Electrical fires caused by leakage, short circuit, overload, and other electrical failures occur frequently in residential areas, and failure to find and locate the fault location in time is the main reason for the evolution of circuit fault into electrical fire, especially in the ancient buildings with aging circuit facilities. Different from modern large-scale buildings, ancient architectural complex is composed of a large number of scattered low buildings with inconsistent facility specifications and simple structure, so it does not have the conditions to deploy unified electrical lines and establish traditional largescale circuit monitoring system. In this study, we design an electrical fire monitoring IoT (EFM-IoT) framework, which can realize early fire warning by monitoring the residual current of appliances and is easy to deploy in ancient buildings. EFM-IoT adopts the data cleaning mechanism based on report form to filter redundant data, reducing computing and storage pressure in the cloud. Moreover, we analyze the energy consumption in the process of residual current and temperature data flow, and explore an effective energysaving mechanism for transmission. EFM-IoT has been successfully applied in Chinese historical and cultural street-Pingjiang Road of Suzhou. Extensive evaluation and analysis results demonstrate that EFM-IoT can effectively alleviate the data traffic and load in the cloud and reduce the response time of early warning.edge computing, electrical fire, Internet of Things, smart city | INTRODUCTIONElectrical fire accidents pose a great threat to the property and life of residents in urban areas. According to China Fire Rescue Bureau, electrical fires account for about 30% of all kinds of fires in China in the last decade. 1 Aging wires, poor quality electrical products, and violation operations are the main causes of these fires. 2 Ancient architectural complex with aging infrastructure and crowded commercial streets is the high-risk area of electrical fires. Therefore, fire-fighting monitoring of appliances in ancient architectural complex is of positive significance for the security of tourists and historic buildings. However, ancient architectural complex is scattered by numerous small-scale buildings, and systematic fire-fighting solutions, relying on unified facility deployment and distribution line remodeling, has restrictions in deployment scenarios that made it can hardly be deployed in preventing and monitoring electrical fire of these areas. 3,4

The emerging smart city is driving massive transformations of modern cities, facing the huge influx of sensor data from IoT devices. Edge computing distributes computing tasks to the near-edge end, which greatly enhances the service quality of IoT applications, that is, ultralow latency, large capacity, and high throughput. However, due to the constrained resource of IoT devices, currently, systems with a centralized model are vulnerable to attacks, such as DDoS from IoT botnet and central database failure, which can hardly provide high-confidence services. Recently, blockchain with a high security promise is considered to provide new approaches to enhancing the security of IoT systems. However, blockchain and IoT have obvious incompatibility, and low-capacity IoT devices can hardly be incorporated into blockchain with high computing requirements. In this paper, a blockchain-edge computing hybrid system (BEHS) is presented to make the adaptation of blockchain to edge computing and provide trustworthy IoT management services for a smart city. A novel extensible consensus protocol designed for proof-of-work, named proof-of-contribution (PoC), is proposed to regulate the data upload behaviors of nodes, especially the data upload frequency of IoT device nodes, so as to protect the system from attack about frequency. In order to secure the data privacy and authenticity, a data access control scheme is designed by integrating symmetric encryption with asymmetric encryption algorithm. We implemented a concrete BEHS on Ethereum, realized the function of PoC mechanism via smart contracts, and conducted a case study for smart city. The extensive evaluations and analyses show that the proposed PoC mechanism can effectively detect and automatically manage the behavior of nodes, and the time cost of data access control scheme is within an acceptable range.

In mobile Internet of Things, it is one key issue how to provide the reliable guarantee of transmission over sensors in connection with the services diversity, dynamic topology and various unknown interference. To solve these problems, we analyzed the Forward Error Control (FEC) sensing technology based on Cyclic Redundancy Check (CRC) and adaptive Multiple Input Multiple Output (MIMO) approach, building upon which we proposed the reliable transmission control scheme, in order to guarantee the Quality of Services. The mathematical analyses and NS simulation results show that the proposed transmission control scheme maintain the better performance such as throughput, reliability, real time performance and energy efficiency, than the FEC alone and MIMO alone approaches. Index Terms-Mobile internet of tings, FEC sensing, adaptive multiple-input multiple-output, reliable transmission I. INTRODUCTION Perception of the physical world and process of change, a lot of objects should be detected [1], [2], including temperature, pressure, humidity, strain, so the miniaturization and low-power sensors for applications is important and significant. There are some challenging issues of design, communication and transmission over sensors in Mobile Internet of Things (MIOT). Wireless communication environment of MIOT is dynamic and harsh, which have various interferences. Transmission and communication over this channel is susceptible to such distortions as finite bandwidth, resources, mobility, application service diversity, etc [3], [4]. These restrictions, combined with the characteristics of sensor networks [5] such as limited computation ability, storage and self-organization, make it difficult to guarantee Quality of Services (QoS) over MIOT.

The particularity of the underwater acoustic channel has put forward a higher request for collection and efficient transmission of the underwater image. In this paper, based on the characteristics of sonar image, wavelet transform is used to sparse decompose the image, and selecting Gaussian random matrix as the observation matrix and using the orthogonal matching pursuit (OMP) algorithm to reconstruct the image. The experimental result shows that the quality of the reconstruction image and PSNR have gained great ascension compared to the traditional compression and processing of image based on the wavelet transform while they have the same measurement numbers in the coding portion. It provides a convenient for the sonar image’s underwater transmission.

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