Mean Time Between Failures/Mean Time to Repair (MTBF/MTTR) are important parameters for measuring the reliability of systems. Shorter MTBF/MTTR normally means high service quality. However, a reasonable level of reliability is usually not defined. In this paper, the calculation method of MTBF/MTTR is presented. This paper also provided the simulation model and simulation experiment oriented service systems for the quantitative analysis of the relation between MTBF/MTTR and performance metrics of service systems. In the paper, the technique is developed for defining the rational level of reliability, and a practical case is used to demonstrate the aforementioned approaches.

Analysis of MTBF/MTTR for Logistics Service System

Message conflicts caused by large propagation delays severely affect the performance of Underwater Acoustic Networks (UWANs). It is necessary to design an efficient transmission scheduling algorithm to improve the network performance. Therefore, we propose a Deep Reinforcement Learning (DRL) based Time-Domain Interference Alignment (TDIA) scheduling algorithm (called DRLSA-IA). The main objective of DRLSA-IA is to increase network throughput and reduce collisions. In DRLSA-IA, underwater nodes are regarded as agents of DRL. Nodes intelligently learn the scheduling by continuously interacting with the environment. Therefore, DRLSA-IA is suitable for the highly dynamic underwater environment. Moreover, we design a TDIA-based reward mechanism to improve the network throughput. With the TDIA-based reward mechanism, DRLSA-IA can achieve parallel transmissions and effectively reduce conflicts. Unlike other TDIA-based algorithms that require enumeration of the state space, nodes merely feed the current state to obtain the transmission decision. DRLSA-IA solves the problem of computational expense. Simulation results show that DRLSA-IA can greatly improve the network performance, especially in terms of throughput, packet delivery ratio and fairness under different network settings. Overall, DRLSA-IA can effectively improve network performance and is suitable for ever-changing underwater environments.

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Deep Reinforcement Learning Based Time-Domain Interference Alignment Scheduling for Underwater Acoustic Networks

With the development of underwater internet, Underwater Acoustic Sensor Networks (UASNs) have become an important method for collecting marine parameters and data. The Medium Access Control (MAC) protocol is the underlying infrastructure of UASNs. It directly affects the utilization of the underwater acoustic channel and the overall performance of the network. The ALOHA protocol is the basis of the random accessing protocols. It freely accesses the channel, so the collision between data packets is very serious. To this end, this paper proposes a Backoff-Toleration-Based Opportunistic (BTBO) protocol. In this protocol, each sensor node opportunistically learns by backoffing tolerantly, and finds the optimal transmission time respectively to transmit the data packet. Simulation results show that the BTBO protocol has better throughput and average end-to-end delay. At the same time, the authors added adaptive design to the dynamic topology networks in the UASNs. The simulation results show that the protocol can effectively alleviate these problems and improve performance. 

Backoff-toleration-based opportunistic MAC protocol for underwater acoustic sensor networks

Many marine scientific and industrial applications in underwater acoustic sensor networks (UASNs) are characterized by dynamic-high load and dense deployment. In such UASNs, the channel reservation mechanism is devised to deal with channel utilization degradation. Unfortunately, channel reservation would lead to an increase in end-to-end delay and a decrease in throughput due to long propagation delay in UASNs. To reduce end-to-end delay and improve throughput, we proposed an efficient distributed medium access control (ED-MAC) protocol for UASNs. ED-MAC reduces the impact of long propagation delays on channel reservation by removing the channel reservation confirmation process and improving channel reservation efficiency. First, the optimized time slot length for the next time frame is obtained based on the data packet number estimate and secondary transmission data packet number, which could reduce idle waiting time. Then, the channel reservation confirmation process is removed by distributed channel reservation mechanism, and efficient channel reservation is achieved through channel reservation packet collision-free transmission and channel multiplexing. Meanwhile, channel reservation is performed before packet generation. Thus, the data packet waiting delay is reduced. Finally, a distributed data packet time slot schedule scheme is proposed to achieve collision avoidance between different data packets, as well as data packets and channel reservation packets. The performance analysis and simulation results show that our proposed ED-MAC could efficiently reduce end-to-end delay and improve throughput in dynamic-high-load and dense UASNs. Simulation results show that ED-MAC can achieve a maximum 98.7% reduction in average end-to-end delay and a maximum 61.1% improvement in throughput. 

An Efficient Distributed MAC Protocol for Underwater Acoustic Sensor Networks

In a Web service-based sampling system (WSSS) with multiple smart sensors, where the smart sensors request the sampling-commands (SCs) from the Web server with polling mechanism, and the Web server picks up the targeted estimates (TEs) from all the estimates received, higher polling frequency and more estimate-submissions enable the Web server to obtain all TEs (ATE) with larger probability, enjoying less delays, while suffering more network traffic. Therefore, how to minimize the network traffic while satisfying the acceptable delays and probability of the server obtaining ATE arises as an interesting issue. To address this issue, this paper first proposes a Markov decision process (MDP) framework to formulate the request process of WSSS, then explores out the optimal polling frequency and probability in the polynomial time for the smart sensors&#x0027; requests, and finally proposes a probability threshold-based algorithm to guide the smart sensors to decide the polling frequency and whether to request the SCs. Theoretical results, simulations, and field experiments document and illustrate its performance. 

Request Control of Web Service-Based Sampling System With Multiple Smart Sensors

Underwater wireless sensor networks (UWSNs) have a long propagation delay; thus, when multiple nodes access the channel at the same time, the nodes cannot perceive the channel status in time, which causes access conflicts between nodes. To reduce multinode access conflicts and improve network throughput, a random handshake MAC protocol based on Nash equilibrium (RHNE-MAC) is proposed. In the RTS phase, RHNE-MAC regards nodes sending control packets to channels as competitive behavior and an incomplete information game. This paper firstly discusses the collision probability of two nodes sending data to the master node at the same time when the nodes are evenly distributed. Subsequently, according to the collision probability and the size of the network, the mathematical expectation concept is added to the payment function. Then, it uses the Nash equilibrium theory to solve the game, which allows the network to dynamically adjust the probability of nodes requesting access to a channel according to the number of nodes, thereby minimizing the problem of multinode access conflicts. Finally, the master node schedules the data transmission for each node according to the successful receipt of control packet information and propagation delay information of the network; therefore, multiple nodes can transmit data after a handshake period. The simulation results show that the throughput is increased by approximately 12% compared with prescheduling-based MAC and by 207% compared with TDMA; moreover, the end-to-end delay also has better performance.

RHNE-MAC: Random Handshake MAC Protocol Based on Nash Equilibrium for Underwater Wireless Sensor Networks

This paper mainly discussed about the reliability of the flexible manufacturing system(FMS),established a reliability model of FMS based on the generalized stochastic Petri nets and dynamic fault treeTimeNet was used to simulate models,these simulation experi ments verified the feasibility and effectiveness of the model,then improved the reliability and security of system

Chen Yan-xia

Papers

RHNE-MAC: Random Handshake MAC Protocol Based on Nash Equilibrium for Underwater Wireless Sensor Networks

Reliability Formal Modeling and Analysis of the Flexible Manufacturing System