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.

In the vehicular ad hoc networks (VANETs), due to the high mobility of vehicles, the network parameters change frequently and the information that the sender maintains may outdate when it wants to transmit data packet to the receiver, so for improving the routing efficiency and reliability, we propose the probability prediction-based reliable and efficient opportunistic routing (PRO) algorithm for VANETs. The PRO routing algorithm can predict the variation of signal-to-interference-plus-noise ratio (SINR) and packet queue length (PQL) of the receiver. The prediction results are used to determine the utility of each relaying vehicle in the candidate set. The calculation of the vehicle's utility is the weight-based algorithm, and the weights are the variances of SINR and PQL. The relaying priority of each relaying vehicle is determined by the value of its 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 greedy perimeter stateless routing algorithms.

The computation task offloading and resource management in mobile edge computing (MEC) ABSTRACTbecome attractive in recent years. Many algorithms have been proposed to improve the performance of the MEC system. However, the research on power control in MEC systems is just starting. The power control in the single-user and interference-free multi-user MEC systems has been investigated; but in the interference-aware multi-user MEC systems, this issue has not been learned in detail. Therefore, a game theory based power control approach for the interference-aware multi-user MEC system is proposed in this paper. In this algorithm, both the interference and the multi-user scenario are taken into account. Moreover, the existence and uniqueness of the Nash Equilibrium (NE) of this game are proved, and the performance of this algorithm is evaluated via theoretical analysis and numerical simulation. The convergence, the computation complexity and the price of anarchy in terms of the system-wide computation overhead are investigated in detail. The performance of this algorithm has been compared with the traditional localized optimal algorithm by simulation. The simulation results demonstrate that the proposed algorithm has more advantages than the traditional one.

For improving the efficiency and the reliability of the opportunistic routing algorithm, we propose a cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, in which the fuzzy logic and topology control are introduced into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variances (rv) of the metrics rather than the metrics’ values; by this, the number of fuzzy rules is reduced dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For improving routing reliability and reducing the control cost, in CBRT, the relay node degree is a range rather than a constant number. The nodes are divided into different categories based on their relay node degree. The nodes adjust their transmission power based on which categories that they belong to. Additionally, for investigating the effect of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the candidate relay nodes based on their utilities. The relay node whose utility is large has high priority to forward the data packet to next hop. By these innovations, the performance of CBRT is much better than that of ExOR; however, the computation complexity does not increase in CBRT.

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.