Gajjala Savithri^1,2, N. Raghavendra Sai^1,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2547-2569, 2024, DOI:10.32604/cmc.2024.055827 - 18 November 2024

Abstract In the world of wireless sensor networks (WSNs), optimizing performance and extending network lifetime are critical goals. In this paper, we propose a new model called DTLR-Net (Deep Temporal LSTM Regression Network) that employs long-short-term memory and is effective for long-term dependencies. Mobile sinks can move in arbitrary patterns, so the model employs long short-term memory (LSTM) networks to handle such movements. The parameters were initialized iteratively, and each node updated its position, mobility level, and other important metrics at each turn, with key measurements including active or inactive node ratio, energy consumption per cycle,… More >

Jin Wang^{1, 2, 3}, Yu Gao², Chang Zhou², R. Simon Sherratt⁴, Lei Wang^{5, *}

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 695-711, 2020, DOI:10.32604/cmc.2020.08674

Abstract Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such… More >