Blockchain-enabled Internet Of Things Research Articles

Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning

Artificial intelligence (AI)-enabled Internet of Things (IoT) has attracted great interests. The accuracy of data training model in AI is vital for further development of IoT. In addition, with the increasing number of intelligent IoT devices, the amounts of data available for transmission, learning and training can lead to serious communication burdens and data reliability issues. In order to address these issues, we study novel network architectures in future 6G networks to support the intelligent IoT. Moreover, inspired by the collective learning of humans, we introduce and adopt a novel method named as collective reinforcement learning (CRL) in the intelligent IoT to realize the sharing of learning and training results. To ensure security and privacy, as well as improve computing efficiency, blockchain, mobile edge computing (MEC) and cloud computing are applied to protect data security and enrich computing resources. On this basis, we formulate an optimization problem in the intelligent IoT based on the proposed framework to optimize transmission latency and energy consumption. Simulation results demonstrate that the system performance has improved significantly. At last, some research challenges and open issues are pointed out to the intelligent IoT in future networks.

An Efficient Verifiable Searchable Encryption Scheme With Aggregating Authorization for Blockchain-Enabled IoT

Blockchain-enabled Internet of Things (IoT) provides a secure sharing of data and resources to the various miners of the IoT network, removes centralized control, and can overcome part of the existing challenges in traditional IoT. However, the IoT ecosystem faces some great challenges in terms of security, such as privacy leaking, eavesdropping, and so on, which seriously impede the deployment of the IoT ecosystem. Verifiable searchable encryption (SE) can make data owners (DOs) in the IoT dispel concerns about privacy and make data users (DUs) believe they get correct search results. For the blockchain-enabled IoT, this article proposes an efficient verification SE scheme with aggregation authorization and trusted revocation. With this scheme, DOs are willing to share their data to DUs for reward in a secure, efficient, and trusted way. For the DU, the DO can generate an aggregating key of a subset of encrypted documents, and then the DU has the privilege to search these documents and to verify the search results. By utilizing the trusted execution environment, the DO can revoke the search privilege of the DU. We present the analysis to show our proposed scheme achieves confidentiality, soundness, and fair payment at the same time. With the performance evaluations, we prove our proposal practical for blockchain-enabled IoT in terms of computational and smart contracts overhead.

A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model

The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration.

Decentralized Consensus Blockchain and IPFS-Based Data Aggregation for Efficient Data Storage Scheme

By the development and advancement of blockchain technique, Internet of Things (IoT) proliferation driven devices and the application of blockchain-enabled IoT alter the view and operating infrastructure of the smart networks. The blockchain is responsible for supporting decentralized systems and offers secured means of authentication, management, and access to IoT system thereby deploying smart contracts offered by Ethereum. The increasing demand and the blockchain expansion generate huge volume of sensitive data. The growing demand and expansion of blockchain-IoT systems is generating large volume of sensitive data. Furthermore, distributed denial-of-service (DDoS) attacks are regarded as the most promising threats for smart contracts in the blockchain-based systems. Therefore, there is a need to detect and classify the attack type and the data should be stored in server more securely with the use of blockchain and data aggregation method. For this purpose, this presented technique aims at introducing decentralized consensus blockchain and Interplanetary file system (IPFS) based data aggregation for effective classification and data storage. The attack is detected using meta-hyperparameter random forest (MHP-RF) classifier. Once the attack is detected, the transaction information is stored in server securely by means of smart contract-based blockchain system. The transaction handling stage classifies the transaction type as normal or abnormal one which then followed by execution of business logic by smart contract thereby appending the transaction of blockchain in the network cloud. The consensus blockchain technique is employed with the use of PoW-enabled scheme integrated with Elgamal-based data aggregation. Therefore, the system security is improved and the intrusion is prevented greatly. The performance analysis of the system is analyzed in terms of accuracy, precision, recall, F-score, Encryption time, decryption time, execution time, and space complexity. The attained outcomes are compared with traditional approaches to prove the effectiveness of proposed strategy. The proposed system is said to be effective in time consumption, classifier performance, and in overcoming space complexity issues.

FIDChain: Federated Intrusion Detection System for Blockchain-Enabled IoT Healthcare Applications.

Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.

