Name
Abstract | ||
|---|---|---|
With the development of the Internet of Underwater Things (IoUT), two critical problems have been prominent, i.e., the energy constraint of underwater devices and large demand for data collection. In this article, we introduce an autonomous underwater vehicle (AUV)-aided underwater acoustic sensor networks (UWSNs) to solve these problems. To improve the performance of UWSNs, we formulate an optimization problem to maximize the energy consumption utility, which is defined to balance the energy consumption and network throughput. To solve this optimization problem, we decompose it into four parts. First, due to the constraint of communication distance, we construct a cluster-based network and formulate the selection of cluster heads as a maximal clique problem (MCP). Second, the clustering algorithm is proposed. Third, we design a novel media access control (MAC) protocol to coordinate data transmission between AUV and cluster heads, among intracluster nodes, as well as among intercluster nodes. Finally, path planning of AUV is formulated as a traveling salesman problem to minimize AUV travel time. Based on the above analysis, two algorithms, namely, AUV-aided energy-efficient data collection (AEEDCO) and approximate AUV-aided energy-efficient data collection (AEEDCO-A), are developed accordingly. The simulation results show that the proposed algorithms perform well and are very promising in UWSNs with demand for large-scale communication, large system capacity, long-term monitoring, and high data traffic load. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JIOT.2020.2988697 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Clustering algorithms,Data collection,Energy consumption,Throughput,Delays,Prediction algorithms,Internet of Things | Journal | 7 |
Issue | ISSN | Citations |
10 | 2327-4662 | 6 |
PageRank | References | Authors |
0.40 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoxiao Zhuo | 1 | 6 | 1.75 |
| Meiyan Liu | 2 | 6 | 1.07 |
| Yan Wei | 3 | 6 | 1.75 |
| Guanding Yu | 4 | 6 | 0.40 |
| Fengzhong Qu | 5 | 196 | 19.02 |
| Rui Sun | 6 | 6 | 0.40 |