The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging

Time sensitive networks support deterministic schedules over Ethernet networks. Due to their high determinism, high reliability and high bandwidth, they have been considered as a good choice for the backbone network of industrial internet of things. In industrial applications, the backbone network connects multiple industrial field networks together and has to carry massive real-time packets. However, the off-the-shelf time-sensitive network (TSN) switches can deterministically schedule no more than 1024 real-time flows due to the limited number of schedule table entries. The excess real-time flows have to be delivered by best-effort services because the switch only supports the two scheduling services. The best-effort services can reduce average delay, but cannot guarantee the hard real-time constraints of industrial applications. To make the limited number of schedule table entries support more real-time flows, first, we relax scheduling rules to reduce the requirement for schedule table entries and formulate the process of transmitting packets as a satisfiability modulo theories (SMT) specification. Then, we divide the SMT specification into multiple optimization modulo theories (OMT) specifications so that the execution time of solvers can be reduced to an acceptable range. Second, we propose fast heuristic algorithms that combine schedule tables and packet injection control to eliminate scheduling conflicts. Finally, we conduct extensive evaluations. The evaluation results indicate that, compared to existing algorithms, our proposed algorithm requires only one-twentieth the number of schedule entries to schedule the same flow set.

Time-Sensitive Networking (TSN) provides end-to-end data transmission with extremely low delay and high reliability on the basis of Ethernet. It is suitable for time-sensitive applications and will be widely used in scenarios such as autonomous driving and industrial Internet. IEEE 802.1Qbv proposes a time-aware shaper mechanism, which enables switches to control the forwarding of traffic in port queues according to pre-defined Gate Control List (GCL). The length of the GCL is limited, and the previous method of scheduling cycle with a hyper period may result in a larger GCL. Based on Satisfiability Modulo Theories (SMT), we propose a TSN scheduling method for industrial scenarios and develops a series of scheduling constraints. Different from the previous scheduling methods, the method proposed in this paper adopts the base period cycle to update GCL regularly, which can effectively reduce the number of time slots in GCL and make the configuration of GCL simpler and more efficient. In addition, compared with the traditional hyper period method, the method proposed in this paper can calculate the scheduling results faster while ensuring low latency and reducing the runtime effectively.

A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to $22\%$ relative to existing methods.

WirelessHART is a networking technology that is widely used in industrial wireless sensor networks. Its reliability and real-time performance are essential to industrial production. Many works have studied these two aspects, primarily focusing on a single WirelessHART network. However, multiple WirelessHART networks usually coexist in a real industrial environment. Applying existing approaches to such coexisting networks would cause performance degradation due to communication interference among these networks. In this paper, we propose a holistic framework that optimizes both reliability and temporality for multiple coexisting networks. The framework consists of two levels. The upper level targets communication channel management, and the lower level addresses data flow scheduling. For the upper level, we propose a network isolation algorithm that improves the data transmission reliability through dynamically adjusting channel assignments to different WirelessHART networks. For the lower level, we propose data flow scheduling algorithms that guarantee the temporality of data flows within each isolated network. These algorithms minimize the number of channels reserved by each isolated network and further enhance the transmission reliability through alleviating channel resource contention. We conduct trace-driven simulations of the channel management algorithm, and the results demonstrate that our algorithm exhibits stable performance and reduces packet loss by 36%. For the scheduling algorithms, the simulations demonstrate that in contrast with existing algorithms, the greater the number of coexisting networks, the fewer resources our algorithms use. When eight networks coexist, our algorithms outperform existing ones by consuming up to 63% fewer channel resources.