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Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks

Yueyue Dai, Du Xu, Ke Zhang, Sabita Maharjan, Yan Zhang

arXiv:2011.08449v2cs.CRcs.LG

TL;DR

Vehicular caching must handle mobility, changing wireless conditions, and distrust over sensitive content. The paper combines DRL-based caching with a permissioned blockchain and PoU verifier selection, with security analysis supporting protection and Uber-dataset results showing the DRL scheme outperforms two benchmarks.

  • Problem

    High vehicle mobility and time-varying wireless channels complicate optimal caching, while sensitive content makes vehicles reluctant to use untrusted caching providers.

  • Method

    The paper uses a blockchain-enabled distributed caching framework, DRL for mobility-aware caching, and Proof-of-Utility to select block verifiers.

  • Results

    Security analysis supports security and privacy protection, while real Uber-dataset results show the DRL-inspired caching scheme significantly outperforms two benchmark policies.

  • Takeaways & Limitations

    The integrated design provides an intelligent and secure content-caching approach for vehicular edge networks.

Abstract

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Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition make it challenge to design an optimal content caching policy. Further, with much sensitive personal information, vehicles may be not willing to caching their contents to an untrusted caching provider. Deep Reinforcement Learning (DRL) is an emerging technique to solve the problem with high-dimensional and time-varying features. Permission blockchain is able to establish a secure and decentralized peer-to-peer transaction environment. In this paper, we integrate DRL and permissioned blockchain into vehicular networks for intelligent and secure content caching. We first propose a blockchain empowered distributed content caching framework where vehicles perform content caching and base stations maintain the permissioned blockchain. Then, we exploit the advanced DRL approach to design an optimal content caching scheme with taking mobility into account. Finally, we propose a new block verifier selection method, Proof-of-Utility (PoU), to accelerate block verification process. Security analysis shows that our proposed blockchain empowered content caching can achieve security and privacy protection. Numerical results based on a real dataset from Uber indicate that the DRL-inspired content caching scheme significantly outperforms two benchmark policies.

I. INTRODUCTION

The paper addresses secure, low-latency vehicular content caching by combining DRL with permissioned blockchain. Vehicles share caching resources, while base stations support blockchain-based coordination and verification.

  • VEC uses base stations and vehicles as edge resources to cache content near vehicles, reducing backhaul traffic and delivery latency.Vehicles can act as caching providers through V2V communication when other vehicles lack sufficient local capacity.
  • Existing DRL-based resource-allocation and offloading studies address dynamic network decisions but do not consider security and privacy.The paper positions this omission as a gap for vehicular content caching involving sensitive information.
  • Permissioned blockchain restricts transaction verification and block creation to authorized nodes, enabling faster consensus with fewer participating nodes.This setting is presented as suitable for energy-constrained and delay-sensitive vehicular networks.
  • The proposed framework lets vehicles perform content caching while base stations maintain the permissioned blockchain for distributed and secure operation.Vehicles serve as caching requesters or providers, and blockchain supports interactions without relying on an untrusted caching provider.
  • The paper formulates caching as a DRL problem that accounts for vehicular mobility and proposes PoU to select block verifiers for efficient consensus.The stated evaluation uses a real dataset and reports effectiveness against benchmark policies, without supplying numerical values here.

B. Blockchain-based Vehicular Content Caching

The blockchain-based caching process authenticates vehicles, matches caching requests with available resources, records transactions, and verifies blocks through base stations. The design targets secure V2V caching while avoiding the energy and delay costs of conventional PoW consensus.

  • Identity Establishment and System Initialization: Vehicles establish authenticated identities using public keys, private keys, and certificates, with wallet addresses derived from public keys.Changeable wallet addresses are used to preserve anonymity and privacy.
  • Triggering Content Caching Smart Contract: Caching requesters send required resources and locations to a nearby base station, while caching providers periodically advertise available resources and locations.The messages include public-key encryption, digital signatures, certificates, and timestamps.
  • Triggering Content Caching Smart Contract: Base stations batch-verify vehicle identities before running the V2V content-caching mechanism.Batch verification passes only when all included signatures are valid; any invalid signature causes failure.
  • Content Delivery and Transaction Recording: Smart contracts execute content delivery and transfer coins from the caching requester’s wallet to the caching provider’s wallet.After caching, the requester generates a transaction recording the shared resource and payment, which is encrypted, signed, broadcast, and audited.
  • Building Block and Performing Consensus Process: A leader base station creates and broadcasts each block, while other base stations verify it; PoW is unsuitable because it is computation-intensive and energy-consuming for vehicular networks.The paper therefore targets a consensus mechanism with low energy and time consumption, using PoU for verifier selection.