Guest Editorial Special Issue on Blockchain-Enabled Internet of Things

Blockchain, as a constantly evolving Peer-to-Peer (P2P) distributed ledger technology with characteristics, such as decentralization, security, interoperation, and trust establishment, can potentially lower the costs of the underpinning infrastructure and maintenance compared with conventional centralized systems. Consequently, the distributed structure of blockchain is naturally suitable for the Internet of Things (IoT), which can be used to build secure and trusted IoT. Despite the advances made in applying blockchain to IoT in the past few years, some research challenges remain to be addressed, including the poor scalability, heterogeneous IoT devices, and the impact of integration on network performance.

Optimized Bandwidth Allocation for MEC Server in Blockchain-Enabled IoT Networks

Powered by the development of the fifth-generation mobile communication technology (5G), the Internet of things (IoT) has been widely applied in people’s life. Due to the limitation of storage and computing power, the data transmission of IoT devices faces challenges in terms of security and privacy. Therefore, many researchers provide the conjunction of blockchain and mobile edge computing (MEC) to make up for the lack of computing and security. MEC can meet the storage and computing requirements for IoT devices. Blockchain can provide a decentralized, antitamper solution that can help devices overcome security deficiencies, whereas the speed of blockchain communication is not fast enough because of the consensus mechanism. In this article, we focus on the permissioned blockchain and propose an optimized bandwidth allocation algorithm to promote the performance of consensus communication. The algorithm contains an In-Network control ideology and supports deployment on MEC servers. Deep reinforcement learning (DRL) is employed to perform the computation of available bandwidth in our scheme. We implement a prototype system in the testbed and perform a simulation, and the results show the advantages compared with the current widely used algorithm. By applying our method, the Internet of things devices can transmit data safely and efficiently.

A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network

The Internet of Things (IoT) is emerging as a new technology for the development of various critical applications. However, these applications are still working on centralized storage architecture and have various key challenges like privacy, security, and single point of failure. Recently, the blockchain technology has emerged as a backbone for the IoT-based application development. The blockchain can be leveraged to solve privacy, security, and single point of failure (third-part dependency) issues of IoT applications. The integration of blockchain with IoT can benefit both individual and society. However, 2017 Distributed Denial of Service (DDoS) attack on mining pool exposed the critical fault-lines among blockchain-enabled IoT network. Moreover, this application generates huge amount of data. Machine Learning (ML) gives complete autonomy in big data analysis, capabilities of decision making and therefore is used as an analytical tool. Thus, in order to address above challenges, this paper proposes a novel distributed Intrusion Detection System (IDS) using fog computing to detect DDoS attacks against mining pool in blockchain-enabled IoT Network. The performance is evaluated by training Random Forest (RF) and an optimized gradient tree boosting system (XGBoost) on distributed fog nodes. The proposed model effectiveness is assessed using an actual IoT-based dataset i.e., BoT-IoT, which includes most recent attacks found in blockchain-enabled IoT network. The results indicate, for binary attack-detection XGBoost outperforms whereas for multi-attack detection Random Forest outperforms. Overall on distributed fog nodes RF takes less time for training and testing compared to XGBoost.

Computation Offloading in Untrusted MEC-Aided Mobile Blockchain IoT Systems

Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

IoT Big Data provenance scheme using blockchain on Hadoop ecosystem

The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).

Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey

Industry 4.0 connects the latest technologies such as cloud computing, Internet of things (IoT), machine learning and artificial intelligence (ML/AI), and blockchain to provide more automation in the industrial process and also bridges the gap between the physical and digital worlds through the cyber-physical system. The inherent feature of IoT devices creates the industry to smart industry (referred to as industrial IoT, i.e., IIoT) through its data-driven decision policies. However, several challenges such as decentralization, security and privacy vulnerability, single point of failure (SPOF), and trust issues exist in the IoT system. Blockchain is one of the promising technologies that can bring about opportunities for addressing the challenges of IoT systems. In this article, we have investigated the integration of IoT with blockchain technology and provided an in-depth study of the blockchain-enabled IoT and IIoT systems. The state-of-the-art research is categorized into data storage and management technique, big data and cloud computing technique (finance and data auditing), and industrial sectors (supply chain, energy, and healthcare sector). The insightful discussion based on the different categories is also presented in the paper. In particular, first, we introduce the IoT and IIoT and then discuss the need for smart contracts in IoT and IIoT systems. Next, we concentrate on the convergence of blockchain and IoT with state-of-the-art research. In addition, this article also provides the open and future research directions towards this era with the highlighted observations.

