Data Transmission Stage Research Articles

Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.

Preserving Privacy in Industrial IoT: A Machine Learning Framework Enhanced by Poly1305 Encryption

Abstract: The convergence of Industrial Internet of Things (IoT) systems and machine learning has ushered in new paradigms for predictive maintenance, anomaly detection, and quality control in industrial sectors. Though, the sensitive nature of data generated by IoT devices raises significant privacy and security concerns. To address these challenges, this review paper presents a thorough examination of safeguarding privacy machine learning frameworks in the context of Industrial IoT environments, leveraging the robust Poly1305 encryption algorithm. This paper commences by surveying the landscape of Industrial IoT applications, highlighting the urgency of safeguarding sensitive data without compromising the potential insights gleaned from machine learning models. It then delves into the intricacies of Poly1305 encryption, elucidating its strengths in ensuring data integrity, authenticity, and confidentiality. The integration of Poly1305 encryption within machine learning pipelines is explored, considering encryption at data collection, transmission, storage, and analysis stages. The review systematically analyses a spectrum of data learning models deployed in Industrial IoT scenarios, spanning predictive maintenance to quality enhancement. Each model's compatibility with Poly1305 encryption is assessed, along with performance implications and security guarantees. The paper navigates through key obstacles including key management, computational overhead, and interoperability concerns. In summation, this review paper not only serves as a comprehensive guide for researchers and practitioners in the realm of Industrial IoT and machine learning but also contributes to the ongoing discourse on safeguarding data privacy while harnessing the transformative power of data-driven insights

Joint CFO and Channel Estimation for RIS-Aided Multi-User Massive MIMO Systems

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel estimation performance. This becomes more critical in RIS-aided communications, as the RIS phases are adjusted based on the channel estimates and even a small channel estimation error leads to a significant performance loss. Motivated by this, we propose a joint CFO and channel estimation method for RIS-aided multi-user massive multiple-input multiple-output (MIMO) systems. Our proposed pilot structure makes it possible to accurately estimate the CFOs without multi-user interference (MUI), using the same pilot resources for both CFO estimation and channel estimation. For optimization of the RIS phase shifts at the data transmission stage, we propose a projected gradient method (PGM) which achieves the same performance as a more computationally demanding grid search technique while having a significantly lower computational complexity. Simulation results demonstrate that the proposed method provides an improvement in the normalized mean-square error (NMSE) of channel es-timation as well as in the bit error rate (BER) performance. Moreover, analyzing the computational complexity and the pilot resource efficiency of the proposed method, we demonstrate that the proposed estimation approach requires low computational load and no extra cost in pilot overhead.

Spatial Sparsity-Based Pilot Attack Detection and Transmission Countermeasure for Cell-Free Massive MIMO System

In this article, we investigate the security performance of a cell-free (CF) massive multiple-input–multiple-output (MIMO) system that is composed of a large number of randomly distributed access points (APs) and multiple users. There may be an eavesdropper who launches an active pilot attack during uplink channel estimation stage to eavesdrop on information in data transmission stage, which threaten the security of the system. A spatial sparsity-based pilot attack detection (SS-based-PAD) method is proposed in this study to determine whether there is an active eavesdropper. Different data transmission schemes were developed according to the detection results. In the presence of an eavesdropper, rate maximization power allocation (SRM-PA) algorithm is proposed to maximize the security of the eavesdropped user. On the contrary, achievable rate equalization power allocation (ARE-PA) algorithm is proposed to provide fair services for all legitimate users. Moreover, an AP selection scheme and corresponding conjugate beamforming are used to enhance the security performance of the system and mitigate the impact of a pilot spoofing attack.

