Recently, mobile crowd sensing (MCS) is captivating growing attention because of their suitability for enormous range of new types of context-aware applications and services. This is attributed to the fact that modern smartphones are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform more complex tasks besides their inherent calling features. Despite a number of merits, MCS confronts new challenges due to network dynamics, the huge volume of data, sensing task coordination, and the user privacy problems. In this paper, a comprehensive review of MCS is presented. First, we highlight the distinguishing features and potential advantages of MCS compared to conventional sensor networks. Then, a taxonomy of MCS is devised based on sensing scale, level of user involvement and responsiveness, sampling rate, and underlying network infrastructure. Afterward, we categorize and classify prominent applications of MCS in environmental, infrastructure, social, and behavioral domains. The core architecture of MCS is also described. Finally, we describe the potential advantages, determine and reiterate the open research challenges of MCS and illustrate possible solutions.

Actually, wireless sensor networks represent a substantial part in IoT. However, their limitation requires a special consideration in IoT applications. For their integration with the internet, it is necessary to adapt such networks using different middleware, with taking into account various challenges such as heterogeneity and interoperability. Previously Service Oriented Architecture (SOA) was the suitable design, but with a better practice, a new design called microservice becomes the leader due to its high performance and its suitability for IoT applications. In this paper, we first survey the most important middleware that have been proposed to handle WSN through IoT. Also, we discuss the most crucial microservices that handle different integration factors by making them supported by the proposed middleware. Our proposal is inspired from artificial neural network architecture to allow dynamic service interaction; it supports unlimited services with a regard to various device capabilities separately of the cloud technologies. Moreover, the evaluation of our design clearly shows that our middleware allows a lightweight WSN integration with IoT regarding to their limitations and requirements.

Due to the intrinsically geo-distributed subcontracting maintenance activity practice, the maintenance scheduling has for a long time been a major challenge in the industry. This research work presents a methodology to schedule the maintenance activities of geo-distributed assets. A multi-agent system based approach is proposed to enable the decision-making for the subcontractors in a distributed industrial environment under uncertainty. An auction based negotiation mechanism is designed to promote competition and cooperation among the different agents, and to obtain global good schedule.Compared to the Weighted Shortest Processing Time first–Heuristic–Earliest Due Date (WSPT-H-EDD) method, the experimental results show that the proposed approach is able to provide scheduling scheme with good performances in terms of Global Cost, Total Weighted Tardiness Cost and makespan.