In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which currently hinder their widespread adoption. This paper introduces HybridChain that combines the advantages of sharded blockchain and DAG distributed ledger, and a consensus algorithm that leverages decentralized learning. Our approach involves validators exchanging perceptions as votes to assess potential conflicts between transactions and the witness set, representing input transactions in the UTXO model. These perceptions collectively contribute to an intermediate belief regarding the validity of transactions. By integrating their beliefs with those of other validators, localized decisions are made to determine validity. Ultimately, a final consensus is achieved through a majority vote, ensuring precise and efficient validation of transactions. Our proposed approach is compared to the existing DAG-based scheme IOTA and the sharded blockchain Omniledger through extensive simulations. The results show that IOTA has high throughput and low latency but sacrifices accuracy and is vulnerable to orphanage attacks especially with low transaction rates. Omniledger achieves stable accuracy by increasing shards but has increased latency. In contrast, the proposed HybridChain exhibits fast, accurate, and secure transaction processing, and excellent scalability.
Motivated by proof-of-stake (PoS) blockchains such as Ethereum, two key desiderata have recently been studied for Byzantine-fault tolerant (BFT) state-machine replication (SMR) consensus protocols: Finality means that the protocol retains consistency, as long as less than a certain fraction of validators are malicious, even in partially-synchronous environments that allow for temporary violations of assumed network delay bounds. Accountable safety means that in any case of inconsistency, a certain fraction of validators can be identified to have provably violated the protocol. Earlier works have developed impossibility results and protocol constructions for these properties separately. We show that accountable safety implies finality, thereby unifying earlier results.
Patikiri Arachchige Don Shehan Nilmantha Wijesekara, Subodha Gunawardena
Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.
Abstract SDN revolutionises network management by providing a centralised controller that enables flexible and effortless configuration of networks. However, this flexibility also leads to a vulnerability that enables the adversary to trick the security system into allowing the installation of unauthorised flow rules in the switches. Blockchain provides us with a way to protect against malicious tampering with flow rules by storing them in the distributed ledger. In this work, we propose FTISCON, a mechanism to preserve the integrity of the OpenFlow flow table that utilizes blockchain technology. We employ the Ethereum Private Blockchain to implement the proof-of-concept and conduct a comparative analysis of the proposed scheme and existing related schemes, evaluating their performance in terms of delay, computation time, transaction cost, and detection rate. The proposed work is found to perform better in each of these. The study results suggest that the proposed approach offers a practical and efficient remedy to prevent flow modification attacks within SDN networks.
Siben Tian, Fenhua Bai, Tao Shen, Chi Zhang · 5 authors
To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm’s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.
Ning Lin, Shaocong Wang, Yi Li, Bo Wang · 22 authors
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.
Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Elias Kougianos, Vasanth Iyer · 5 authors
This work presents a sustainable cybersecurity solution using Physical Unclonable Functions (PUF), Trusted Platform Module (TPM), and Tangle Distributed Ledger Technology (DLT) for sustainable device and data security. Security-by-Design (SbD) or Hardware- Assisted Security (HAS) solutions have gained much prominence due to the requirement of tamper-proof storage for hardwareassisted cryptography solutions. Designing complex security mechanisms can impact their efficiency as IoT applications are more decentralized. In the proposed architecture, we presented a novel TPM-enabled PUF-based security mechanism with effective integration of PUF with TPM. The proposed mechanism is based on the process of sealing the PUF key in the TPM, which cannot be accessed outside the TPM and can only be unsealed by the TPM itself. A specified NV-index is assigned to each IoT node for sealing the PUF key to TPM using the Media Access Control (MAC) address. Access to the TPM's Non-Volatile Random Access Memory (NVRAM) is defined by the TPM's Enhanced Authorization policies as specified by the Trust Computing Group (TCG). The proposed architecture uses Tangle for sustainable data security and storage in decentralized IoT systems through a Masked Authentication Messaging (MAM) scheme for efficient and secure access control to Tangle. We validated the proposed approach through experimental analysis and implementation, which substantiates the potential of the presented PUFchain 4.0 for decentralized IoT-driven security solutions.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Advanced Memory and Neural Computing
Advanced Steganography and Watermarking Techniques
With the arrival of the 5G era, the Internet of Things (IoT) has entered a new stage, and the amount of IoT data is growing rapidly. The traditional blockchain cannot handle massive amounts of data, which presents scalability challenges for blockchain technology. Existing blockchain improvement technologies such as off-chain payments, protocol improvements, and sharding techniques have performance bottlenecks and limitations in the data, which is rapidly growing. The blockchain is fundamentally a decentralized distributed ledger, and the traditional chain structure is inadequate for addressing concerns such as forks, double-spending attacks, and other factors in the current IoT landscape. In this paper, we propose a high-throughput distributed ledger based on Directed Acyclic Graph (DAG) named TEEDAG. We design a consensus algorithm based on self-referencing parallel chains combined with Trusted Execution Environment (TEE) to ensure the security of the consensus process. The experiment proves that TEEDAG demonstrates a significantly higher throughput compared to traditional blockchain solutions and offers improved security and efficiency compared to existing DAG-based distributed ledger solutions.