Joint Localization and Communication Study for Intelligent Reflecting Surface Aided Wireless Communication System

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

Pilot spoofing detection for massive MIMO mmWave communication systems with a cooperative relay

In massive millimeter-wave (mmWave) multiple-input–multiple-output (MIMO) systems, eavesdroppers may transmit the identical pilot sequences as legitimate users to manipulate channel estimation in pilot phase and afterwards intercept the transmitted information in data transmission stage, constituting a severe attack for secure communications. In this paper, for a three-terminal massive mmWave MIMO system with a cooperative relay, the achievable secrecy rate under the presence of a pilot spoofing attack is investigated, and then an accurate detection scheme is proposed. Specifically, in the first slot of the uplink pilot stage, the full-duplex relay sends pilot sequence to the base station simultaneously when it receives the transmitted pilot signal from the user, through which the CSI from the relay to the base station is obtained. In the second slot, after the received pilot signal has been forwarded from the cooperative relay, the equivalent CSI from the base station to the user is obtained, and the joint CSI from the user/attacker to the relay is derived, based on which the likelihood ratio method is exploited to detect the potential pilot spoofing attack. Finally, numerical simulation is presented to show the performance of the proposed scheme in terms of miss detection and false detection events.

Secure Two-Way Transmission via Autonomous Reconfigurable Intelligent Surface

In this letter, we propose a secure two-way communication scheme using an autonomous reconfigurable intelligent surface (RIS) to protect the information exchange between two legitimate nodes, both equipped with full-duplex radios, against passive eavesdropping. The proposed scheme consists of two stages, i.e., the secret channel estimation stage and the secure two-way data transmission stage. In stage 1, we use the Takagi decomposition to obtain an estimation of the secret channel state information (CSI). In stage 2, the secrecy of the two-way transmission is protected by the randomness of the estimated secret CSI. The reflection patterns are determined at the RIS autonomously and are carefully designed such that they become transparent at the receiver side. Moreover, the RIS does not need to inform the legitimate nodes of the reflection patterns. Without any pre-shared information, the proposed scheme prohibits eavesdropping even if the eavesdropper is close to the transmitter, the receiver, or the RIS. The secret CSI is updated in each of the transmission, which reduces the overhead and enables the proposed scheme to apply to both stable and varying channels. Two transceivers are designed to achieve different balances between efficiency and complexity. Numerical results verify the effectiveness of the proposed scheme.

Research on the digital economy promoting the high-quality development of trade in the central and western regions under the background of big data technology

In order to improve the effect of the digital economy in promoting the development of trade in the central and western regions, this paper combines the intelligent digital economy identification algorithm to construct a trade analysis model, and proposes a collaborative cache strategy in the hybrid mobile opportunistic network scenario. By placing the AP access point at the center of the network, the disadvantage of relying only on node mobility for opportunistic data access is effectively compensated. For different communication scenarios, this paper divides the data transmission stage into D2D communication stage and D2A communication stage according to different relay choices in the data transmission process. Moreover, this paper conducts modeling analysis on them respectively, and discusses the access delay of nodes to data in the network. Finally, this paper verifies that the algorithm in this paper has a certain effect through experimental research, and puts forward suggestions for promoting the high-quality development of trade in the central and western regions.

An area autonomous routing protocol based on multi-objective optimization methods for field observation instrument network

The cold and arid regions of China occupy a large proportion of the total land area, are rich in resources and have a prominent strategic position, but their fragile ecological environment seriously affects the information collection and lifecycle of the field observation instrument network (FOIN), which affects the in-depth research for cold and arid regions. To balance the energy consumption and improve performance of the FOIN, an area autonomous routing protocol based on multi-objective optimization methods for FOIN (FOI-MOC) was proposed Firstly, in the network preparation stage, the FOI-MOC algorithm calculates the number of optimal cluster heads, evenly partitions for FOIN, and allocates the number of regional cluster heads. Then, in the cluster establishment stage, the different objective functions are constructed based on the residual energy, distance, and density of nodes in their respective regions. Multi-objective optimization algorithms, NSGAII and PSO are utilized to address the Pareto optimal solution set The Pareto optimal solution set is scored by dynamically assigning weights to each objective function through the entropy method, and final cluster heads are elected for each region. Finally, in the data transmission stage, single-hop transmission is adopted within clusters, and single-hop or multi-hop transmission is employed between clusters according to the distance between cluster head and base station. The experimental results indicate that the developed protocol based on multi-objective optimization methods can efficiently balance network energy consumption and prolong network lifecycle.

