Given a Vehicular Fog Computing (VFC) scenario, computing tasks with varying sensitivity levels must be offloaded while jointly balancing latency, energy, and trust requirements. Existing trust-based schemes often neglect some of these trade-offs or fail to adapt to task sensitivity and node reliability in dynamic vehicular settings. This paper proposes a trust-based task offloading (TTO) framework that employs a multi-dimensional trust model, combining Reputation, Experience, and Knowledge (REK) to compute a trust-aware overhead function that adapts to task requirements.We also incorporate computable encryption options, enabling task owners (TOs) to protect highly sensitive tasks. The interaction among TOs is modeled as a potential game, which implies the finite-improvement property and the existence of a Nash equilibrium, enabling distributed, self-adaptive decision-making that aligns individual incentives with overall system efficiency. This design allows vehicles to achieve an optimal balance between trust assurance and performance without centralized coordination. The efficiency of the framework against centralized approaches is validated by analyzing the price of anarchy. The simulation results confirm that TTO converges in 60 iterations for 100 nodes, achieving a 30-35% lower average delay and up to 25% lower total overhead compared to the considered baselines. These results indicate that TTO effectively balances trust, latency, and energy requirements.

With the increasing global mobile data traffic and daily user engagement, technologies, such as mobile crowdsensing, benefit hugely from the constant data flows from smartphone and IoT owners. However, the device users, as data owners, urgently require a secure and fair marketplace to negotiate with the data consumers. In this paper, we introduce a novel federated data acquisition market that consists of a group of local data aggregators (LDAs); a number of data owners; and, one data union to coordinate the data trade with the data consumers. Data consumers offer each data owner an individual price to stimulate participation. The mobile data owners naturally cooperate to gossip about individual prices with each other, which also leads to price fluctuation. It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario. Hence, we propose a data pricing strategy based on mean-field game (MFG) theory to model the data owners’ cost considering the price dynamics. We then investigate the interactions among the LDAs by using the distribution of price, namely the mean-field term. A numerical method is used to solve the proposed pricing strategy. The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme. The result further demonstrates that the influential LDAs determine the final price distribution. Last but not least, it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.

Information-enabled technology, such as digital contact tracing (DCT), is designed to assist decision-making. However, during the pandemic, DCT also contributes additional factors to consider for individuals and policymakers. In this article, we conceptualise the interactions between individuals and policymakers and propose a leader-followers mean-field Game model to analyse their decision-making processes by integrating key factors, such as individuals’ health state, self-control effort, privacy, and social activities, under the influence of interventions and subsidies from policymakers. The simulation demonstrates that moderate subsidies are sufficient to induce early control efforts and higher collective efforts even with privacy concerns and the negative influence of engaging activities; Equilibrium and system stability can be reached when healthy or the summation of healthy and mediocre healthy populations dominates; The model highlights the crucial elements for future data analysis and collection, such as DCT is vital to indicate population health states.

Digital transformation is increasingly reliant onservice-based operations in fog networks. The latter is a geo-dispersed form of the cloud, extending resources closer to end-users for improved privacy and reduced latency. The dispersion leverages diversity of compute-network capacities and energy prices, while promotes the coexistence of multiple providers. This drives variation in operational cost, coupled with limited information sharing across providers. Consequently, there is a critical need for an orchestration solution that preserves autonomy and optimizes operational cost across domains, while meeting service requirements. This paper proposes a novel service-based fog management and network orchestrator (sbMANO), which utilizes service metadata in enabling multi-provider resource management. The sbMANO is empowered with a novel optimization algorithm for service-based joint request mapping and response routing. The algorithm acts on partial information and preserves the edge for delay-critical services. The performance of the algorithm is evaluated analytically fordelay-awareanddelay-agnosticvariants. The results show that both achieve near-optimal performance in maximizing user satisfaction with minimum operational cost. Furthermore, the delay-aware variant outperforms the agnostic counterpart, with higher user satisfaction and lower operational cost.

With the increasing demand for digitalization and participation in Industry 4.0, new challenges have emerged concerning the market of digital services to compensate for the lack of processing, computation, and other resources within Industrial Internet of Things (IIoTs). At the same time, the complexity of interplay among stakeholders has grown in size, granularity, and variation of trust. In this paper, we consider an IIoT resource market with heterogeneous buyers such as manufacturer owners. The buyers interact with the resource supplier dynamically with specific resource demands. This work introduces a broker between the supplier and the buyers, equipped with Distributed Ledger Technologies (DLT) providing a service for market security and trustworthiness. We first model the DLT-assisted IIoT market analytically to determine an offline solution and understand the selfish interactions among different entities (buyers, supplier, broker). Considering the non-cooperative heterogeneous buyers in the dynamic market, we then follow an independent learners framework to determine an online solution. In particular, the decision-making procedures of buyers are modeled as a Partially Observable Markov Decision Process which is solved using independent Q-learning. We evaluate both the offline and online solutions with analytical simulations, and the results show that the proposed approaches successfully maximize players’ satisfaction. The results further demonstrate that independent Q-learners achieve equilibrium in a dynamic market even without the availability of complete information and communication, and reach a better solution compared to that of centralized Q-learning.

The Internet of Things (IoT) connected by software-defined networking (SDN) promises to bring great benefits to cyber-physical systems. However, the increased attack surface offered by the growing number of connected vulnerable devices and separation of SDN control and data planes could overturn the huge benefits of such a system. This article addresses the vulnerability of the trust relationship between the control and data planes. To meet this aim, we propose an edge computing-based Blockchain as a Service (BaaS), enabled by an external BaaS provider. The proposed solution provides verification of inserted flows through an efficient, edge-distributed, blockchain solution. We study two scenarios for the blockchain reward purpose: 1) information symmetry, in which the SDN operator has direct knowledge of the real effort spent by the BaaS provider and 2) information asymmetry, in which the BaaS provider controls the exposure of information regarding spent effort. The latter yields the so-called “moral hazard,” where the BaaS may claim higher than actual effort. We develop a novel mathematical model of the edge BaaS solution and propose an innovative algorithm of a fair reward scheme based on game theory that takes into account moral hazard. We evaluate the viability of our solution through analytical simulations. The results demonstrate the ability of the proposed algorithm to maximize the joint profits of the BaaS and SDN operator, i.e., maximizing the social welfare.

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.

The Mobile Edge Network (MEN) has emerged as the basic infrastructure to support fifth-generation networks, mobile edge computing and fog computing. The characteristics of mobility must be addressed to guarantee the quality of service in MENs. As one of the critical problems in MENs, flexible deployment plays a part in exploiting edge networks. Despite the abundance of recently proposed strategies, most concentrate on the change in user demands and inevitably ignore the influence for the user mobility, which is common in future networks. We propose Provision for Mobile Edge Computing (PMEC), a prototype that takes advantage of storage devices with flexible placement. In PMEC, we accommodate various considerations and select different storage devices to cache, deploying the cache with the relationship of a two-tiered structure in the MEN. Thus, we construct a flexible overlay network with the objective of minimizing the cost in a two-tiered edge network. Based on the analysis of the problem, we solve the two-tiered placement from bottom to top using the dynamic minimal spanning tree (MST) algorithm and design two algorithms for each tier including the basic algorithm and the improved algorithms. The simulation is conducted on realistic data to demonstrate the performance of our algorithms.