Distributed Consensus in Wireless Interconnected Autonomous Systems: A DAG-based Approach Review

Table of Contents

• 1. Gabatarwa
• 2. Tsarin Yarda a CAS
  • 2.1 Mean/Maximum/Minimum Estimation Consensus
  • 2.2 Byzantine Fault Tolerance Consensus
  • 2.3 State Machine Replication
• 3. DAG-based Approach for Wireless CAS
  • 3.1 DAG Message Structure
  • 3.2 Two-dimensional DAG Strategy
• 4. Technical Implementation
  • 4.1 Mathematical Framework
  • 4.2 Aiwartar Code
• 5. Experimental Results
• 6. Future Applications
• 7. Nassosi
• 8. Binciken Kwararru

1. Gabatarwa

Tsarin Kai-haɗin kai (CAS) yana wakiltar fasaha mai canzawa, wacce ke ba da damar haɗin gwiwar tuƙi mai sarrafa kai da tsarin zirga-zirga na hankali. Bayyanar hanyar sadarwa ta mota (VANET) da kayan aikin 5G sun hanzarta ci gaban CAS, suna gabatar da sabbin buƙatu don sarrafa bayanai da tsarin yarjejeniya.

2. Tsarin Yarda a CAS

2.1 Mean/Maximum/Minimum Estimation Consensus

These consensus mechanisms operate on numerical quantities, where nodes iteratively update and converge to the mean, maximum, or minimum values. The update rule is as follows: $x_i(t+1) = \sum_{j=1}^n w_{ij} x_j(t)$, where $w_{ij}$ represents the weight matrix and $x_i(t)$ denotes the state of node i at time t.

2.2 Byzantine Fault Tolerance Consensus

BFT Consensus yana magance kalubalen da ɓarnatattun nodes ke yada bayanan ƙarya. Practical Byzantine Fault Tolerance (pBFT) yana buƙatar nodes $3f+1 don jure f ɓarnatattun nodes, yana tabbatar da kaddarorin aminci da aiki.

2.3 State Machine Replication

SMR ensures all correct nodes execute identical command sequences, maintaining consistency in distributed systems. However, traditional SMR assumes reliable message delivery, which poses challenges in wireless CAS environments.

3. DAG-based Approach for Wireless CAS

3.1 DAG Message Structure

Tsarin DAG da aka gabatar ya ƙirƙiro yarjejeniya yada bayanai maras ƙin amana wacce take jurewa asarar saƙo da jinkirin da ba a iya annabta ba. Kowane saƙo yana nuni zuwa saƙon da ya gabata, ya samar da zane mai sarƙaƙƙiya maras karkata, yana hana rikicewar tarihi.

3.2 Two-dimensional DAG Strategy

The enhanced protocol implements a two-dimensional DAG, achieving partial ordering for blockchain applications and total ordering for SMR. This dual approach simultaneously addresses both data consistency and service replication requirements.

4. Technical Implementation

4.1 Mathematical Framework

Consensus convergence can be modeled using Markov chains: $P(X_{t+1} = j | X_t = i) = p_{ij}$, where the transition probability $p_{ij}$ depends on network connectivity and message reliability. DAG growth follows: $G_{t+1} = G_t \cup \{m_{t+1}\}$, where each new message m references multiple previous messages.

4.2 Aiwartar Code

class DAGConsensus:
    def __init__(self, node_id):
        self.node_id = node_id
        self.dag = DirectedAcyclicGraph()
        self.tips = set()
    
    def create_message(self, data, references):
        message = {
            'id': generate_uuid(),
            'data': data,
            'references': references,
            'timestamp': time.time(),
            'creator': self.node_id
        }
        self.dag.add_vertex(message['id'], message)
        for ref in references:
            self.dag.add_edge(ref, message['id'])
        return message
    
    def validate_consensus(self, threshold=0.67):
        tips_count = len(self.tips)
        approved_messages = self.calculate_approval()
        return approved_messages / tips_count >= threshold

5. Experimental Results

Experimental evaluation shows significant improvements: compared with traditional flooding protocols, message loss is reduced by 45%; under high mobility conditions, consensus convergence speed increases by 60%; tolerance to Byzantine attacks reaches 85%. Even with 30% packet loss rate, the DAG-based method maintains 92% consensus accuracy.

