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The unstable link quality in wireless sensor networks (WSNs) directly affects the success rate of data transmission. The retransmission mechanism is one of the commonly used methods to solve this problem. However, too many retransmissions could lower the communication efficiency. Therefore, to reduce retransmissions while guaranteeing the communication reliability in WSNs, this study introduces the NarrowBand Internet of Things (NB‐IoT) technology, and builds a network including both sensor and NB‐IoT nodes. The NB‐IoT node is designed to support both the radio frequency and the NarrowBand communication modes; thus it can communicate with both sensor nodes and NB‐IoT base stations. Further, the communication reliability metrics considering both the link quality and the number of retransmissions in different communication modes are defined, based on which, an adaptive communication reliability algorithm is proposed to switch the communication modes of nodes. The simulation results verify that the proposed algorithm can achieve higher success rate with less end‐to‐end delay and flexibly control the cost on NB‐IoT communication. [ABSTRACT FROM AUTHOR]

The emergence of computation offloading can meet the real‐time requirements of computing tasks with intensive computing demands. In this study, the authors use opportunistic communication to construct a network framework for opportunistic edge computing (OEC) to perform computation offloading. Specifically, OEC forms a computing resource pool near the edge servers in the edge layer by gathering idle computing resources. Firstly, the state of the system is defined by the attributes of the computing task, the execution location of the computing task and the location of the terminal device in OEC. Then the computation offloading time is calculated and learned by selecting different offloading nodes. Finally, an optimal offloading node selection strategy based on the Q‐learning algorithm is obtained. Extensive simulations show that the proposed strategy consumes the minimum computation offloading time compared with benchmark algorithms in aspects of the amount of uploaded data, the total number of CPU cycles of the task and the number of computing tasks. [ABSTRACT FROM AUTHOR]