Physical terminals provide network services to upper-layer applications, but their limited memory and processing power make it challenging to perform security updates and patches, leaving them vulnerable to known security threats. Attackers can exploit these weaknesses to control the terminals and attack the network. To restrict unauthorized access to the network and its resources, appropriate access control mechanisms are necessary. In this paper, we propose a fine-grained access control method based on smart contracts (FACSC) for terminals in software-defined networking (SDN). FACSC utilizes the attribute-based access control (ABAC) model to achieve fine-grained control over terminal access networks. To ensure the security and reliability of access control policies and terminal-related attribute information, we utilize smart contract technology to implement the ABAC model. Furthermore, we leverage the programming protocol-independent packet processor (P4) to filter and forward packets in the data plane based on the packet option field, enabling rapid terminal access. Experimental results show that our proposed method achieves fine-grained secure authentication of terminals in SDN networks with a low authentication processing overhead.
Distributed ledger technologies (DLT) usage is currently limited to a single platform as they do not have design-based interoperability capabilities. In general, it's challenging for a DLT to communicate with another one. Although several DLT solutions have been proposed and applied in specific application areas, building a general-purpose interoperability solution for any DLT remains a challenge. In previous work, we proposed a tailor-made interoperability solution between Hyperledger Fabric and Corda. This paper extends that work to enable interoperability between Hyperledger Fabric and Ethereum. The main contribution of this paper is to provide a new approach to enable interoperability between these two DLT. The approach follows a new request-response interaction model and future payments to enable the payments of services. A prototype was developed and evaluated through a case scenario, performance tests and cost analysis. Performance tests showed bottlenecks under heavy load scenarios due to Ethereum's design. Costs analysis showed that the approach is suitable for purchasing high-priced services. These promising results constitute a step forward in developing a general-purpose solution for DLT interoperability.
The metaverse, as an emerging technical term, conceptually aims to construct a virtual digital space that runs parallel to the physical world. Due to human behaviors and interactions being represented in the virtual world, security in the metaverse is a challenging issue in which the traditional centralized service model is one of the threat sources. To conquer the obstacle caused by centralized computing, blockchain-based solutions are potential problem-solving methods. However, it is difficult for a single blockchain to support large-scale data and business services in the metaverse, due to the scalability restrictions. Moreover, multi-chain settings also encounter the interoperability issues. In this work, we propose a Relay chain and Asynchronous consensus-based Consortium blockchain cross-Chain model, which realizes message transmission and cross-chain transactions in multiple chains by adopting the relay chain and cross-chain gateways. All nodes of the application chains and the relay chain execute cross-chain transactions in sequence and reach a consensus on transactions at any transmission delay. Our experiment evaluations demonstrate that our approach performs well in atomicity, security, and functionality (cross-chain transactions), such that the performance of blockchain scalability in the metaverse can be improved, compared with the traditional relay chain schemes.
Ali Nikhalat-Jahromi, Ali Mohammad Saghiri, Mohammad Reza Meybodi
Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.
The inherent security and computational demands of classical consensus protocols are often presumed impervious to various forms of attack. However, quantum computing advancements present considerable threats to the assumed attack resistance of classical security strategies currently deployed within blockchain systems. Adopting consensus algorithms fortified with post-quantum security measures holds the potential to significantly enhance the privacy and security dimensions of traditional blockchains. Notable advantages of such post-quantum solutions in the context of blockchain consensus include accelerated transaction verification, clarified mining authorship, and resilience against quantum attacks. Yet, a comprehensive analysis of the implications of post-quantum solutions for blockchain consensus is notably absent in the existing scholarly discourse. This paper aims to bridge this gap by delivering a systematic review of Post-Quantum Blockchain Consensus (PQBC). The four primary contributions of this study are (i) a systematic approach to presenting a comprehensive overview of PQBC, (ii) the systematic selection and analysis of 29 key studies from an initial pool of 1192 papers, (iii) a critical review of methods, enhancements to security, scalability, trust, and privacy, as well as the evaluation employed for PQBC, and (iv) a discussion of primary gaps and prospective directions for future PQBC research.