LEACH Clustering Routing Protocol Based on Balanced Energy Consumption

<p>Faced with the problems of unbalanced energy consumption (EC) and short lifetime of nodes in Wireless Sensor Networks (WSN), a Low Energy Adaptive Clustering Hierarchy (LEACH) clustering routing protocol based on energy balance, namely LEACH-EB (LEACH Based on Energy Balance) protocol was proposed. At the initial selection stage, the nodes which are close to the base station (BS) with great remaining energy and many neighbor nodes are selected as the cluster heads (CHs); then, the non-CH nodes enter the clusters which have the least costs based on the strength and remaining energy of the communication signals between themselves and different CHs. At the data transmission stage, if the CH which sends the information is one hop away from BS, the CH needs to select a neighbor CH with the largest forwarding probability as the next hop relay node based on the remaining energy of each neighbor CH, the number of nodes in the cluster, and the distance from BS. The selected neighbor CH continues to determine the next hop in the above manner until the data is successfully sent to BS. Simulation tests show that LEACH-EB protocol can receive more data and extend the network life cycle by 60%, 43.1%, and 13.36% compared with LEACH, LEACH-C, and FIGWO, respectively. </p> <p> </p>

Secrecy Enhancement for SSK-Based Communications in Wireless Sensing Systems

Reliability and security are the main concerns in modern industrial wireless sensing systems because of the nature property of the industrial wireless sensing environment. Therefore, how to enhance the secrecy and make the industrial wireless sensing systems smart attract a lot of attention. In order to address the secrecy problem in industrial wireless sensing systems, this article proposes a secrecy enhancement mechanism based on space shift keying (SSK) communication in industrial wireless sensing systems where a transmitting node, a receiving node, and an eavesdropper are considered. To prevent the valid information from being attacked by the eavesdropper, the technologies of antenna selection (AS) and the artificial noise (AN) are jointly used in the proposed scheme to enhance the physical layer security (PLS). Eventually, the designed AN is canceled at the receiving node and remains at the eavesdropper. In addition, two kinds of AS methods, namely, signal-to-leakage-noise ratio (SLNR)-based method and Euclidean distance AS (EDAS)-based method are proposed for the proposed secrecy enhancement scheme. Specifically, the SLNR and EDAS methods are designed for the known eavesdropper channel case and the unknown eavesdropper channel case, respectively. Moreover, to make the scenario practical, the channel estimation error is fully considered in the sensing data transmission stage since the perfect channel state information (CSI) is rarely available in the actual sensing environment. The results from simulations demonstrate that the proposed scheme can enhance the system performances of secrecy rate (SR) and bit error rate (BER) when compared with the traditional random selection method.

Routing Protocol for Wireless Sensor Networks Based on Archimedes Optimization Algorithm

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime.

Enabling LPWAN Massive Access: Grant-Free Random Access with Massive MIMO

With the rapid growth of the number of Internet of Things (IoT) devices, the support for massive access is becoming vital for future low power wide area network (LPWAN). Up to now, LoRa™ and NB-IoT are the two leading LPWAN technologies that use very distinct random access (RA) schemes, that is, grant-free RA scheme and grant-based RA scheme, respectively. The low-overhead grant-free RA of LoRa makes it suitable for light traffic load, while the grant-based RA scheme of NB-IoT is more suitable for moderate traffic load as it could provide high reliability in data-transmission stage for the active terminals without collisions. However, when facing massive access and high traffic load, both LoRa and NB-IoT are unable to provide high throughput and high success rate with low signaling overhead. To address the challenge of massive access, grant-free RA with massive multiple-input and multiple-output (MGFRA) is being considered as an alternative technique. In this article, we first introduce MGFRA and present simplified analysis and simulations to demonstrate its effectiveness. Then, we envision the future LPWAN requirements on RA and compare the performance of the existing leading LPWAN technologies with MGFRA. Finally, a proof-of-concept prototype of LPWAN based on MGFRA, called as TurMass™, is introduced and some experimental results are provided to demonstrate its effectiveness in realistic wireless environments.