IV. DEEP REINFORCEMENT LEARNING-BASED VEHICULAR CONTENT CACHING

The paper formulates V2V content caching as a system-utility maximization problem under mobility-dependent communication conditions, cache-capacity limits, and delivery-latency constraints. Because the resulting binary optimization is nonconvex and difficult to scale under changing channels, the paper turns to DRL.

  • System model: The model considers one base station, caching requesters, and caching providers among N vehicles in a single cell.Requesters generate content with required caching resources and maximum delivery latencies; providers supply local cache capacity.
  • Caching model: Caching providers store requester content subject to local cache capacities, while requesters pay for consumed caching resources.The binary variable x_ip indicates whether requester v_i caches content on provider v_p, and payment is proportional to required caching resource.
  • Communication model: V2V communication is constrained by vehicle distance and channel conditions, which determine transmission rate, latency, and energy consumption.The communication rate depends on bandwidth, transmission power, channel gain, path-loss exponent, and noise power.
  • Optimization problem: The optimization maximizes system utility while enforcing cache-capacity and maximum content-delivery-latency constraints.The formulation includes binary caching decisions x_ip ∈ {0,1}.
  • Motivation for DRL: Binary decisions make the feasible set and objective nonconvex, while vehicle movement creates time-varying wireless channels that hinder conventional optimization.The paper therefore motivates DRL for high-dimensional, time-varying decision-making and scalability.

B. DRL-based V2V Content Caching Solution

The paper reformulates V2V caching as a DRL problem whose state captures the vehicular environment, actions represent caching matches, and rewards reflect constrained system utility. It uses DDPG to learn policies and then trains actor and critic networks with replayed experience.

  • DRL formulation: The caching problem is reformulated using system state, action, and reward before applying a DRL-based V2V caching algorithm.The formulation is illustrated in Fig. 2.
  • State: The state represents observed vehicular conditions, including communication rates, content latencies, delivery energy, driving directions, requester requirements, and provider capacities.These quantities vary with vehicle mobility and therefore capture changing V2V conditions.
  • Action and reward: The action matches caching requesters with caching providers, while the reward is current system utility for feasible actions and a negative penalty otherwise.Feasibility is determined by cache-capacity and delivery-latency constraints.
  • DDPG selection: DDPG is selected because it supports high-dimensional observation and action spaces, unlike Q-learning and DQN under the paper’s problem setting.The caching agent uses primary actor and critic networks, a target network, and replay memory.
  • Training procedure: Training repeatedly executes policy actions, observes rewards and next states, stores experience tuples, samples mini-batches, and updates critic and actor parameters.The target network supplies target values, while replay memory stores tuples of state, action, reward, and next state.

C. Action Refinement

Because DRL produces continuous outputs but caching decisions are binary, the paper refines the policy output into integer requester-provider matches using a weighted bipartite graph and maximum-weight matching.

  • Output conversion: The DRL output is a fractional solution, whereas each caching variable must be an integer binary decision.Action refinement begins by extracting continuous values x′_ip ∈ [0,1].
  • Bipartite graph: The refinement constructs a weighted bipartite graph linking caching requesters to provider nodes representing available service capacity.Provider replication encodes how many requester assignments each provider can serve.
  • Integer matching: The Hungarian algorithm finds a complete maximum-weight matching, setting x_ip = 1 for matched requester-provider pairs and x_ip = 0 otherwise.This produces the detailed integer caching pairs from the refined action.

V. PROOF-OF-UTILITY CONSENSUS IN VEHICULAR NETWORKS

The paper presents Proof-of-Utility consensus for the permissioned blockchain’s edge plane and defines BS utility evaluation for selecting block verifiers.

  • Verifier selection: Proof-of-Utility evaluates base-station utility to select block verifiers in the permissioned blockchain.The mechanism is proposed for the edge plane’s block-consensus process.

A. PoU Consensus

Proof-of-Utility (PoU) selects base-station delegates using vehicle voting and utility, then organizes them into rotating leader and verifier roles for block production and verification.

  • Delegate Selection: PoU uses vehicle voting to select base-station delegates according to their utility rather than users’ stake.Higher utility indicates stronger computing and processing capability for generating and verifying blocks.
  • Delegate Selection: Vehicles act as token holders, with vote weights proportional to the coins they hold, while base stations serve as neutral consensus nodes.Base stations do not directly participate in content delivery or coin payment.
  • Block Production and Verification: The elected delegates are divided into one leader and multiple verifiers, with leadership assigned among delegates in round-robin order.The leader collects V2V caching transactions and produces the block; the remaining delegates verify it.
  • Block Production and Verification: Block verification proceeds through broadcast, verification, and confirmation phases among the elected delegates.Verifiers check the leader’s message signature, audit packaged caching requests, and exchange signed audit results.
  • Block Production and Verification: PoU rewards participating base stations after a block is appended and reshuffles delegate order after every delegate has led once.If a delegate fails to produce a block, its block is skipped and its transactions are transferred onward.

