Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

No abstract

No abstract

No abstract

AbstractIn the Vehicular ad hoc networks (VANETs), due to the high mobility of vehicles, the network parameters change frequently and the information which the sender maintains may outdate when it wants to transmit data packet to the receiver, so for improving the routing effective, we propose the probability prediction based reliable (PRO) opportunistic routing for VANETs. The PRO routing algorithm can predict the variation of Signal to Interference plus Noise Ratio (SINR) and packet queue length (PQL) in the receiver. The prediction results are used to determine the utility of each relaying vehicle in the candidate set. The calculation of the vehicle utility is weight based algorithm and the weights are the variances of SINR and PQL of the candidate relaying vehicles. The relaying priority of each relaying vehicle is determined by the value of the utility. By these innovations, the PRO can achieve better routing performance (such as the packet delivery ratio, the end-to-end delay, and the network throughput) than the SRPE, ExOR (street-centric), and GPSR routing algorithms.Index TermsOpportunistic routing, Vehicular ad hoc networks, SINR, Packet queue length, Probability prediction. I. INTRODUCTIONVehicular ad hoc network (VANETs) is a kind of network which combines the wireless communication with the vehicles to enable the vehicles to communicate with each other [1] [2]. Due to the specific characteristics of VANETs, the VANETs are quite different with the traditional mobile ad hoc networks (MANETs). For instance, the speed of vehicles in VANETs is much higher than that in MANETs; the moving directions of vehicles in VANETs are limited by the urban streets; higher probability of network partition in VANETs than that in MANETs due to the traffic light [3]; due to the different structures of the streets (for instance, one-/two-way street, two-/four-lanes street), the network topologies are quite different with different streets [4], which is called topology diversity. Therefore, the routing algorithms in VANETs are different with that in traditional MANETs. The routing algorithms which are effective in MANETs may have poor performance in VANETs.There are two routing strategies for the VANETs: deterministic routing and opportunistic routing [5]. In deterministic routing, the sender sends data packet to one neighbor vehicle which is chosen based on the optimal algorithms. In opportunistic routing, the sender sends the data packet to a set of relaying vehicles rather than only one relaying vehicle to improve the packet delivery ratio between sender and receiver. In this paper, we mainly focus on the opportunistic routing. A. MotivationThe main advantage of opportunistic routing compared with

In this paper, we present a Minimum Spanning Tree (MST) based topology control algorithm, called Local Minimum Spanning Tree (LMST), for wireless multi-hop networks. In this algorithm, each node builds its local minimum spanning tree independently and only keeps on-tree nodes that are one-hop away as its neighbors in the final topology. We analytically prove several important properties of LMST: (1) the topology derived under LMST preserves the network connectivity; (2) the node degree of any node in the resulting topology is bounded by 6; and(3) the topology can be transformed into one with bi-directional links (without impairing the network connectivity) after removal of all uni-directional links. These results are corroborated in the simulation study.

No abstract

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR; however, the computation complexity is not increased in CBRT. 