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Traditional network data transmission (DT) has certain limitations in terms of processing, storage, and communication capabilities. Moreover, DT is easily affected by the network environment, which can reduce the real-time performance of DT. In severe cases, network transmission failures, DT interruptions, and other issues may even occur. As a new network, wireless sensor networks (WSN) have been widely used in military, industrial, agricultural, medical and other fields. In this paper, WSN was regarded as an important research method, and in-depth research was carried out around the real-time and energy consumption (for the convenience of the following text, the abbreviation for energy consumption is EC) of WSN DT. This paper focused on the issues of real-time performance, EC, and real-time transmission. It balanced EC and improved real-time performance through the low energy adaptive clustering hierarchy (LEACH) protocol and the power-efficient gathering in sensor information systems (PEGASIS) protocol. The experimental results showed that the real-time rates of LEACH under single reaction nodes were 45.5% and 48.6%, respectively. The lowest and highest real-time rates of PEGASIS were 86.2% and 89.5%, respectively. The real-time rate of PEGASIS was much higher than that of LEACH, proving that the proposed PEGASIS had high real-time performance and reduced DT delay.

The existing data transmission methods for industrial Internet of Things (IoT) ignore the tracking of abnormal nodes in the blockchain network, resulting in a low success rate of data transmission in industrial IoT, easy data loss, and low transmission efficiency. Therefore, a blockchain-based optimization method for industrial IoT data transmission is proposed. A B-Markov positioning model is constructed using neural networks to map IoT to consortium chains, and smart contracts are designed to eliminate abnormal nodes. Through dynamic optimization algorithms and centralized transmission models, combined with asymmetric encryption of blockchain, efficient data transmission is achieved. Experimental results show that this method has short-time consumption, high integrity, and a high success rate.

In this paper, we present a versatile Multicode Bi-Phase Encoder (MBPE) circuit capable of encoding five different Bi-Phase line codes, namely: Bi-Phase-Level (Bi-Φ-L), Bi-Phase-Mark (Bi-Φ-M), Bi-Phase-Space (Bi-Φ-S), Differential Manchester (DM) and Inverse Differential Manchester (IDM) codes. The design methodology is based on a new definition of these codes in terms of encoding rules and state diagrams, instead of the traditional way of representing them in terms of their bit transition. The operation mode of the MBPE is set by three selection lines, which can be either hardware or software controlled. This will facilitate the process of altering the data transmission protocol without the need of changing the encoder hardware. The functionality and design of the MBPE is outlined. VHDL has been used to describe the behavior of the MBPE whose operation was verified using the ModelSim XE II Simulation tools. Implementation and testing of the MBPE on XILINX Spartan-II FPGA showed that the MBPE circuit is capable of encoding NRZ data into any of the five codes.

The data transmission fails due to errors which have been generated by disconnections, noises and distortions in a communication line. To protect such failures, the following three schemes of error-control procedures are mainly used : (i) automatic-repeat-request (ARQ), (ii) forward-error-correction (FEC) and (iii) hybrid ARQ which combines FEC with ARQ. This paper proposes two stochastic models of hybrid ARQ policies with retransmission number K and response time T, and derives the mean times to success of data transmission. Comparing the mean times with each other, we discuss analytically which policy is better. These results are also evaluated in numerical examples.

Data transmission, such as task assignment, is important in software process environments. This paper presents a new scheme using XML-based event for data transmission. For example, "task" is a commonly used process data. A task is modeled as an XML-based event in which task components, such as role, tool, and document can be easily encapsulated. And, the event can be transmitted over the web — simply as a message. Moreover, XML-based event is capable of modeling different types of data elements in which document type definition (DTD) can be used to validate correctness of the data.

The real-time data analysis requires an integrated approach to know the last known state of variables of a concept under monitoring. Thereby, the Internet-of-Thing (IoT) devices have provided alternatives to address distributed data collection strategies. However, the autonomy of IoT devices represents one of the main challenges to implement the collecting strategy. Battery autonomy is affected directly by the energy consumption derived from data transmissions. The Data Stream Processing Strategy (DSPS) is an architecture oriented to the implementation of measurement projects based on a measurement and evaluation framework. Its online processing is guided by the measurement metadata informed from IoT devices associated with a component named Measurement Adapter (MA). This paper presents a new data buffer organization based on measurement metadata articulated with online data filtering to optimize the data transmissions from MA. As contributions, a weighted data change detection approach is incorporated, while a new local buffer based on logical windows is proposed for MA. Also, an articulation among the data buffer, a temporal barrier, and data change detectors is introduced. The proposal was implemented and released on the pabmmCommons library. A discrete simulation on the library is here described to provide initial applicability patterns. The data buffer consumed 568 Kb for monitoring 100 simultaneous metrics. The online estimation of the mean and variance based on the Statistical Process Control consumed 238 ns. However, as a limitation, other scenarios need to be addressed before generalizing results. As future work, new alternatives to filter noise online will be addressed.

This article introduces the high-rise building fire escape ZigBee positioning systems and data transmission optimization method. An analysis of the ZigBee routing path was carried out based on the characteristics of high-rise building fires and extremely large numbers of people. In order to address the problem of delays in transferring data, a large amount of data was used when starting the optimization of the routing path.