Secure data sharing and storage are critical to realizing the ultra-high safety and efficiency promised by intelligent transportation systems (ITS). However, ensuring data integrity under stringent resource constraints of ITS remains an open challenge. Existing approaches either incur substantial computational and transmission overhead or demand resources beyond what ITS environments can provide. To address this dilemma, we propose a lightweight blockchain-based data sharing and storage framework tailored for ITS. The system features a distributed, asynchronous reputation mechanism that enables trustworthy data evaluation and dissemination without burdening critical computing and communication paths. Additionally, a resource-efficient blockchain storage layer ensures low-latency, tamper-resistant access to locally shared data. Extensive simulations demonstrate that our approach outperforms existing solutions in both integrity assurance and resource efficiency.

Open Radio Access Networks (O-RANs) are transforming the landscape of telecommunications to better performance and higher cost-efficiency by enabling network operators to integrate diverse vendor components. Nevertheless, the involvement of multiple Network Service Providers (NSPs) and Mobile Network Operators (MNOs) also brings new challenges in the management of computation and network resources. To resolve this challenge, we propose a blockchain-based framework to guarantee secure, transparent, and decentralized resource allocation in O-RAN systems. Our resource allocation mainly considers the tradeoff of cost and service quality. Our design facilitates real-time adjustments to resource distribution based on dynamic network conditions and incorporates user feedback to optimize service quality continuously. By integrating a Proof-of-Reputation (PoR) consensus mechanism, the framework enhances the reliability and integrity of transactions among competing vendors without central oversight. We evaluate the performance of our design through extensive simulations, which demonstrate significant improvements over the baselines in resource utilization and service delivery across various network scenarios.

Quantum Internet has potential capabilities far beyond the traditional Internet and is thus a promising future platform for communication and computation. Entanglement is a cornerstone of quantum mechanics and forms the basis of numerous quantum applications in the quantum Internet. While existing studies primarily focus on two-user entanglement, a plethora of applications necessitates the leap to multi-user entanglement. This paper tackles the fundamental problem of multi-user entanglement routing in the quantum Internet, aiming to entangle multiple quantum users with a high entanglement rate. We abstract the problem as a novel graph routing problem, which is not readily addressed by existing graph problem solutions due to the unique characteristics of the quantum Internet. To address this problem, we first consider a sufficient condition ensuring a feasible solution's existence and design an algorithm with the optimal solution. Given the NP-Completeness and NP- Hardness of determining a feasible solution's existence and deriving an optimal solution in general cases, respectively, we propose two heuristic algorithms to offer efficient solutions, which are shown, via extensive simulations, to outperform the existing algorithms in terms of entanglement rates.

Quantum networks have emerged as a future platform for quantum information exchange and applications, with promising capabilities far beyond traditional communication networks. Remote quantum entanglement is an essential component of a quantum network. How to efficiently design a multi-routing entanglement protocol is a fundamental yet challenging problem. In this paper, we study a quantum entanglement routing problem to simultaneously maximize the number of quantum-user pairs and their expected throughput. Our approach is to formulate the problem as two sequential integer programming problems. We propose efficient entanglement routing algorithms for these two optimization problems and analyze their time complexity and performance bounds. Evaluation results highlight that our approach outperforms existing solutions in both the number of quantum-user pairs served and network throughput.

Generating a long-distance quantum entanglement is one of the most essential functions of a quantum network to support quantum communication and computing applications. The successful entanglement rate during a probabilistic entanglement process decreases dramatically with distance, and swapping is a widely-applied quantum technique to address this issue. Most existing entanglement routing protocols use a classic entanglement-swapping method based on Bell State measurements that can only fuse two successful entanglement links. This paper appeals to a more general and efficient swapping method, namely n-fusion based on Greenberger-Horne-Zeilinger measurements that can fuse n successful entanglement links, to maximize the entanglement rate for multiple quantum-user pairs over a quantum network. We propose efficient entanglement routing algorithms that utilize the properties of n-fusion for quantum networks with general topologies. Evaluation results highlight that our proposed algorithm under n-fusion can greatly improve the network performance compared with existing ones.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We also introduce a data relay process that can enhance data accessibility in wireless edge networks. We formulate a revenue sharing problem to maximize the profit of both the data producer and resellers/relayers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers/relayers. Extensive simulations show that with resellers and relayers, our mechanism can achieve up to 49.5 percent higher profit for the data producer and resellers/relayers.

