# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Data Transmission Networks
Results improved bandwidth in communication links and storage media. Regarding the improvement in systems or complete networks, [PERSON] et al. [27] have proposed a tool to improve the performance of the data transmission system of the real-time monitoring network for the surveillance of a specific volcanic area (Campi Flegrei, Italy). The authors' contribution helped increase transmission speed, network availability, and connection reliability, which together guarantee continuous monitoring and fault tolerance. For network quantification, the authors used performance, delay, standard deviation, and packet loss statistics. The quality parameters established were: real-time delay (msec. transmission), availability (continuous monitoring in real time), robustness (tolerance to faults) in transmission, acquisition, and visualization. The mentioned system has allowed review of network conditions, as well as activate, execution, and repair operations such as ping (lost packets and delays), throughput test (average throughput, network performance, change of frequency, and channel), and node check (periodic operations to detect and restore point-to-point links). These actions are recorded in reports and feedback in a MySQL database. On the other hand, the system has allowed review of seismic network conditions with the Earthworm seismic data acquisition system. The author quantified the effectiveness of the monitoring system by relating the number of failures in an observation interval with the average response time to repair them; with favorable results, they were able to improve availability by a 4.8%. Studies related to the instrumental network structures for seismic-volcanic monitoring and solutions implemented in data centers [28; 29] have proposed improvements for the autonomy of seismic equipment, mainly in terms of energy consumption and local storage. Other authors have proposed solutions for data acquisition systems, as well as arrays of sensors, digitizers, and wireless transmission designed to reduce energy consumption, autonomy, and extended operation [30]. Regarding efficient routing protocols, [PERSON] et al. [31] designed a routing protocol to reduce power consumption and latency using opportunistic routing. The sender selects the suitable ones from a group of neighboring nodes with specific criteria (priority of Q-maximum value, routing timeout for UWSN, and multipath suppression) for forwarding packets to the next hop with optimal routing. The results were obtained by mathematical analysis and simulation. Although the performance of this protocol was compared with other Q-learning protocols, any-path routing protocols based on \"greedy approaches\" do not consider long-term end-to-end rewards. Other studies have highlighted MAC protocols based on TDMA and subsequent comparisons [32; 33]. These are mainly improvements for wireless sensor networks in power consumption, latency, bandwidth, and network configuration topology (energy consumption, scalability, delay, traffic adaptability, throughput, and overheads). [PERSON] et al. [34] used WSN analysis and Markov Chain models related to the network performance, transmission time from the nodes to the gateway, power consumption, scalability, QoS, redundancy, and failure tolerance. In reference to data collection and efficient data transmission, [PERSON] et al. [35] proposed a mechanism for reliable data transmission applied to WSN, considering link loss rates and redundancy. A Markov decision process (MDP) identifies the level of packet redundancy to improve reliable data collection and decrease the rate of data transmission. Moreover, it analyzes the link state quality and considers other parameters such as the number of data transmissions, the average delivery delay, energy consumption, and network lifetime. The results obtained were used through experimentation. These advances could be applied in the future as an effective solution in environmental monitoring, tsunami alerts issued, or seismic sensor networks for real-time monitoring.