The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS). Despite the IATS's benefits, security remains a significant challenge. Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies. Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development. In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details. Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation. Additionally, the chaotic krill herd technique is used to tune the DBN model’s hyperparameters. The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions. The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.
With the tremendous growth of modern communicating, sensing, computing, and analyzing devices and techniques, the last few years have witnessed industry growth and tremendous academic efforts in intelligent autonomous transportation systems (IATS) [
The more commonly discussed topic interms of applications of the BT in transportation is logistics; indeed, several studies were introduced in the last few years. In a globalized world, many industries must design effective and longer supply chains to achieve success [
Major enterprises, like Maersk and IBM, have built partnerships to examine blockchain implementation. Many stakeholders could depend on the blockchain to gain trust and to handle the fluctuation of data [
This article focuses on the design of blockchain with deep learning enabled IATS for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS technique applies Ethereum as the fundamental blockchain for storing confidential data such as order details, cargo details, etc. Besides, the deep belief network (DBN) model is utilized to recommend vehicle and cargo matching during transportation. Moreover, the chaotic krill herd algorithm is applied for the hyperparameter tuning of the DBN model. The performance validation of the BDL-IATS technique takes place and the results are inspected under varying aspects.
Section 2 discusses the related works to the research study and Section 3 discusses proposed research methodology and Section 4 performance analysis and comparison with existing system and Section 5 discusses conclusion with future findings.
In cargo logistics transportation, the logistics turnover amongst connects is delayed, the data chain of total system was incomplete, and data transparency was minimal. Accordingly, loopholes from logistics transportation are prone to appear. To the stable function of logistics transportation method from logistics company. When it fails, massive security loopholes are created from the system like system breakdown and collapse. When the logistics method was integrated into the decentralized blockchain platforms, the potential issues of typical logistics management method is resolved; therefore, the logistics management method appears that natural adaptabilities to blockchain technology.
The blockchain is a decentralization distributed storing that is combining kept by every participating node; in the meantime, the participating node is attain real-time data from the network. During this manner, the blockchain data tamper-proof and trustworthy features are combined as to logistics management. The data from the blockchain is allocated with joining nodes from the network in real time that is important to Logistics Companies. Only if the upload data has been attained by another node from time is the quick and smooth function of logistics transfer is make sure. During the case of meeting business scenario, blockchain is resolve the issues of data asymmetry and lack of trust. Concurrently, utilizing appropriate recommendation techniques enhances the turnover rate of trucks and cargo from the truck-finding state.
Tokody et al. [
Kumar et al. [
In Zhou et al. [
This article has developed an effective BDL-IATS technique for secure cargo and vehicle matching in the IATS environment. The BDL-IATS technique has employed applies Ethereum as the fundamental blockchain for storing confidential data such as order details, cargo details, etc. Moreover, the CKHA with DBN model is utilized to recommend vehicle and cargo matching during transportation.
Deep Belief Network (DBN) is a Deep Neural Network (DNN) collected of
The DBN trained technique to normal charge voltage has 2 phases of pre-trained and fine-tuned. During the pre-trained step,
For tuning the hyperparameters of the DBN model, the CKHA is utilized and thereby optimized the overall performance of the network. KH is a novel generic stochastic optimized method to the global optimized issue. It can be simulated as performance of a krill swarm. Once the hunting to the food and communicate with everyone, the KH method repeat the execution of 3 actions and follows search way that progress the main function values [ Foraging action; The movement inclined by other krill; Physical diffusion.
Even KH method adapts the Lagrangian method as demonstrated in the subsequent model:
where:
The direction managed by the secondary effort
In order to the
For enhancing search effectiveness and making sure convergence to an optimum solution, the chaos method was combined with KH structure to this study scope [
The Chebyshev map upgrade parameters
During the case of CKH,
The proposed research is implemented in NS-3 environment. This section inspects the performance analysis of the BDL-IATS technique with other techniques interms of different measures.