Model construction of material distribution system based on digital twin

Aiming at the problems of poor periodicity of workshop material distribution, difficult prediction of station material demand time node, and redundant distribution route, this paper proposes a model construction method of material distribution system based on digital twin. Build a material distribution control mode based on digital twin, and establish a full-cycle material distribution mechanism on this basis to comprehensively optimize the distribution cycle from the material preparation stage, dynamic replenishment stage, and data transmission stage of adjacent distribution cycles. Build the digital twin model of material distribution system, establish the material demand time node dynamic prediction operation mechanism based on LSTM, accurately predict the station material demand time node, establish the material distribution route optimization model with the lowest total cost, and optimize the AGV route. Finally, it is applied to the asynchronous line workshop of A enterprise to verify the effectiveness of this method.

Game Theory and Coverage Optimization Based Multihop Routing Protocol for Network Lifetime in Wireless Sensor Networks

Wireless sensor networks (WSNs) are self-organizing monitoring networks with a large number of randomly deployed micro sensor nodes to collect various physical information so as to realize tasks such as intelligent perception, efficient control and decision-making. However, WSNs nodes are powered by batteries, so they will run out of energy after a certain time. This energy limitation will greatly constrain the network performance like network lifetime and energy efficiency. In this study, for the purpose of prolonging network lifetime, we proposed a multi-hop routing protocol based on game theory and coverage optimization (MRP-GTCO). Briefly, in the stage of setup, two innovational strategies including clustering game with penalty function and cluster head coverage set were designed to realize the uniformity of cluster head distribution and improve the rationality of cluster head election. In the data transmission stage, we firstly derived the applicable conditions theorem of inter-cluster multi-hop routing. Based on this, a novel multi-hop path selection algorithm related to residual energy and node degree was proposed to provide an energy-efficient data transmission path. The simulation results showed that MRP-GTCO protocol can effectively reduce the network energy consumption and extend the network lifetime 159.22%, 50.76% and 16.46% compared with LGCA, RLEACH and ECAGT protocols.

A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks

The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs the K-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.

Construction of an Innovative Development Model of Intelligent Media under the Coverage of a Wireless Sensor Network

A distributed network composed of sensor nodes with limited computing and communication capabilities is a wireless sensor network, which is a system that can autonomously and intelligently complete target tasks according to the surrounding environment. With its wide application in the fields of smart medical care, smart home, networks of vehicles, and smart media, its communication performance requirements are also increasing. According to the needs of different application scenarios, the first key problem to be solved in wireless sensor networks is what optimization coverage strategy to adopt, which will have a direct impact on the optimal allocation of very limited resources such as node energy, communication bandwidth, computing power, and other network services’ quality. Nodes adopt a probability-based joint perception model, and its algorithm is a node scheduling mechanism based on the connected dominating set to construct a tree. Each node sets the waiting time and becomes a candidate node priority according to the remaining energy and the distance from the parent node. In this paper, the issue of energy fairness is considered in heterogeneous wireless sensor networks. In order to prevent the occurrence of “energy holes,” this paper proposes a node deployment model similar to cellular networks. By deploying heterogeneous sensor nodes in this way, energy saving can be achieved. Based on this model, game theory is used to simulate the data packet transmission process between sensor nodes, an energy consumption mechanism suitable for each stage of data transmission is proposed, and Nash equilibrium is finally obtained by rationally designing the measurement function. The node deployment scheme proposed in this paper not only balances the energy consumption of the network but also prolongs the life cycle of the network. Aiming at the three-dimensional (3D) space environment, this paper studies a node deployment mechanism that guarantees connectivity and has a perceptual coverage degree of k with the help of the Reuleaux tetrahedron model. In this paper, a node deployment strategy is proposed and compared with the node deployment algorithm for 3D sensor network based on truncated octahedron. The simulation experiment results show that the wireless sensor network with artificial intelligence can not only optimize the production process of intelligent media but also improve each link of the media industry chain and further catalyze the development of new formats in the media industry. The experimental results also show that the algorithm can effectively meet the requirements of sensing coverage and connectivity coverage, the number of working nodes is small, the network life cycle is significantly prolonged, and the overall energy consumption of the network is reduced. The study results of this paper provide a reference for follow-up research on the construction of innovative development model of intelligent media under the coverage of wireless sensor network.

