A Stable and Reliable Short-Path Routing Scheme for Efficient Acoustic Wireless Sensor Networks (AWSNs)

Abstract

Owning to the vital resources in a harsh and unforeseeable aqueous environment, the network stability and reliability in underwater acoustic wireless sensor networks (UAWSNs) have paramount significance. Stability guarantees the consistent performance of the network node's energy consumption, avoids data loss, packets reception time and network lifetime. The reliability of packet ensures the selection of favorable channel and avoid adverse channel effects, and the vital information is easily obtained from data packets. This paper introduces two new routing schemes for UAWSNs; stable and reliable short-path routing (RSPR) scheme, and cooperative reliable short-path routing (CoRSPR). In RSPR routing, the destination node is selected by considering the weighting function parameters of the highest residual energy, highest SNR, lowest euclidean distance, and least number of neighbor nodes. The scheme reduces the energy consumption due to less number of nodes contribution in the packet advancement process. The RSPR protocol is a non-cooperative technique, where the packets are delivered using a single-path link, which may not be consistently reliable. To cope with this issue, the CoRSPR protocol is proposed, which takes cooperative routing into account, for stable and reliable data delivery. In cooperative routing, the reception of more than one copy of the data packet is involved by the destination node. This reduces the unfavorable channel effects during data delivery. The simulation results show that the proposed schemes achieve better performance in terms of dead nodes, energy left in the battery, packet acceptance ratio, successful receiving of packets at the sink and E-2-E delay. INDEX TERMS Acoustic wireless sensor networks, short-path routing, network reliability, RSPR, CoRSPR.

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

“…To achieve the first objective which is called , the Euclidean distance between the sensor nodes and their neighbours heading in the direction is calculated. Subsequently, this is utilised to select the best next hop to minimise transmission distance [ 29 ]. The second objective function is the residual energy for each node.…”

Adaptive Priority Scheduling of Internet of Things Data for Disaster Management in Smart Cities

“…model is adaptive only on the basis of the node's energy state and mentions the probability of congestion without, however, proposing a solution to this problem. The model established in [37] introduces two schemes for path selection for which the criterion for both methods is a weighting function of the highest residual energy, highest signal-to-noise reduction (SNR), smallest distance, and Bit Error Rate (BER). The chosen weighed criteria are good avenues for more in-depth analysis in similar research.…”

“…Some suppose the evolution of traffic conditions and the state of the network to be static, and the latter are only accounted for when initiating the data transfer. For the methods claiming to perform adaptive scheduling, most of them are oriented toward energy efficiency [33]- [34], [36]- [37] and are more focused on path planning. Their schemes do not ponder the effect of network traffic characteristics [33], the fairness toward low-priority packets [18] and the emphasis on urgent data prioritization [34] for emergency situations.…”

MuLSi-Co: Multilayer Sinks and Cooperation-Based Data Routing Techniques for Underwater Acoustic Wireless Sensor Networks (UA-WSNs)

“…In addition, the noise degrades the quality of the information packets. These two major problems are incorporated in [30]. For these two problems, two different routing protocols are designed.…”