Internet of Things (IoT) is progressively becoming an essential aspect of daily life that can be sensed anywhere and anytime, transforming the traditional lifestyle into a high-tech one. Numerous applications in the edge are brought to life based on IoT infrastructures. Especially, edge computing has witnessed the proliferation and impact of IoT-enabled devices benefiting from the data collection and computation capabilities of IoT. However, establishing an IoT from scratch can be monetarily expensive, and leasing the existing sub-networks confronts the potentially dishonest behavior of service providers. To address these issues, we propose a novel framework of leasing edge IoT networks and analyze the influence of sub-network owners’ dishonest behavior on the network. We model the interaction between the edge user and the owners of sub-networks by a Stackelberg game with a unique equilibrium, jointly analyzing the pricing and data collection mechanisms. The Primal-dual Decomposition algorithm and its theoretical analyses are provided for the corresponding strategies of the edge user and sub-network owners. Evaluations demonstrate that the proposed algorithm in the leasing model can save data collection cost up to 53% compared with existing data collection strategies, and illustrate the difference in network performance compared with the game without dishonest owners.

Edge caching is an effective way to reduce congestion and latency in 5G networks. Non-volatile memory (NVM) devices are developing fast, with the potential of fast access, and higher endurance versus traditional storage devices, to further boost mobile data offloading efficiency in 5G networks. This paper studies how to effectively use the two-layer storage system (NVM-enhanced) in 5G edge caching. We first model an edge caching optimization problem with NVM storage devices included and develop a parallel distributed algorithm with guaranteed convergence in joint caching and routing decisions. A fully decentralized algorithm for scenarios without any coordination is further developed which also guarantees the convergence. Real-world trace-driven simulations and experiments over a small-scale system demonstrate that NVM significantly boosts the performance of edge caching and the proposed algorithms outperform the existing ones.

Quantum networks are considered as a promising future platform for quantum information exchange and quantum applications, which have capabilities far beyond the traditional communication networks. Remote quantum entanglement is an essential component of a quantum network. How to efficiently design a multi-routing entanglement protocol is a fundamental yet challenging problem. In this paper, we study a quantum entanglement routing problem to simultaneously maximize the number of quantum-user pairs and their expected throughput. Our approach is to formulate the problem as two sequential integer programming steps. We propose efficient entanglement routing algorithms for the two integer programming steps and analyze their time complexity and performance bounds. Results of evaluation highlight that our approach outperforms existing solutions in both served quantum-user pairs numbers and the network expected throughput.

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system to enhance the security for transactions and determine the incentive for miners in edge computing environments. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem for a fair incentive to PoW miners. We formulate the problem and propose an iterative and another heuristic algorithm to determine the incentive that the miner will receive for a new block. We further prove that the iterative algorithm can obtain global optimal results. Numerical simulation results show that our proposed algorithm can give a reasonable incentive to miners under different system parameters in edge blockchain systems.

This paper considers a federated temporal difference() (TD()) learning algorithm and provides both asymptotic and finite-time analyses. To protect each worker agent cost information from being accessed by possible attackers, we propose a privacy-preserving variant of the algorithm by adding perturbation to the exchanged information. We show the rigorous differential privacy guarantee by using moments accountant and derive an upper bound of the utility loss for the privacy-preserving algorithm. Evaluations are also provided to corroborate the efficiency of the algorithms.

How to serve mobile users in rural areas by 5G networks is challenging due to the sparse distribution of base stations and poor connection to the cloud. Existing solutions focus on transmission frequency implementation such as frequency multiplexing, In this paper, we consider a decentralized caching scheme for two reasons. First, caching contents in the edge is an effective approach to reduce the transmission latency and improve the quality of service for mobile users. Second, the decentralized caching allows base stations to serve mobile users without any coordination of the cloud. Meanwhile, data privacy in the edge is critical for individual users. This paper aims to jointly determine the caching and routing policy in rural areas of 5G networks in a decentralized manner and simultaneously design a proper privacy-preserving mechanism. We tackle the challenges in two progressive steps. First, we design a decentralized algorithm with the convergence guarantee. Furthermore, we enhance the developed decentralized algorithm with a privacy-preserving mechanism based on (local) differential privacy and prove its privacy guarantee. We conduct extensive numerical simulations based on real-world traces to evaluate the proposed algorithms. Results highlight significant performance improvements compared to existing baselines.