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Numerous Internet of Things (IoT) systems produce massive volumes of information that must be handled and answered in a quite short period. The growing energy usage related to the migration of data into the cloud is one of the biggest problems. Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time, increase security, and reduce the congestion of networks. Therefore, in this paper, Optimized Energy Efficient Strategy (OEES) has been proposed for extracting, distributing, evaluating the data on the edge devices. In the initial stage of OEES, before the transmission state, the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system. The initial stage is followed by the reconstructing and the processing state. The processed data is transmitted to the nodes through controlled deep learning techniques. The entire stage of data collection, transmission and data reduction between edge devices uses less energy. The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy. Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme. Predictive accuracy is 97.5 percent, data performance rate was 97.65 percent, and execution time is 14.49 ms.

RETRACTED ARTICLE: IoT-based smart environment using intelligent intrusion detection system

One of the most basic characteristic features of every smart device in a network based on the Internet of Things (IoT) is to gather a larger set of data that has been created and then transfer the gathered data to the destination/receiver server through the internet. Thus, IoT-based networks are most vulnerable to simple or complex attacks that need to be identified in the early stage of data transmission for saving the network from these malicious attacks. The chief goal of the proposed work is to design and form the intelligent intrusion detection system (I-IDS) using the machine learning models such that the attacks can be identified in the IoT network. The model is built considering the normal and malicious attacks on the data that are generated in IoT smart environment. To simulate such a model, a testbed is built where a wireless router, a DHT11 sensor, and a node MCU are being used during the design phase. An attacker or adversarial system is built to perform poisoning and sniffing attacks using a laptop system. The node captures the sensor values and transmits the data to the ThinkSpeak platform, during the normal phase via the wireless gateway, and in the attack phase, the malicious attacker interprets the data, modifies it while transmitting from node to the ThinkSpeak server. Thus, the attack called Man-In-The-Middle (MITM) is performed and classified as abnormal data. Various machine learning algorithms are performed on the data, and finally, the results obtained using a probabilistic model called as Markov model have a high performance evaluated based on the I-IDS IoT network. The results obtained during the experimental analysis show that the Markov model has obtained a 100% detection rate and 19% of false alarm rate (FAR) with high precision and low error rate. The algorithms such as naïve Bayes classifier, support vector machine (SVM), decision tree, and Adaboost are considered in comparison with the Markov model. The optimal solution is obtained concerning other evaluation metrics like sensitivity, F1, and true-positive rate (TPR). Therefore, the integrated network of IoT-WSN with its performance metrics is tabulated to show the potentials of securing a network system. Additionally, the proposed work gives a high level of security for IoT smart environment as compared with the other machine learning algorithms using the novel technique of intelligent IDS.

A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

With the rapid development of the Internet of Things (IoT), wearable devices have become ubiquitous and interconnected in daily lives. Because wearable devices collect, transmit, and monitor humans’ physiological signals, data privacy should be a concern, as well as fully protected, throughout the whole process. However, the existing privacy protection methods are insufficient. In this paper, we propose a practical privacy-preserving mechanism for physiological signals collected by intelligent wearable devices. In the data acquisition and transmission stage, we employed existing asymmetry encryption-based methods. In the data publishing stage, we proposed a new model based on the combination and optimization of k-anonymity and differential privacy. An entropy-based personalized k-anonymity algorithm is proposed to improve the performance on processing the static and long-term data. Moreover, we use the symmetry of differential privacy and propose the temporal differential privacy mechanism for real-time data to suppress the privacy leakage while updating data. It is proved theoretically that the combination of the two algorithms is reasonable. Finally, we use smart bracelets as an example to verify the performance of our mechanism. The experiment results show that personalized k-anonymity improves up to 6.25% in terms of security index compared with traditional k-anonymity, and the grouping results are more centralized. Moreover, temporal differential privacy effectively reduces the amount of information exposed, which protects the privacy of IoT-based users.