Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the IoT are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this paper. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting (SAW) and multicriteria decision-making (MCDM) are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Due to resource constraints and working surroundings, many IIoT nodes are easily hacked and turn into zombies from which to launch attacks. It is challenging to detect such networked zombies rooted behind the Internet for any individual defender. In this article, we combine federated learning (FL) and fog/edge computing to combat malicious codes. Our protocol trains a global optimized model based on distributed datasets of collaborators while removing the data and communication constraints. The FL-based detection protocol maximizes the values of distributed data samples, resulting in an accurate model timely. On top of the protocol, we place mitigation intelligence in a distributed and collaborative manner. Our approach improves accuracy, eliminates mitigation time, and enlarges attackers’ expense within a defense alliance. Comprehensive evaluations confirm that the cost incurred is 2.7 times larger, the mitigation response time is 72% lower, and the accuracy is 47% higher on average. Besides, the protocol evaluation shows the detection accuracy is approximately 98% in the FL, which is almost the same as centralized training.

The increasing number of web APIs registered in service sharing communities (e.g., ProgrammableWeb.com that provides a platform benefiting the social interactions between different software developers) has provided a promising way to quickly build various apps with diverse functions. Generally, an app developer can manually discover, select, and compose a set of appropriate web APIs to build a new app satisfying the developer's functional and nonfunctional business requirements, economically and conveniently. However, the above manual web APIs selection process is usually time-consuming and cumbersome as most app developers often do not have much background knowledge of candidate web APIs. Moreover, the manually selected web APIs cannot always guarantee to be integrated successfully as the compatibilities between different web APIs are often varied and not validated. In view of these challenges, we define a weighted APIs correlation graph (W-ACG) in this paper to model the APIs functions and compatibilities. Furthermore, we propose a novel web APIs recommendation approach named Keywords-based and Compatibility-aware APIs Recommendation (K-CAR) based on the defined W-ACG. Through analyzing the input keywords describing the functions expected by an app developer, K-CAR can return the app developer a set of optimal APIs that are not only functional-qualified but also compatibility-guaranteed. Extensive experiments are deployed on 18,478 real-world web APIs and 6146 real-world apps to evaluate the usefulness and efficiency of K-CAR.

Crowdsourcing is a booming technique that enables participants to exchange data directly, thus making it possible to answer latency-sensitive service requests and relieve the burden of core networks. With some incentives, providers compete to furnish service requests, thus pledging the quality of experience (QoE) for requestors. However, the decentralized communication in crowdsourcing increases the probability of information tapering. Furthermore, providers' arbitrary selection of the requests poses great threat to the efficient and profitable service provision for the requestors. To deal with these challenges, we propose a blockchain-powered crowdsourcing method, named BPCM, while considering the privacy preservation in mobile environment. Specifically, a mobile crowdsourcing framework based on blockchain is designed first to preserve the privacy of the participants and keep the integrity of the service request and provision. Then, density-based spatial clustering of applications with noise (DBSCAN) and improved dynamic programming (IDP) are adopted to cluster the requestors and generate service strategies, respectively. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) are utilized to select the optimal strategy that achieves the tradeoffs among maximizing the service time, increasing the profits, and reducing the energy consumption for the providers. Finally, comprehensive experiments are conducted to verify the accuracy and effectiveness of BPCM.