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Jijin, J.;  Seet, B.;  Chong, P.H.J. Smart-Contract-Based Automation for OF-RAN Processes. Encyclopedia. Available online: (accessed on 17 April 2024).
Jijin J,  Seet B,  Chong PHJ. Smart-Contract-Based Automation for OF-RAN Processes. Encyclopedia. Available at: Accessed April 17, 2024.
Jijin, Jofina, Boon-Chong Seet, Peter Han Joo Chong. "Smart-Contract-Based Automation for OF-RAN Processes" Encyclopedia, (accessed April 17, 2024).
Jijin, J.,  Seet, B., & Chong, P.H.J. (2022, October 27). Smart-Contract-Based Automation for OF-RAN Processes. In Encyclopedia.
Jijin, Jofina, et al. "Smart-Contract-Based Automation for OF-RAN Processes." Encyclopedia. Web. 27 October, 2022.
Smart-Contract-Based Automation for OF-RAN Processes

The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. 

smart contract automation opportunistic fog radio access network industrial Internet-of-Things federated deep learning blockchain computation offloading

1. Introduction

Recent growth in big data and the use of artificial intelligence (AI) in the Internet-of-Things (IoT) led to an increasing need of resources for computation offloading and AI model training. However, advanced AI techniques such as deep learning (DL) are computationally resource-intensive if executed on IoT devices with low computation capacity. Hence, the need for them to offload their DL tasks to more resourceful devices increased significantly. Traditional offloading to the cloud via a cloud radio access network (C-RAN) has several issues, such as heavy workload at centralized baseband units (BBUs), limited backhaul capacity, and difficulty in serving delay-sensitive applications [1]. Consequently, researchers proposed fog radio access network (F-RAN), in which fog access points (FAPs) are deployed at the network edge to serve the IoT devices. These FAPs can be existing infrastructure entities further equipped with fog functionalities, or new entities deployed in an existing infrastructure [2]. However, existing F-RANs do not leverage the presence of available resourceful user devices to utilize their high, but idle, computation resources.
Researchers argue that the DL tasks are more apt to be offloaded to an opportunistic F-RAN (OF-RAN), which researchers proposed in [3]. OF-RAN enhances the F-RAN by harnessing the concept of opportunistic networks (oppnets), a type of ad hoc network for utilizing available local resources in an opportunistic manner [4]. Each oppnet is established by a seed node that assigns one or more helper nodes to assist with a specific task. In OF-RAN, the role of the seed node and service node is equivalent to that of FAP in F-RAN, and helper node in oppnet. A seed node in the OF-RAN recruits locally available resourceful user devices, such as high-end smartphones and tablets, as service nodes, which collectively form a virtual FAP (v-FAP) to serve a resource-limited client, e.g., an IoT device.
In this entry, researchers consider an important problem that has yet to be addressed for OF-RAN to meet real-world deployment requirements, which is a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Automating these repetitive processes can improve operational efficiency, reduce the cost of service delivery, and help move towards a zero-touch network management model. The automation mechanism must be custom designed for the specific processes of OF-RAN, but such a mechanism has not been proposed for OF-RAN in the literature, to the best of researchers' knowledge.
Motivated by the recent emergence of blockchain technology with smart contracts as an enabler of trusted and distributed systems, this entry proposes an automated mechanism using smart contracts for OF-RAN processes, built on researchers' follow-up preliminary work on a blockchain-enabled OF-RAN in [5]. The system architecture of the proposed smart-contract-enabled OF-RAN is shown in Figure 1. At the access layer, seed nodes are infrastructure devices, such as Wi-Fi access points (APs) and pico- and femto-cell base stations (BSs) equipped with fog functionalities. Each seed node is a blockchain node, which hosts a smart contract, maintains a copy of the blockchain, and establishes a blockchain network with other seed nodes. At the terminal layer, each client is served by a v-FAP formed by multiple service nodes. The selection of service nodes in a v-FAP, placement of service tasks into service nodes, and processing of service tasks are all executed automatically, according to the smart contract.
Figure 1. Smart-contract-enabled OF-RAN.
To demonstrate the role that researchers' smart-contract-based automation for OF-RAN processes can play in real life applications, a federated DL use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented on a physical testbed.

2. Blockchain with Smart Contracts for Distributed Systems

Smart contracts and blockchain technologies are among the key enablers of Industry 4.0. This section reviews works on using blockchain with smart contracts for distributed systems to secure and automate their processes.
In [6], the authors proposed a blockchain-based secure DL for IoT, which supports collaborative DL with device integrity and confidentiality. The rules and policies to regulate the learning and mining tasks are defined in the form of smart contracts residing in the blockchain. The learning task is performed locally in IoT devices, and the learned local models are aggregated at an edge server acting as a blockchain node that mines and coordinates blockchain transactions. The proposed system is shown to be efficient in terms of accuracy, time delay, and security. However, due to limited resources of IoT devices, it is not suitable when large or complex learning tasks are involved.
The authors in [7] proposed blockchain-assisted federated learning for edge nodes to cooperatively train and predict popular files to be cached for IoT devices. Each edge node trains its local model, and then compresses and sends the local gradients to a cloud server for aggregation and update of the global model. The updated global model parameters are then returned to the edge nodes for further training or selecting files to be cached. In order to record, secure, and verify transactions, a smart contract constituting the following is proposed: (i) identity contract: verifies identity of IoT and edge nodes; (ii) submission contract: provides interface for edge nodes to submit their gradients to the blockchain; (iii) verification contract: elects supervisory consortium to verify transactions; (iv) credit contract: reward/penalizes participants. The proposed system is shown to improve cache hit rate and reduce file upload time. Although a blockchain is used, not much has been explored about the impact of blockchain parameters, such as block size and block interval, on the caching efficiency or security.
In [8], a security architecture for IoT networks based on software-defined networking (SDN), blockchain, and fog/edge computing is proposed. Decentralization in blockchain is used to secure sharing of IoT data and resources. A SDN-enabled edge switch continuously monitors the data flow to the fog nodes where traffic traces are learned and analyzed to identify malicious traffic flows. DL algorithms are used to detect attacks at network edge. A central cloud server manages the attack detection model in the fog nodes, which can be a processing or proofing agent. The processing agent trains the local model using local data obtained from the edge switch, while the proofing agent aggregates local models obtained from proofing agents and verifies the resulting attack detection model. All three entities (manager, processing agent, and proofing agent) interact with each other through transactions effectuated using a smart contract residing in the blockchain. The authors show that their architecture performs well in mitigating attacks, but do not evaluate its performance impact on delay-sensitive applications.
The authors in [9] presented EdgeChain, a blockchain-based architecture to make mobile edge application placement decisions for mobile hosts of multiple service providers (SPs). It uses the logic of the placement algorithm as a smart contract with the consideration of resources from all mobile edge hosts participating in the system. However, the proposed algorithm only considers fairness in resource sharing among multiple SPs. Other factors such as energy consumption and end-to-end latency, which are important to energy-constrained mobile devices and delay-sensitive applications, have not been considered. To reduce the blockchain’s energy and computation requirements without compromising its traceability and non-repudiation, a lightweight blockchain system known as LightChain is proposed in [10]. It features a consensus mechanism with low computing power consumption, a lightweight data structure for information broadcast, and a method to limit the growing storage cost of the ledger.
In [11], a secure federated learning technique called Deepchain is proposed, where blockchain cryptographic features are used to preserve privacy of local gradients and guarantees auditability of training process. Its smart contract comprises a trading contract and processing contract, which guide the secure training process. It is evaluated in terms of cipher size, throughput, accuracy, and training time. Similarly, in [12], blockchain is employed to secure federated learning, but with the additional use of digital twins of end devices at edge servers to mitigate the issue of unreliable transmission links. However, it is unclear how deviations in data between end devices and their digital twins can impact the resulting edge intelligence. Furthermore, using blockchain to secure federated learning process can incur high mining costs and long information exchange delays due to the consensus protocol of the blockchain network. In contrast, researchers' proposal herein does not use blockchain to secure federated learning, but information about the resourceful user devices in the OF-RAN, in order to facilitate their selection as service nodes for a v-FAP to perform federated learning or other offloading services.

3. System Model

Figure 2a shows the system model of the proposed smart-contract-enabled OF-RAN, in which the seed node and service nodes that constitute a v-FAP manage and execute the computation tasks offloaded by a client, respectively. The following explains the function of each key entities in the model:
Figure 2. (a) System model; (b) Sequence of operations.
  • Smart Contract: Defines the rules and logic for automating the OF-RAN processes through four sub-contracts: (i) registration; (ii) selection and placement; (iii) service; and (iv) mining. The registration contract registers interested resourceful user devices as potential service nodes. The selection and placement contract firstly selects a set of user devices based on the cost of using their resources as service nodes in a v-FAP, and then executes OF-RAN’s task-to-node assignment (TNA) as defined in researchers' follow-up work in [13]. The TNA is a process for the placement of the service tasks into the service nodes based on performance criteria such as node energy, process latency, and fairness in workload distribution. The service contract implements the service logic, which is application-specific. As a use-case of researchers' proposed smart contract for OF-RAN, the federated learning application is chosen. The mining contract is responsible for new block generation from the transaction data generated upon executing the service contract to update the ledger;
  • I/O Interface: For both seed node and service nodes to exchange information when serving a client;
  • Local Application Model: A service node’s application model that processes information from the seed node and generates a local outcome for the client;
  • Global Application Model: A seed node’s application model that collates the local outcome from each service node and generates a global outcome for the client;
  • Lookup Table: Records the identity and performance of each service node, which can be looked up for future selection of service nodes when a new v-FAP is to be formed;
  • Application Output: The global outcome generated by the global application model. In federated learning use-case, the application output is the aggregated weight, also referred to as global update for the client’s DL model.
Figure 2b shows the sequence of operations of researchers' system. For a resource-limited client to offload its task to OF-RAN, it first sends a service request {1} including the task requirements to its associated remote radio head (RRH), which in turn notifies the client {2} of an available nearby seed node to offload its task. The client then offloads its task {3} to this seed node in the form of data and initial model parameters. The seed node splits the task into sub-tasks, and, based on the TNA scheme, places the sub-tasks into each service node {4}. Upon processing, the service nodes send their local outcomes {5} to the seed node for collation. Finally, the seed node generates and returns a global outcome {6} to the client.
In researchers' proposed smart-contract-enabled OF-RAN, every seed node is a blockchain node that monitors the transactions between nodes in a v-FAP. On completing the client’s task, the seed node updates its lookup table, and then mines a new block from it as proof-of-work for propagation to blockchain under a permissioned consensus protocol [14]. Thus, computations performed in the v-FAP for an offloading application are unaffected by the blockchain computation performed by the seed node. This ensures that the delay-sensitive applications can be supported.


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