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With the development of Internet, various kinds of new applications appear constantly. They all have high requirements to the time delay, throughput, especially strong real-time applications such as mobile monitoring, video calls. The satellite network in Navigation Satellite System, which is necessary for the mobile monitoring, has many disadvantages such as asymmetric bandwidth, unstable network, high bit error rate and so on. This is a new challenge to the existing congestion control method. In order to improve the performance of congestion control mechanism, we put forward TCP-QS (Quick Start) from the TCP protocol control in this paper. TCP-QS algorithm mainly optimize the slow start stage. At the beginning of the connection, the value of parameter cwnd is set as a larger value according to the detected network bandwidth in which way, the time of the slow start stage is shortened during the transmission, and is adjusted the value of parameter ssthresh dynamically according to the change of network. When packet loss occurs, it takes different methods according to the different reasons.

Wireless Sensor Networks (WSN) is widely used as an effective medium to integrate physical world and information world of Internet of Things (IOT). While keeping energy consumption at a minimal level, WSN requires reliable communication. Multicasting is a general operation performed by the Base Station, where data is to be transmitted to a set of destination nodes. Generally, the packets are routed in a multi-hop approach, where some intermediate nodes are also used for packet forwarding. This problem can be reduced to the well-known Steiner tree problem, which has proven to be NP-complete for deterministic link descriptors and cost functions. In this paper, we propose a novel multicast protocol, named heuristic algorithms for the solution of the Quality of Service (QoS) constrained multicast routing problem, with incomplete information in Wireless Sensor Networks (WSN). As information aggregation or randomly fluctuating traffic loads, link measures are considered to be random variables. Simulation results show that the Hop Neural Networks (HNN) based heuristics with a properly chosen additive measures can yield to a good solution for this traditionally NP complex problem, when compared to the best multicast algorithms known.

A gear tooth or a gear is usually considered as an independent component in the traditional reliability research. However, gear trains are significantly different from the traditional series systems. Time-dependent configuration of the traditional series systems is fixed. But the meshing tooth-pairs of gear trains are changing all the time during operation. The failures of meshing tooth-pairs are dependent statistically under the common load action. In the paper, the reliability analysis considering time-domain meshing relation of tooth-pairs is studied and meshing relation equations are presented. Then, reliability analysis method of gear trains is developed based on the Monte Carlo simulation with dependent failure and time domain meshing relation. The differences of reliability results between time domain reliability analysis method and traditional series system reliability methods are illustrated by several examples.