Abstract-To solve the defect of traditional node deployment strategy, the improved fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA) was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition. Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically. Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network. The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved. The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks. Keywords-wireless sensor networks; coverage optimization; sensor nodes IntroductionIn recent years, the Internet of Things, cloud computing and big data have developed rapidly. The Internet of things is the key technology of the information interaction between the future reality and the virtual. It is also the key research direction to realize the "information society". The Internet of Things mainly has three communication sensing technologies, namely, intelligent sensing, identification technology and pervasive computing. Internet of Things technology is widely used in information processing, in order to achieve a complete integration of the physical world and the information world. As a core technology of Internet of Things, Wireless Sensor Network (WSN) is attracting much attention. The wireless sensor has three functions, such as information collection, processing and transmission. They correspond to the three modern basic information technologies, namely, sensor technology, computer 58

This paper designs a failure signal remote collection terminal based on engineering mechanical hydraulic system. The terminal takes ATmega16 microcontroller as control core, using BenQ M22A module for GPRS wireless transmission. It overcomes region and time limit of engineering machinery and monitors engineering machinery's running status at any real-time, provides the basis for engineering machinery's breakdown service.

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In colleges and universities, financial work is not only related to the work and life of each teacher and student, but also related to the development of colleges and universities. In the era of big data, the accounting profession of colleges and universities is accompanied by the development of science and technology and network. The focus of its functions is from original cost-effective type to management type. The construction of financial comprehensive management systems in colleges and universities not only improves the efficiency of integrated information management in universities, but also strengthens the level of financial management to achieve a cashless accounting model; it can also improve the level of information construction in universities, strengthen the safety of financial payments, achieve transparency in official consumption, and contribute to the innovation and development of universities.

This paper puts forward a suitable channel estimation scheme for multiple input multiple output and orthogonal frequency division multiplexing system (MIMO-OFDM) based on discrete wavelet transform. According to the least-squares standard (LS), this plan uses pilot to estimate the unit impulse response of MIMO channel firstly, then does wavelet denoising in changing domain, in order to reduce the frequency spectrum leakage and improve the estimation precision. At the same time, this method does not need to know channel information in advance, and can follow up the changes of channel on time with good error rate performance.