A Novel Optimal Selection Algorithm for Agricultural Trade Export in Blockchain-Enabled Internet of Things

With the maturity of modern science and technology, such as networks and computers, Internet of Things has been widely used in various fields of industry, opening up a new situation for the development of the industry and creating a broader development platform. This paper systematically analyzes the characteristics and changes of the commodity structure, regional structure, market structure, and main structure of export management of agricultural products. The proposed algorithm uses computer technology, network technology, and remote communication technology, electronic, digital, and network—the entire business process of business. The empirical analysis shows that the structure of agricultural product export trade reflects the endowment and comparative advantage of agricultural resources in China. The proposed blockchain technology supports and guides agricultural export enterprises to develop their own brands, strengthen the quality and safety management of agricultural products, and innovate and expand the policy support system for optimizing the structure of agricultural product export trade. By optimizing the export mode of agricultural trade, we can achieve the purpose of increasing the export volume of agricultural products and the total value of trade exports. The experimental results show the effectiveness of the proposed algorithm, which can greatly improve the volume of trade exports and the total value of trade exports.

Energy-efficient and Blockchain-enabled Model for Internet of Things (IoT) in Smart Cities

Wireless sensor networks (WSNs) and Internet of Things (IoT) have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities. The data generated from these sensors are used by smart cities to strengthen their infrastructure, utilities, and public services. WSNs are suitable for long periods of data acquisition in smart cities. To make the networks of smart cities more reliable for sensitive information, the blockchain mechanism has been proposed. The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources; leading to extending the network lifetime of sensors. In this paper, a linear network coding (LNC) for WSNs with blockchain-enabled IoT devices has been proposed. The consumption of energy is reduced for each node by applying LNC. The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models. Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes, packet delivery ratio, throughput, and the optimized residual energy compared to other current techniques.

Application-Oriented Block Generation for Consortium Blockchain-Based IoT Systems With Dynamic Device Management

Due to its salient features, such as immutability and auditability, blockchain is becoming more integrated into the Internet of Things (IoT) for enhancing security and developing a decentralized IoT framework. However, different IoT applications require different transaction processing performance, which brings challenges to the convergence of blockchains in IoT. Moreover, the membership of a distributed IoT system may fluctuate when an IoT device joins or leaves the system. The dynamic nature of IoT systems also introduces new challenges for device management. Accordingly, we propose an application-oriented block generation (AOBG) scheme for blockchain-enabled IoT with dynamic device management and conditional traceability. Specifically, we first construct a framework for a consortium blockchain-based IoT system, including structures for application-oriented transactions and blocks, and consensus mechanism. We present different miners, respectively, for processing urgent and ordinary transactions adaptively with applications. Then, an AOBG protocol is proposed for this framework based on group signature. The group signature is used to achieve anonymity, traceability, and nonframeability. Combining time-bound keys in group signature with node accounts in blockchain, the proposed scheme can realize efficient transaction verification, dynamic device management, conditional traceability with data security, and privacy preservation. Extensive experiments demonstrate high efficiency of the proposed scheme.

An algorithm based on markov chain to improve edge cache hit ratio for blockchain-enabled IoT

Reasonable allocation of storage and computing resources is the basis of building big data system. With the development of IoT (Internet of Things), more data will be brought. A three-layer architecture includes smart devices layer, edge cloud layer and blockchain-based distributed cloud layer. Blockchain is used in IoT for building a distributed decentralize P2P architecture to deal with the secure issue while edge computing deals with increasing volume of data. Edge caching is one of the important application scenarios. In order to allocate edge cache resources reasonably, to improve the quality of service and to reduce the waste of bandwidth resources, this paper proposes a content selection algorithm of edge cache nodes. The algorithm adopts markov chain model, improves the utilization of cache space and reduces the content transmission delay. The hierarchical caching strategy is adopted and the secondary cache stores slides of contents to expand the coverage of cached content and to reduce user waiting time. Regional node cooperation is adopted to expand the cache space and to support the regional preference of cache content. Compared with the classical substitution algorithm, simulation results show that the algorithm in this paper has higher cache hit ratio and higher space utilization.

