules choice capacity. A transmission link’s SNR (signal-to-noise ratio) is used to gauge the intensity of signals being sent over it in order to guarantee accurate and timely data transfer. In addition, the direct congruential generator is repeated in order to enable data flow from agricultural sensors to central stations (BS). Compared with previous arrangements, smart agriculture obtained an average of 13.5 percent in the organisation throughput, 38.5 percent in the parcel drop percentage, 13.5 percent in the organisation inactivity, and 16 percent in energy usage. Comparatively speaking, this is a huge step forward.

Optimal path selection using reinforcement learning based ant colony optimization algorithm in IoT-Based wireless sensor networks with 5G technology

The Internet of Things (IoTs) expanded quickly, giving rise to numerous services, apps, electronic devices with integrated sensors, and associated protocols, which are still being developed today. By enabling physical objects to communicate with each other and share important information while making decisions and carrying out their essential jobs, the IoTs enable them to see, hear, think, and execute crucial tasks. Wireless sensor networks (WSN), which act as the IoT's permanent layer, are essential for fifth-generation (5G) communications, which need the IoT to be considerably helped. A WSN comprises many sensor nodes that track and transmit data to the sink. Every round's data transmission ends at the sink (or base station). This work presents a Proximal Policy Optimization based Ant Colony Optimization (PPO-ACO) algorithm for optimal path selection in WSN. The proposed algorithm combines the strengths of both PPO with a reinforcement learning (RL) method, and ACO, a swarm intelligence method, to address the stochastic nature of the network and the complex trade-off between energy efficiency and security. The PPO component learns the policy for path selection based on the sampled rewards, while the ACO component updates the pheromone levels to guide the search toward the optimal path. Compared to the state-of-the-art, our simulation findings show that the suggested PPO-ACO algorithm performs better in terms of the number of active nodes. The average residual energy of the suggested algorithm decreases later than existing algorithms, indicating its higher efficiency.

Modified Rijndael Algorithm for Resource-Constrained IoT-Based Wireless Sensor Networks

INTRODUCTION: IoT devices are small, have limited battery and computing capabilities, and cannot tolerate complex encryption. OBJECTIVES: Many encryption algorithms struggle to strike a balance between security and complexity. Increasing the level of security necessitates increasing complexity, which leads to higher encryption and decryption times. This research aims to provide a novel encryption method, Modified Rijndael Algorithm that balances security and complexity. METHODS: The classic Rijndael encryption technique has been modified to make it lighter and more secure, suitable for resource-constrained IoT devices. RESULTS: These modifications include adding the novel pre-processing step SISA-Crossover based on the two-point crossover operation of genetic algorithms, removing the Sub Byte step, changing the shift column phase, and decreasing the rounds. CONCLUSION: The proposed technique provides speed and higher security in encryption and decryption, increasing efficiency and randomness while reducing encryption and decryption times by 21.06% and CPU consumption by 13.2% compared to the traditional method.

Designing of Real-time Communication Method to Monitor Water Quality using WSN Based on IOT

Data accuracy has always been the essential and foremost requirement of any communication. A Real-time Water quality-monitoring system (WQMS) needs high-level data accuracy to process the water perfectly for the desired usage in any specific purpose. Exponential growth in man-made processes, human activities, Industrialization, and economic growth along with depleting safe water resources has made the water pollution issue a foremost threat to human survival and human civilisation as a whole. The Conventional WQMS lacks in providing data accuracy while testing and analysing the water samples at sites due to improper data transmission, human intervention, instrument node working and calibration issues. Therefore, monitoring the quality of the water is essential with a prime focus on data accuracy through proper testing, data analysis and data transmission methods to provide real-time data accuracy. In this research work, an IOT-based wireless sensor network (WSN) is proposed that uses mesh networking to connect the sensor nodes and message queuing telemetry transfer (MQTT) protocol to send the acquired data to a cloud server ADDA Fruit IO. ESP32 standalone microcontroller with in-built Wi-Fi is used as a transceiver on sensor nodes and master nodes. As far as sensor and master node power is concerned, a self-adapting power generation system is incorporated using solar power and water energy harvesting techniques. The sensor nodes are calibrated as per WHO standards using deionized water and buffer capsules. Five random samples are collected from river water, pond water, Borewell water, R.O water and Municipal committee water to analyse the proposed system’s accuracy. The accuracy test and analysis is done using statistical tools on water sample measurements by the proposed sensor node, and the same is compared with the actual certified instrument, hardware manual-based measurement, Laboratory value check and transmitted value on Cloud Server. The proposed WQMS is designed to measure various WQM parameters i.e. Total Dissolve Solid (TDS), pH, Temperature and turbidity and to ensure data accuracy.

Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks.

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

An improved authentication and key management scheme in context of IoT-based wireless sensor network using ECC

Wireless sensor network (WSN) is used for sensing data/information from the nearby environment with the help of various spatially dispersed miniature sensor devices/nodes and forwarding the data to the direction of the base station which is connected to the network through the Internet for processing the collected data. Currently, Internet of Things (IoT) based smart computing applications are becoming popular in which WSN is used to run a process to output information from sensed data or events. However, these sensor nodes have less computation ability and low power capacity. In these resource-constrained WSNs, remote user authentication is a serious challenge that demands the attention of the researchers’ community. Several authentication protocols which address a variety of security concerns are present in the literature. Ghani et al. proposed an efficient authentication and key management scheme that claims to be resistant to known attacks. During the study of their scheme, we found that it is efficient and effective for protecting against a few attacks. However, it does not have the ability to counter several security threats like user impersonation attack, insider attack, database attack, and stolen smart card attack. To counter these aforesaid security loopholes, in this paper a new ECC integrated scheme suitable for IoT-based WSN with a higher level of protection is proposed. The informal security analysis using mathematical approaches and the formal security analysis using a well-accepted AVISPA simulator and BAN logic ensures that the suggested work is highly secure from all the relevant threats. Moreover, the performance analysis and comparison of the proposed scheme with the most recent relevant schemes proves that our scheme is much more efficient in terms of computation, communication, and storage overheads than all the available schemes in the literature.

Intermittent Fault Diagnosis of Split-Star Networks and its Applications

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability <inline-formula><tex-math notation="LaTeX">$t_{I}^{PMC}(S_{n}^{2})$</tex-math></inline-formula> of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional split-star network <inline-formula><tex-math notation="LaTeX">$S_{n}^{2}$</tex-math></inline-formula> under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional split-star network <inline-formula><tex-math notation="LaTeX">$S_{n}^{2}$</tex-math></inline-formula> under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN.