In the context of the telecom network trending towards centralization, the relatively decentralized and citizen-centric, non-profit network architecture known as Wireless Community Network (WCN) has emerged. However, WCN faces challenges related to unintentional shifts towards centralization, the lack of automation and verifiability to handle increasing volumes of information, and the absence of real-time and more flexible incentive mechanisms to incentivize a diverse range of contributors based on the quality of their contributions. As the network expands, maintenance becomes more challenging for small volunteer teams, potentially compromising network performance, reliability, and overall trustworthiness. With the development of the decentralized physical infrastructure network (DePIN), this paper proposes a methodology for designing a decentralized wireless community network (DeWCN) system. This methodology includes the design of a consensus layer, a trust and reputation management layer, and presents incentive-driven oracle design methodologies for verifiable common resource pools. This is the first work in the DePIN domain discussing the design of DeWCNs. Unlike projects like Helium [1], we eliminate the need for specialized devices and mining-based token systems.

Although PoW has achieved great success over the last decade, its core problem, energy waste, has not been well answered. The purpose of PoL is to use the wasted computing power in another field that is also consuming massive computing power, training large-scale deep learning models. The success of PoW is largely due to that its proof can be verified in constant time. In this paper, we propose a new method for replacing cryptopuzzles with large-scale model training based on the observation of the similarity between the gradient changes during model training and hash functions. Our new consensus mechanism can verify a proof in constant time. Experiments show that the performance of our proposed new consensus mechanism meets expectations.

Since Bitcoin, blockchain has attracted the attention of researchers. The consensus mechanism at the center of blockchain is often criticized for wasting a large amount of computing power for meaningless hashing. At the same time, state-of-the-art models in deep learning require increasing computing power to be trained. Proof of Learning (PoL) is dedicated to using the originally wasted computing power to train neural networks. Most of the previous PoL consensus mechanisms are based on two methods, recomputation or performance metrics. However, in practical scenarios, these methods both do not satisfy all properties necessary to build a large-scale blockchain, such as certainty, constant verification, therefore are still far away from being practical. In this paper, we observe that the opacity of deep learning models is similar to the pre-image resistance of hash functions and can naturally be used to build PoL. Based on our observation, we propose a method called Directed Guiding Gradient. Using this method, our proposed PoL consensus mechanism has a similar structure to the widely used Proof of Work (PoW), allowing us to build practical blockchain on it and train neutral networks simultaneously. In experiments, we build a blockchain on top of our proposed PoL consensus mechanism and results show that our PoL works well.

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.