Chryssis Georgiou, Michel Raynal, Elad M. Schiller
Numerous distributed applications, such as cloud computing and distributed ledgers, necessitate the system to invoke asynchronous consensus objects an unbounded number of times, where the completion of one consensus instance is followed by the invocation of another. With only a constant number of objects available, object reuse becomes vital. We investigate the challenge of object recycling in the presence of Byzantine processes, which can deviate from the algorithm code in any manner. Our solution must also be self-stabilizing, as it is a powerful notion of fault tolerance. Self-stabilizing systems can recover automatically after the occurrence of arbitrary transient faults, in addition to tolerating communication and (Byzantine or crash) process failures, provided the algorithm code remains intact. We provide a recycling mechanism for asynchronous objects that enables their reuse once their task has ended, and all non-faulty processes have retrieved the decided values. This mechanism relies on synchrony assumptions and builds on a new self-stabilizing Byzantine-tolerant synchronous multivalued consensus algorithm, along with a novel composition of existing techniques.
Because of the characteristics of decentralization, immutability, and transparency, blockchain has gradually become a new and revolutionary technology, which has far-reaching significance for the development of modern technology. However, the traditional Bitcoin blockchain that supports synchronous consensus suffers from the fatal flaw of low throughput. To improve throughput, a number of DAG distributed ledgers have been proposed that support asynchronous consensus, all of which allow multiple nodes to process concurrent transactions asynchronously. However, most DAG distributed ledgers do not implement consistent sorting of transactions, making it difficult to deploy smart contracts. To overcome this problem, in this paper, an epoch-based transaction consistency sorting protocol for DAG distributed ledger is proposed, which not only provides the possibility for the deployment of smart contracts but also can be used to resolve conflicting transactions in the ledger. Transaction consistency sorting protocol provides a more reasonably ordered list of all transactions by taking scalars, such as the set of their own past and future, parent block, and timestamp. In addition, through theoretical analysis, the stability and rationality of the transaction consistency sorting protocol are proved, and there is no Condorcet cycle. Finally, the simulation results demonstrate the protocol is efficient and achieve a throughput of at least 2000 transactions per second.
To protect participants’ confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.
Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Elias Kougianos, Babu Kaji Baniya · 5 authors
This article presents the first-ever hardware-assisted blockchain for simultaneously handling device and data security in smart healthcare. This article presents the hardware security primitive physical unclonable functions (PUF) and blockchain technology together as PUFchain 2.0 with a two-level authentication mechanism. The proposed PUFchain 2.0 security primitive presents a scalable approach by allowing Internet of Medical Things (IoMT) devices to connect and obtain PUF keys from the edge server with an embedded PUF module instead of connecting a PUF module to each device. The PUF key, once assigned to a particular media access control (MAC) address by the miner, will be unique for that MAC address and cannot be assigned to other devices. PUFs are developed based on internal micro-manufacturing process variations during chip fabrication. This property of PUFs is integrated with blockchain by including the PUF key of the IoMT into blockchain for authentication. The robustness of the proposed Proof of PUF-Enabled authentication consensus mechanism in PUFchain 2.0 has been substantiated through test bed evaluation. Arbiter PUFs have been used for the experimental validation of PUFchain 2.0. From the obtained 200 PUF keys, 75% are reliable and the Hamming distance of the PUF module is 48%. Obtained database outputs along with other metrics have been presented for validating the potential of PUFchain 2.0 in smart healthcare.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G$-invariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all $G$-invariant single-hidden-layer or ``shallow'' neural network ($G$-SNN) architectures with ReLU activation for any finite orthogonal group $G$, and we prove a second theorem that characterizes the inclusion maps or ``network morphisms'' between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every $G$-SNN to a signed permutation representation of $G$ acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of $G$, thus admitting a topological interpretation. The $G$-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the $G$-SNN architectures for some example groups $G$ and visualize their structure. Finally, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.