Distributed edge caching has drawn great attention with the fast development of smart edge devices. Caching popular contents in the edge can reduce latency and improve the quality of service of edge mobile users. Meanwhile, the data privacy in the edge is critical to preserve the privacy of individual users and devices. How to jointly determine the caching and routing policy in the edge network in a distributed manner and simultaneously design the proper privacy preserving mechanism are challenging. We tackle these challenges in two progressive steps. First, we design a distributed algorithm which can achieve the global optimum. Second, we propose a privacy-preserving mechanism based on differential privacy and prove the privacy guarantee. We conduct extensive numerical simulations based on real-world requests to evaluate the performance of the proposed distributed algorithm and the privacy mechanism. Results highlight a significant improvement of the proposed distributed algorithm while only up to 10.1% of the total serving cost increased by the privacy mechanism.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We formulate a fair revenue sharing problem to maximize the profit of both the data producer and resellers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers. Extensive simulations show that with resellers, our mechanism can achieve higher profit for the data producer and resellers.

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system in edge environments to enhance the security for transactions and determine the incentive for miners. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem that gives the corresponding PoW miner when a new block generates. We further formulate it into a two-stage Stackelberg game. We propose an algorithm and prove that it can obtain the global optimal results for the incentive that the miner will receive for a new block. Numerical simulation results show that our proposed algorithm can give reasonable incentive to miners under different system parameters in edge blockchain systems.

Edge caching is an effective approach to improve the quality of service for mobile users and therefore a critical component for 5G networks. Despite the importance, it is not clear how to determine which contents to cache and how to the serve requests in 5G networks to minimize the total operational cost in a distributed and online manner, especially when some mobile users can be served by multiple small base stations. In this paper, we formulate an optimization problem to jointly decide the caching policy and the routing decision. There are two challenges: the need for distributed control and the lack of future information. We therefore develop an online distributed algorithm with provable performance guarantees in terms of convergence and competitive ratio compared to the offline optimal solution. Numerical simulations based on real-world traces highlight the significant performance improvement compared to existing baselines.

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

This paper considers how to cache popular contents and load balancing in 5G networks to minimize the total operating cost. Specifically, popular contents requested by mobile users (MUs) are cached in small base stations (SBSs) to serve them with better quality and lower cost because the SBSs are often much closer to MUs than the base station (BS). Due to limited caching capacity and bandwidth of SBSs, the caching policy and load balancing algorithm need to be carefully designed jointly and dynamically over time. In this paper, we formulate the joint content placement and load balancing by an online optimization problem. This problem is challenging because of the integer constraint in content placement and the lack of future information. We tackle the challenges in two progressive steps. First, we propose a primal-dual algorithm to solve the problem efficiently and prove it always achieves the optimal cost assuming all system information is available. Then we integrate promising online optimization algorithms with the proposed primal-dual algorithm so that only limited short-term predictions are needed. Theoretical performance bounds are also derived. We conduct extensive numerical simulations to evaluate the performance of proposed algorithms. Results highlight that the proposed online algorithms can reduce the system cost significantly (by as much as 27%) compared to the existing solutions and perform similarly to the offline optimal solution.

This paper considers a mobile data collecting problem in a wireless sensor network with private residual sensor networks for the scenario in which the owners of residual sensor networks may perform dishonest behavior. The interaction between the wireless sensor network operator and the owners of residual sensor networks is modeled by a Stackelberg game which has a unique Stackelberg equilibrium. The influence of the Stackelberg equilibrium caused by the dishonest residual sensor networks owner are analyzed. An algorithm and a theoretical analysis are provided for the corresponding strategies of the operator and owners. Simulations are conducted to illustrate the difference of network performance compared with the game without dishonest residual owners.

This paper studies a data gathering problem in a wireless sensor network containing multiple private residual subnetworks. The interaction between the wireless sensor network operator and the owners of residual sub-networks is modeled by a Stackelberg game, which forms a novel framework for jointly analyzing the pricing, gathering data, and planning routes. It is shown that the game has a unique Stackelberg equilibrium at which the wireless sensor network operator sets prices to minimize total cost, while owners of residual sub-networks respond accordingly to maximize their utilities subject to their bandwidth constraints. An algorithm and theoretical analyses are provided for the corresponding strategies of the operator and owners, and validated by extensive simulations. It is demonstrated that the algorithm achieves lower network cost compared with existing data gathering strategies.