Impact of Network Load on Direct Acyclic Graph Based Blockchain for Internet of Things

Author(s):  
Bin Cao ◽  
Shouming Huang ◽  
Daquan Feng ◽  
Lei Zhang ◽  
Shengli Zhang ◽  
...  
2020 ◽  
Vol 28 (4) ◽  
pp. 1643-1656 ◽  
Author(s):  
Yixin Li ◽  
Bin Cao ◽  
Mugen Peng ◽  
Long Zhang ◽  
Lei Zhang ◽  
...  

2020 ◽  
Author(s):  
Tanweer Alam ◽  
Baha Rababah ◽  
Rasit Eskicioglu

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.


Author(s):  
Baha Rababah ◽  
Tanweer Alam ◽  
Rasit Eskicioglu

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.


Author(s):  
Caixiang Fan ◽  
Sara Ghaemi ◽  
Hamzeh Khazaei ◽  
Yuxiang Chen ◽  
Petr Musilek

Distributed ledgers (DLs) provide many advantages over centralized solutions in Internet of Things projects, including but not limited to improved security, transparency, and fault tolerance. To leverage DLs at scale, their well-known limitation (i.e., performance) should be adequately analyzed and addressed. Directed acyclic graph-based DLs have been proposed to tackle the performance and scalability issues by design. The first among them, IOTA, has shown promising signs in addressing the preceding issues. IOTA is an open source DL designed for the Internet of Things. It uses a directed acyclic graph to store transactions on its ledger, to achieve a potentially higher scalability over blockchain-based DLs. However, due to the uncertainty and centralization of the deployed consensus, the current IOTA implementation exposes some performance issues, making it less performant than the initial design. In this article, we first extend an existing simulator to support realistic IOTA simulations and investigate the impact of different design parameters on IOTA’s performance. Then, we propose a layered model to help the users of IOTA determine the optimal waiting time to resend the previously submitted but not yet confirmed transaction. Our findings reveal the impact of the transaction arrival rate, tip selection algorithms, weighted tip selection algorithm randomness, and network delay on the throughput. Using the proposed layered model, we shed some light on the distribution of the confirmed transactions. The distribution is leveraged to calculate the optimal time for resending an unconfirmed transaction to the DL. The performance analysis results can be used by both system designers and users to support their decision making.


Author(s):  
P.Y. Calland ◽  
A. Mignotte ◽  
O. Peyran ◽  
Y. Robert ◽  
F. Vivien

2016 ◽  
Vol 16 (03) ◽  
pp. 1650012 ◽  
Author(s):  
Divya Tomar ◽  
Sonali Agarwal

As most of the plant species are at the risk of extinction, the task of plant identification has become a challenging process and an active area of research. In this paper, we propose a leaf recognition system for plant species classification using leaf image data through a novel direct acyclic graph based multi-class least squares twin support vector machine (DAG-MLSTSVM) classifier. Hybrid feature selection (HFS) approach is used to obtain the best discriminant features for the recognition of individual plant species. Leaves are recognized on the basis of shape and texture features. The experimental results indicate that the proposed DAG-MLSTSVM based plant leaf recognition system is highly accurate and having faster processing speed as compared to artificial neural network and direct acyclic graph based support vector machine.


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