Blockchain technology is impacting several industries, including the creative industries and those operating in the Internet of Things (IoT). Lately, researchers' attention has been devoted to the application of blockchain in the recorded music industry. However, thus far, no research has investigated the use of such technology in the Internet of Musical Things (IoMusT). The IoMusT is a new area emerging in industry and academy as an extension of the IoT to the musical domain. The IoMusT itself, as the IoT, is a distributed network of musical things, which are objects augmented with information and communication technologies serving a musical purpose. The IoMusT vision requires, above all, IoT features such as decentralization, seamless authentication, transparency, data integrity and privacy, and self-maintenance, as well as the musical domain features such as efficient handling of copyrights and speed of royalties payment. Such features can be brought by blockchain. In this paper, we investigate the integration of blockchain technology with the IoMusT, and we name such synthesis “Blockchain-based IoMusT”. We present a vision for this new paradigm in terms of the novel opportunities that are enabled, and we propose a set of application scenarios enabled by technological integration. Finally, we outline the open research directions in this promising area.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
After the success of the Bitcoin blockchain, came several cryptocurrencies and blockchain solutions in the last decade. Nonetheless, Blockchain-based systems still suffer from low transaction rates and high transaction processing latencies, which hinder blockchains' scalability. An entire class of solutions, called Layer-1 scalability solutions, have attempted to incrementally improve such limitations by adding/modifying fundamental blockchain attributes. Recently, a completely different class of works, called Layer-2 protocols, have emerged to tackle the blockchain scalability issues using unconventional approaches. Layer-2 protocols improve transaction processing rates, periods, and fees by minimizing the use of underlying slow and costly blockchains. In fact, the main chain acts just as an instrument for trust establishment and dispute resolution among Layer-2 participants, where only a few transactions are dispatched to the main chain. Thus, Layer-2 blockchain protocols have the potential to transform the domain. However, rapid and discrete developments have resulted in diverse branches of Layer-2 protocols. In this work, we systematically create a broad taxonomy of such protocols and implementations. We discuss each Layer-2 protocol class in detail and also elucidate their respective approaches, salient features, requirements, etc. Moreover, we outline the issues related to these protocols along with a comparative discussion. Our thorough study will help further systematize the knowledge dispersed in the domain and help the readers to better understand the field of Layer-2 protocols.
Memory in biological beings is as complex as the rational complexity of that concrete being requires. Clearly, memory helps to conform knowledge bases to serve the needs of the specific natural being. To analogize from Robotics concepts, it seems that the degrees of freedom in the biological being’s memory are higher or lower depending upon the rationality of each living being. Robots and artificial systems appear to require analogous structures. That is, to build a reactive system, the requirement of memory is not highly demanding with respect to the degrees of freedom. However, the required degrees of freedom seems to grow as the ability of the artificial system to deliberate increases. Consequently, to design artificial systems that would implement cognitive abilities, it is required to rethink memory structures. When designing a Cognitive Artificial System, memory systems should be thought of as highly accessible discrete units. In addition, these systems would require designs in the form of distributed architectures with non-linear features, such as those of human thought. In addition, they should allow for complex mixed types of data (text, images, time or so). Blockchain has attracted great interest for a few years now, especially since the appearance of Bitcoin. A blockchain is a distributed ledger that combines an append-only data structure designed to be resistant to modifications, with a consensus protocol [ 1 , 2 ]. This innovation can be thought of as a sequence of containers, the blocks, that store two things: the information of a “system” and the “service” that such system provides [ 2 ], and it provides an interesting starting point to rethink memory systems in robots.
Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (<i>IoT</i>). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (<i>Multizone-DAG-Blockchain</i>) framework is proposed for the fog-based IoT environment. In this context, fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes, thus preserving the energy consumption of the IoT devices. Both IoT and fog nodes are initially authenticated using a non-cloneable physical function- based validation mechanism (<i>DPUF-VM</i>) in which multiple authentication certificates are verified in the blockchain. Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q- Curve algorithm. In the cloud, sensitive data is stored as ciphertext. Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes. The fog node first performs a redundancy analysis using the Jaccard Similarity (JS) measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network (<i>N2IN</i>) algorithm. A lightweight proof-of-authentication (<i>PoAh</i>) algorithm is presented and executed by the optimal consensus node selected by the bi- objective spiral optimization (<i>BoSo</i>) algorithm for transaction validation. The proposed work is modeled in Network Simulator 3.26 (ns-3.26), and the performance is evaluated in terms of energy consumption, storage cost, response time, and throughput.