B. Utility Evaluation

The utility evaluation measures base stations by the time required to collect transactions, produce and verify blocks, and transmit content, subject to vehicle latency constraints.

  • Utility Definition: Utility decreases monotonically with block-production and verification time, and becomes zero when content delivery exceeds a vehicle’s maximum latency.For a block containing K transactions, the relevant latency threshold is τ^k = min{τ_1, τ_2, ..., τ_K}.
  • Utility Definition: A base station’s total time includes transaction collection and hash computation, block verification, and content transmission.These components jointly determine the time consumption used in utility evaluation.
  • Verification Delay: Block verification time comprises block broadcasting, cross-verification among verifiers, and block confirmation.The leader broadcasts to verifiers simultaneously, so broadcasting is determined by the longest transmission time.
  • Verification Delay: Cross-verification includes local verification, broadcasting local results, and a second audit among verifiers.The model tracks the block size, local-verified result size, second-audit result size, computing resources, and inter-verifier distances.
  • Utility-Based Selection: Vehicles prefer base stations providing faster block production and verification, so the utility function evaluates performance with respect to time consumption.Each vehicle votes for the base station selected by the utility-based procedure, with voting weight tied to its caching transaction fee.

VI. SECURITY ANALYSIS AND NUMERICAL RESULTS

The paper evaluates the security of blockchain-based V2V caching and its performance using vehicle trajectories from the Uber dataset, including the proposed PoU consensus.

  • The evaluation combines security analysis with numerical assessment of V2V content caching and PoU performance.

A. Security Analysis

The proposed permissioned blockchain supports secure, privacy-aware, and auditable V2V content caching without mutual trust among vehicles.

  • Permissioned blockchain establishes secure content caching for multiple vehicles without requiring mutual trust.
  • Trust Model: Caching requesters and providers exchange content peer-to-peer without a single trusted third party, reducing dependence on centralized security.The paper connects this design to robustness and scalability.
  • Privacy Protection: Vehicles and base stations use pseudonymous public keys, while signed messages and transactions are accessible only to entities holding the appropriate private keys.The paper states that this combination protects vehicle privacy and prevents signature forgery without private-key access.
  • Majority Attack: Elected delegates mutually audit blocks and transactions, while vehicles democratically select delegates through PoU to address majority attacks.
  • Traceability: Blockchain timestamps preserve transaction records that cannot be modified by a single entity, enabling later tracing through any base station.

B. Permance Analysis of Vehicular Content Caching Scheme

The proposed DRL-empowered caching scheme is evaluated against benchmark policies and under different learning rates and requester counts. It achieves higher reward and caching success, while reward converges across tested learning rates.

  • The evaluation uses Python and TensorFlow with a real-world Uber trajectory dataset, 100 example vehicles, and a 1.52 km^2 observation area.The dataset contains 4.5 million New York City pickups from April–September 2014 and 14.3 million from January–June 2015.
  • The comparison includes Greedy Content Caching, which selects the provider with the highest wireless data rate, and Random Content Caching, which selects a provider randomly within distance γ.
  • The proposed DRL-empowered algorithm significantly outperforms GCC and RCC in cumulative average reward.It uses current network topology and wireless channel conditions, whereas the benchmarks do not acquire real-time vehicular-network parameters.
  • Over 86% of caching requesters successfully complete content caching within stringent deadline constraints, compared with about 78% for GCC and 5% for RCC.
  • At learning rates 10^-2, 10^-3, and 10^-4, cumulative average rewards converge, with 10^-2 converging slightly faster.The proposed algorithm uses a learning rate of 10^-2.
  • Increasing caching requesters raises reward, sharply from 10 to 30 and only slightly from 30 to 50.With fixed total provider caching resource, system utility approaches its upper bound at 30 requesters.

C. Performance Analysis of PoU Consensus

The PoU consensus analysis examines how block size and communication distance affect vehicle utility. Utility decreases as either block size or distance to the selected PoU leader increases.

  • The evaluation varies block size I_k, local-verified result size O_k, second-audit result size W_k, and BS computation resources within specified ranges.I_k, O_k, and W_k are uniformly distributed in [10, 50] MB, MB, and [100, 500] KB; BS resources range from 5 to 10 GHz.
  • Utility decreases as block size I_k increases and as communication distance to the PoU-selected leader increases.Larger distance requires more communication time for block verification.
  • The reported utility trends agree with the preceding analysis in subsection V-B.
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