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The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here. The room-temperature operated SINGOR system can be self-driven by indoor light with a Si solar cell, and it features ultrahigh sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10, 2, 1, and 1 ppb, respectively. Each SINGOR consisting of an array of nanostructured sensors has the capability of gas pattern recognition and classification. Furthermore, multiple SINGOR systems are wirelessly connected as a sensor network, which has successfully demonstrated flammable gas leakage detection and alarm function. They can also achieve gas leakage localization with satisfactory precision when deployed in one single room. These successes promote the development of using nanostructured-gas-sensor network for wide range applications including smart home/building and future smart city.

The Internet of Things (IOT) is an Internet-based network that covers everything. Message Queuing Telemetry Transport (MQTT) is one of the commonly used communication protocols for any platform in the Internet of Things. Whether it is from the perspective of overall security or device compatibility and resource consumption, the MQTT protocol has certain advantages and is the most competitive communication protocol in the current Internet of Things. However, the user password login form adopted by the MQTT protocol has a certain degree of security problems. IOTA is an encrypted currency, and the Tangle network it uses is a new distributed structure. Based on this situation, this article innovatively combines the Tangle network with the MQTT protocol, and proposes a new communication option for MQTT nodes. When a new node enters the network, start from the Broker with the help of a “walker”, by judging the connection and survival of the node, using the Markov Chain Monte Carlo (MCMC) random walk algorithm to complete the node selection, and finally realize MQTT Communication between nodes. This article puts forward the design idea of this scheme, and realizes the scheme through simulation experiment.

As the feature size of devices decreases and the number of transistor interconnect exceeds billions, the runtime of global router becomes a problem in very large-scale integrated circuit design. The congestion prediction in the global routing, which can cost huge time, is one of the most important and challenging problems. In this paper, we propose a density and pins peaks-based fast neural network algorithm (NNA) to predict congestion map. In order to save runtime, traditional prediction methods are replaced by a machine learning method with local density and pins as features. Furthermore, to improve the performance of proposed method, the predictive identification method to specify order of the rip-up and reroute for congested regions is also proposed. Compared with state-of-the-art router, NTHU-Route 2.0, on ISPD08 benchmarks, our method greatly improves the speed of predicting routing congestion information by 1094% in test cases, and saves runtime of the global router.