Accuracy (%) | |||||
---|---|---|---|---|---|
Time (hrs) | ItemCF | UserCF | GBDT | LightGBM | BDL-IATS |
0 | 1.10 | 1.62 | 2.14 | 2.64 | 3.20 |
3 | 28.72 | 29.20 | 34.27 | 37.65 | 38.61 |
6 | 47.78 | 49.96 | 54.06 | 57.20 | 60.09 |
9 | 60.57 | 62.26 | 65.16 | 71.43 | 74.33 |
12 | 66.12 | 69.02 | 71.92 | 78.67 | 83.26 |
15 | 69.26 | 72.16 | 76.50 | 81.57 | 86.15 |
18 | 74.57 | 75.54 | 78.91 | 82.78 | 86.64 |
21 | 76.74 | 78.19 | 80.85 | 83.02 | 86.88 |
24 | 77.47 | 79.88 | 81.33 | 84.47 | 86.64 |
Precision (%) | |||||
---|---|---|---|---|---|
Time (hrs) | ItemCF | UserCF | GBDT | LightGBM | BDL-IATS |
0 | 3.03 | 3.03 | 3.49 | 3.03 | 5.11 |
3 | 32.87 | 34.72 | 38.19 | 44.66 | 48.83 |
6 | 48.60 | 50.91 | 54.15 | 61.09 | 66.87 |
9 | 57.62 | 60.39 | 64.10 | 71.96 | 78.44 |
12 | 62.48 | 64.79 | 68.26 | 75.43 | 80.98 |
15 | 65.25 | 67.57 | 72.19 | 78.67 | 81.91 |
18 | 67.80 | 69.42 | 74.27 | 78.44 | 82.60 |
21 | 68.26 | 70.80 | 75.20 | 78.67 | 82.37 |
24 | 68.72 | 70.34 | 75.43 | 79.36 | 82.14 |
Recall (%) | |||||
---|---|---|---|---|---|
Time (hrs) | ItemCF | UserCF | GBDT | LightGBM | BDL-IATS |
0 | 0.85 | 1.50 | 1.50 | 2.59 | 2.38 |
3 | 32.90 | 35.08 | 37.48 | 40.75 | 46.42 |
6 | 45.98 | 48.38 | 51.65 | 55.58 | 63.43 |
9 | 52.31 | 54.49 | 57.32 | 66.26 | 72.15 |
12 | 57.98 | 60.59 | 63.43 | 71.49 | 78.91 |
15 | 61.03 | 64.30 | 67.57 | 74.11 | 81.09 |
18 | 62.34 | 65.39 | 68.88 | 75.64 | 81.74 |
21 | 63.86 | 67.35 | 69.75 | 75.20 | 81.96 |
24 | 64.08 | 66.91 | 69.53 | 75.64 | 81.52 |
Conversion rate (%) | |||||
---|---|---|---|---|---|
Time (hrs) | ItemCF | UserCF | GBDT | LightGBM | BDL-IATS |
0 | 1.69 | 1.69 | 2.01 | 1.91 | 2.54 |
3 | 16.94 | 17.36 | 18.53 | 20.75 | 21.38 |
6 | 23.92 | 25.30 | 27.21 | 29.22 | 29.85 |
9 | 28.16 | 28.90 | 30.91 | 33.66 | 35.89 |
12 | 31.12 | 32.50 | 33.87 | 36.94 | 38.85 |
15 | 33.13 | 34.09 | 35.99 | 37.79 | 40.23 |
18 | 33.56 | 34.62 | 36.10 | 38.43 | 40.86 |
21 | 33.87 | 35.04 | 36.31 | 38.43 | 40.54 |
24 | 34.83 | 35.15 | 36.31 | 38.21 | 40.76 |
For instance, with 3 hrs, the BDL-IATS technique has accomplished higher CR of 21.38% whereas the ItemCF, UserCF, GBDT, and LightGBM techniques have resulted to lower CR of 16.94%, 17.36%, 18.53%, and 20.75% correspondingly. Also, with 12 hrs, the BDL-IATS approach has accomplished maximum CR of 38.85% whereas the ItemCF, UserCF, GBDT, and LightGBM techniques have resulted to lower CR of 31.12%, 32.50%, 33.87%, and 36.94% correspondingly. Moreover, with 24 hrs, the BDL-IATS technique has accomplished higher CR of 40.76% whereas the ItemCF, UserCF, GBDT, and LightGBM systems have resulted to lower CR of 34.83%, 35.15%, 36.31%, and 38.21% correspondingly.
Finally, an average response time (ART) analysis of the BDL-IATS technique with recent approaches takes place in
Avg. response time (sec) | |||||
---|---|---|---|---|---|
Time (hrs) | ItemCF | UserCF | GBDT | LightGBM | BDL-IATS |
0 | 4.99 | 4.79 | 4.24 | 3.76 | 3.12 |
3 | 4.88 | 4.63 | 4.18 | 3.68 | 3.23 |
6 | 4.87 | 4.68 | 4.10 | 3.73 | 3.20 |
9 | 4.94 | 4.76 | 4.21 | 3.81 | 3.18 |
12 | 4.96 | 4.74 | 4.29 | 3.79 | 3.39 |
15 | 4.99 | 4.80 | 4.32 | 3.98 | 3.25 |
18 | 5.04 | 4.79 | 4.41 | 3.87 | 3.29 |
21 | 5.05 | 4.85 | 4.45 | 3.95 | 3.34 |
24 | 5.07 | 4.87 | 4.37 | 3.85 | 3.17 |
The results indicated that the BDL-IATS technique has resulted in least ART over the other techniques. For instance, with 3 hrs duration, the BDL-IATS technique has provided lower ART of 3.12hrs whereas the ItemCF, UserCF, GBDT, and LightGBM techniques have obtained higher ART of 4.99, 4.79, 4.24, and 3.76 hrs respectively. In addition, with 12 hrs duration, the BDL-IATS method has provided lower ART of 3.39hrs whereas the ItemCF, UserCF, GBDT, and LightGBM techniques have gained higher ART of 4.96, 4.74, 4.29, and 3.79 hrs correspondingly. Moreover, with 18 hrs duration, the BDL-IATS approach has provided lower ART of 3.29hrs whereas the ItemCF, UserCF, GBDT, and LightGBM techniques have reached higher ART of 5.04, 4.79, 4.41, and 3.87 hrs respectively. Furthermore, with 24 hrs duration, the BDL-IATS technique has offered lesser ART of 3.17hrs whereas the ItemCF, UserCF, GBDT, and LightGBM methods have obtained higher ART of 5.07, 4.87, 4.37, and 3.85 hrs respectively.
By observing the comprehensive result analysis, it can be ensured that the BDL-IATS technique has resulted in enhanced performance over the other methods in the IATS environment.
This article has developed an effective BDL-IATS technique for secure cargo and vehicle matching in the IATS environment. The BDL-IATS technique has employed applies Ethereum as the fundamental blockchain for storing confidential data such as order details, cargo details, etc. Moreover, the DBN model is utilized to recommend vehicle and cargo matching during transportation. Furthermore, the CKHA is applied for the hyperparameter tuning of the DBN model. The performance validation of the BDL-IATS technique takes place and the results are inspected under varying aspects. The simulation results reported the better performance of the BDL-IATS technique over the recent state of art approaches. Therefore, the BDL-IATS technique can be utilized as a proficient tool for the IATS environment. In the future, data encryption techniques can be used to achieve secure communication in the IATS environment.