The world-changing blockchain technique provides a novel method to establish a secure, trusted, and decentralized system for solving the security and personal privacy problems in Internet of Things (IoT) applications. As the mining process in blockchain requires high computational power, the lightweight IoT devices need to purchase computational resources from edge servers and thus can offload their computational tasks. In this paper, we investigate the incentive mechanism for the blockchain platform to attract IoT devices to purchase more computational power from edge servers to participate in the mining process, thereby building a secure blockchain network while guaranteeing the profits of the blockchain platform. We model the interaction between the blockchain platform and IoT devices as a two-stage Stackelberg game, where the blockchain platform act as the leader and IoT devices act as followers. We analyze the existence and uniqueness of the Stackelberg equilibrium, and propose an efficient algorithm to compute the Stackelberg equilibrium point. Furthermore, we evaluate the performance of our algorithm through extensive simulations, and analyze the strategies of the blockchain platform and IoT devices under different situations.

Along with the development in the internet of things technology and smart city, a distributed network has been formed among cities. This makes it easy to integrate distributed electric energy into the power grid, thus become an efficient way to use energy. However, how to guarantee the security and privacy protection of distributed electricity trading has not been solved effectively. In this paper, we propose a blockchain-based electricity trading (B-ET) ecosystem and design a smart contract to ensure transactions are conducted in a safe and reliable manner. To overcome the shortcomings of high latency in traditional proof-of-work (PoW) consensus, we proposed a credit-based PoW consensus mechanism by integrating the concept of “stake” to improve the consortium blockchain under the B-ET ecosystem. Then, we take combined cooling, heating, and power (CCHP) system as an example that supplies distributed energy, and model its interactions with the agent of power gird by a novel Stackelberg game. We show that the optimal utilities of entities in a city can be obtained at the Stackelberg equilibrium by a distributed algorithm, which is guaranteed to exist and be unique. In the end, we conduct a number of numerical simulations to evaluate our proposed model and verify our algorithms, which demonstrate their correctness and efficiency completely.

Real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous accidents. In this article, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of Vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget but also maintains the truthfulness of the auction process that prevents some vehicles from offering unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.

Mobile crowd sensing (MCS) is a new paradigm for urban-scale monitoring. This paper concentrates on the Cost-Fair Task Allocation (CFA) problem for the MCS scenario where the collaboration of multiple probabilistic mobilephone users is needed to yield more reliable observation. CFA aims to allocate sensing tasks to users so that the sensing costs undertaken by all users are as balancing as possible, while the requirement of the requester for data reliability can be satisfied. CFA is greatly important to MCS campaigns in terms of reliability and sustainability. We design two algorithms to solve the CFA problem in the offline and online cases, respectively. Specifically, we propose a novel penalty-based model to reformulate the offline CFA problem, and based on this model, we design an offline algorithm, which can yield a computation-efficient ε-solution with any small ε > 0. For the online case, we design a polynomial-time approximation algorithm, which struggles to allocate each of the sequentially arriving tasks to users as fairly as possible, and can achieve an upper-bounded competitiveness relative to the optimal CFA solution. Finally, we conduct extensive numeric analyses to validate the performance of our algorithms under diverse experimental setups.

The rapid growth in demand for large language models (LLMs) has strained cloud-edge infrastructure. While edges offer low latency and clouds provide vast resources, scheduling LLM requests efficiently remains a major challenge due to their unpredictable processing times, which leads to Headof-Line (HOL) blocking that degrades system throughput and responsiveness. To address this, we introduce the Online CloudEdge Collaborative Request Scheduling (OCE-CRS) framework. OCE-CRS models the proactive scheduling of LLM requests as a contextual combinatorial bandit problem. At its core is our novel Combinatorial Neural Delayed Upper Confidence Bound (CN DUCB) algorithm, which learns to predict request processing times from the semantic content of the request prompt alone. This enables an inspired policy based on Shortest Job First (SJF) that prioritizes shorter jobs for edge execution, simultaneously maximizing throughput and mitigating HOL blocking. To prevent time-consuming neural network training from blocking scheduling decisions, we employ an asynchronous mechanism. This decouples model updates from the real-time scheduling loop, effectively handling the resultant delayed feedback where observations from past rounds are used in later training steps. We provide a theoretical sublinear regret bound for our algorithm. Extensive experiments validate that OCE-CRS significantly improves throughput, Job Completion Time (JCT), and queueing delay, demonstrating superior performance and robustness in both static and continuous batching environments.