6. Future Applications

The DAG-based consensus framework demonstrates broad application prospects in smart city infrastructure, industrial IoT, drone swarm coordination, and decentralized financial systems. Future research directions include quantum-resistant cryptography integration, cross-chain interoperability, and adaptive consensus parameters based on network conditions.

7. Nassosi

1. Wu, H., et al. "When Distributed Consensus Meets Wireless-Connected Autonomous Systems." Journal of LaTeX Class Files, 2020.
2. Lamport, L. "The Part-Time Parliament." ACM Transactions on Computer Systems, 1998.
3. Leiserson, C.E., et al. "There's plenty of room at the Top: What will drive computer performance after Moore's law?" Science, 2020.
4. Nakamoto, S. "Bitcoin: A Peer-to-Peer Electronic Cash System." 2008.
5. Buterin, V. "A Next-Generation Smart Contract and Decentralized Application Platform." Ethereum White Paper, 2014.

8. Binciken Kwararru

A yi harshe ya fita jini Wannan binciken ya samu cikakken ci gaba wajen sa Byzantine consensus ya dace da tsarin mara waya na ainihi, amma ya yi watsi da lissafin kudin lantarki na DAG a cikin na'urorin iyaka masu iyaka.

Sarkar dabaru Masu rubutu sun gano daidai gazawar yarjejeniyar al'ada a cikin yanayin maras amfani na waya → sun gabatar da tsarin DAG don magance asarar saƙo → sun cimma tsari biyu don ɗaukar nau'ikan amfani daban-daban → sun cimma yarjejeniya ta blockchain da SMR a lokaci guda. Duk da haka, sarkar ta karye a wurin faɗaɗawa: yayin da adadin nodes ke ƙaru, rikitaccen DAG yana ƙaruwa da ƙima, yana haifar da toshewar tabbaci, wanda zai iya raunana ikon yanke shawara na ainihi a cikin ayyukan aminci masu mahimmanci kamar motocin tuƙi kai tsaye.

Fitattun abubuwa da raunuka: Babban hasashe shine daidaita DAG daga blockchain (kamar IOTA's Tangle) zuwa yarjejeniyar CAS na gaba ɗaya - wannan yana da ƙwarjin gaske. Dabarar tsarawa biyu ta warware matsalar rashin daidaituwa da cikakken tsari cikin kyakkyawa. Duk da haka, raunin bayyananne na takardar shine gwajin ma'auni tare da tsoffin yarjejeniyoyi maimakon madadin na zamani (kamar HoneyBadgerBFT ko Algorand consensus). Idan aka yi la'akari da sanannen raunin tsarin tushen DAG ga harin sarkar parasitic (kamar rahotannin raunin IOTA na 2019-2020), da'awar 85% na rashin kuskure tana nuna bege mai yawa.

Umarnin Aiki: Masu kera motoci da na IoT yakamata su yi amfani da wannan hanyar nan take don aikace-aikacen da ba na aminci ba (kamar taron motoci ko fakin motoci mai hankali). Duk da haka, don yanke shawara ta atomatik, yakamata a jira sigar 2.0 wacce za ta magance matsalolin rikitaccen lissafi. Ƙungiyar bincike yakamata ta mai da hankali kan haɗa wannan tsarin DAG tare da ayyukan bazuwar da za a iya tantancewa (kamar yadda ake amfani da su a Algorand) don rage raunin haɗin gwiwar kai hari. Lokacin yana da kyau - yayin da ake ƙara aikin 5G-V2X, idan an magance matsalolin iya faɗaɗawa a cikin watanni 18-24, wannan fasaha na iya zama ginshiƙin hanyar sadarwa ta gaba a cikin motoci.

Hanyar takardar ta yi daidai da yanayin masana'antu mai faɗi, wato juya zuwa tsarin yarjejeniya mara lokaci, kamar yadda Facebook's Diem blockchain da Amazon's quantum ledger database suka nuna. Duk da haka, ba kamar waɗannan abubuwan da aka tsara su ba, marubutan sun magance matsala mai wahala ta cikakkiyar hanyar sadarwa mara igiya. Idan aka kwatanta da aikin Google na baya-bayan nan na koyon tarayya na tsarin mai cin gashin kansa, wannan yarjejeniya ta tushen DAG tana ba da garantin daidaito mai ƙarfi, amma farashin sadarwa mai yawa - wannan ma'auni yana buƙatar tantancewa a hankali bisa buƙatun aikace-aikacen.