Clustered geographic-opportunistic routing protocol for underwater wireless sensor networks

The most adverse characteristics of underwater wireless sensor network (UWSN) communications are high propagation delay, high error rate, very low bandwidth, and limited available energy. The energy resources replacement is also more expensive. The proposed clustering-based geographic- opportunistic routing with adjustment of depth-based topology control for communication recovery of void regions (C- GEDAR). The cluster-based GEDAR routes the packet to the surface of sonobuoys with the help of clusters. The void sensor node recovery algorithm is used to recover the void nodes to calculate their new depth. The proposed routing protocol is to be simulated and its performances are evaluated by using an Aquasim simulator. The simulated result shows that C-GEDAR performs better average energy consumption, good packet delivery ratio (PDR) and less end-to-end delay.

FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.