Proof of X-repute blockchain consensus protocol for IoT systems

Recently, blockchain technology has been used to address the security issues of Internet of things (IoT) applications. However, some issues particular to blockchain should be solved to meet the security requirements of IoT systems. The core of blockchain technology is distributed computing, along with the collaboration mechanism of group trust under the distributed computing environment, which can solve the scalability, collaboration ability, trust relationship, and security protection challenges faced by the IoT. The existing blockchain consensus protocol can only solve the trust cooperation problem with limited credibility. Although distributed trust relationship management has advantages over centralized trust relationship management, several risks exist. Here, the most critical issue is the credibility of the blockchain consensus protocol. Based on the characteristics of the traditional public chain, we propose a repute-based consensus protocol for blockchain-enabled IoT systems. In the protocol, two methods are designed to enable the blockchain system to reach a consensus rapidly and safely. The repute rewards and punishments method settles the repute values of nodes; nodes with satisfactory behavior receive repute rewards. The repute mining method proposes repute requirements for consensus nodes; nodes with high repute value produce blocks more easily. Security analysis has been conducted using a theory model and experimental evaluation. The establishment of our repute system can improve the consensus protocol, resisting stronger attacks, and giving several users with lower computing power a greater opportunity to participate in consensus. The experimental results show that the repute-based consensus protocol has advantages in terms of security and that its resistance capability against attacks is improved.

BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence

In the recent year, Internet of Things (IoT) is industrializing in several real-world applications such as smart transportation, smart city to make human life reliable. With the increasing industrialization in IoT, an excessive amount of sensing data is producing from various sensors devices in the Industrial IoT. To analyzes of big data, Artificial Intelligence (AI) plays a significant role as a strong analytic tool and delivers a scalable and accurate analysis of data in real-time. However, the design and development of a useful big data analysis tool using AI have some challenges, such as centralized architecture, security, and privacy, resource constraints, lack of enough training data. Conversely, as an emerging technology, Blockchain supports a decentralized architecture. It provides a secure sharing of data and resources to the various nodes of the IoT network is encouraged to remove centralized control and can overcome the existing challenges in AI. The main goal of our research is to design and develop an IoT architecture with blockchain and AI to support an effective big data analysis. In this paper, we propose a Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence that provides an efficient way of converging blockchain and AI for IoT with current state-of-the-art techniques and applications. We evaluate the proposed architecture and categorized into two parts: qualitative analysis and quantitative analysis. In qualitative evaluation, we describe how to use AI and Blockchain in IoT applications with “AI-driven Blockchain” and “Blockchain-driven AI.” In quantitative analysis, we present a performance evaluation of the BlockIoTIntelligence architecture to compare existing researches on device, fog, edge and cloud intelligence according to some parameters such as accuracy, latency, security and privacy, computational complexity and energy cost in IoT applications. The evaluation results show that the proposed architecture performance over the existing IoT architectures and mitigate the current challenges.

Credit-Based Payments for Fast Computing Resource Trading in Edge-Assisted Internet of Things

The introduction of edge computing into blockchain-enabled Internet of Things (IoT) for offloading computational tasks is attracting increasing attention. Computing resource trading unavoidably happens in edge-assisted IoT. However, efficient computing resource trading cannot be achieved because of the “cold start” and “long return” problems. To address these challenges, we propose to use a credit-based payment for fast computing resource trading in edge-assisted blockchain-enabled IoT; therefore, the IoT nodes can finish fast payment and frequent trading by borrowing resource coins from other IoT nodes based on their credit values. In our resource-coin loan problem, we propose an iterative double-auction-based algorithm, where a broker is introduced to solve the loan allocation problem and to determine the size of the loan each lender would provide to each borrower. Furthermore, the broker enforces specific loan pricing rules to induce the borrowers and lenders to bid truthfully. Then, the hidden privacy information could be extracted to achieve the optimal resource-coin allocation and loan pricing. The proposed algorithm can maximize the economic benefits while protecting privacy. Simulations showed that the proposed algorithm can maximize social welfare. In addition, we compared the proposed algorithm with the credit-bank-based method in terms of the satisfaction function and payments. The experimental results demonstrated that the proposed algorithm was individually rational, truthful, and budget-balanced.

Blockchain-Enabled Wireless Internet of Things: Performance Analysis and Optimal Communication Node Deployment

Blockchain has shown a great potential in Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without involvement of any third party. Understanding the relationship between communication and blockchain as well as the performance constraints posing on the counterparts can facilitate designing a dedicated blockchain-enabled IoT systems. In this paper, we establish an analytical model for the blockchain-enabled wireless IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution in spatial domain and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Based on the system model and performance analysis, we design an algorithm to determine the optimal full function node deployment for blockchain system under the criterion of maximizing transaction throughput. Finally, the security performance is analyzed in the proposed networks with three typical attacks. Solutions such as physical layer security are presented and discussed to keep the system secure under these attacks. Numerical results validate the accuracy of our theoretical analysis and optimal node deployment algorithm.