Underwater acoustic sensor networks (UASNs) are among the most critical tools for underwater environment sensing and information acquisition. Since the complexity of the underwater environment renders battery replenishment and replacement more difficult and UASNs are mainly powered by batteries built into the underwater acoustic sensor nodes, the optimization of node energy consumption is particularly important for extending the network lifetime. Recently, energy consumption optimization solutions for UASNs have mainly focused on node clustering and multihop transmission. However, most schemes are implemented using distributed algorithms; in these types of approaches, decision information usually comes from only a subset of nodes, and it is difficult to make the most globally beneficial decision. Therefore, in this paper, the idea of centralized control is applied for the optimization of network energy consumption from a global perspective. Specifically, a centralized control-based clustering scheme (CCCS) for UASNs is proposed. In this scheme, a node density-based adaptive clustering approach is adopted, intracluster controllers are established within clusters, and the choices of both relay nodes and relay clusters are optimized to achieve energy balancing and route optimization. The performance achieved in simulations shows that compared to similar methods, CCCS is able to balance the node energy consumption and effectively increase the lifetime of a UASN.

Underwater acoustic sensor networks (UASNs) are used extensively in activities such as underwater data collection and water pollution detection. A UASN consists of acoustic sensors that use batteries as their power supply. Because of the complex underwater environments in which UASNs are employed, replacing these batteries is difficult. Prolonging the battery life of UASNs by reducing their energy consumption (improving their energy efficiency) is one means of mitigating this problem. This paper proposes an energy-balanced unequal layering clustering (EULC) algorithm that improves the energy efficiency of acoustic sensors. The EULC algorithm designs UASNs with unequal layering based on node depth, providing a solution to the “hot spot” issue through the construction of clusters of varying sizes within the same layer. Simulation results show that the EULC algorithm effectively balances the energy in UASN nodes and thereby prolongs network lifetime.

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime.

Named data networking (NDN) is one representation and implementation of information-centric networking (ICN) and is considered to be among the most promising designs for the next generation of network architecture. The introduction of NDN into vehicular ad-hoc networks (VANETs) and utilization of its content-centric characteristic to improve data transmission and distribution in VANETs has become a research hotspot in recent years. However, research on mobility support of NDN-based VANETs still faces many challenges. To solve the issue of data transmission path breaking due to Consumer mobility in NDN-based VANETs, this article proposes a novel vehicle tracking-based Data packet forwarding scheme (VTDF) to improve the successful delivery rate of Data packets in mobile environments. In this approach, the urban road structure is divided into complex multi-junction and straight lane scenarios and Data packets are forwarded according to vehicle movement information. Simulations indicated that this vehicle tracking scheme provides a lower average data transmission delay, shorter handover delay between roadside units, and higher data delivery rate for Consumers compared to the standard methods.

Underwater acoustic sensor networks (UASNs) currently provide an important technical means of underwater communication, but there are difficulties in power updating or power replenishment because the sensor nodes work in an underwater environment. Therefore, energy consumption optimization has become the focus of research on UASNs. Node clustering is widely considered to be able to optimize network energy consumption. Although the current clustering-based routing method prolongs the network life cycle to a certain extent, some nodes may fail due to excessive energy consumption caused by excessive data transmission tasks, and the problems of high and uneven energy consumption still exist. To extend the life cycle of UASNs, this article proposes an unequal clustering method based on particle swarm optimization. Our method uses iterative updates to dynamically adjust the cluster size based on the remaining energy of the cluster head, the distance from the cluster head to the Sink node, and the number of times the cluster head forwards data between clusters to balance the cluster head load. The energy consumption of the whole path from the cluster head to the Sink node and the number of hops required are considered in the intercluster transmission phase. Simulation results show that this method can effectively reduce the network energy consumption.

In recent years, a typical representation of the next-generation Internet architecture, named data networking (NDN), and a critical form of underwater Internet of Things (IoT), underwater acoustic sensor networks (UASNs), have attracted widespread attention in academia. Meanwhile, since the battery energy of the sensor node is limited, and the battery is difficult to replace or recharge in underwater environments, extending the networks’ lifetime has become a key issue in UASNs. In this study, we try to deploy a UASN on NDN architecture, and explore the energy consumption of the NDN-based UASN under shallow water and deep water conditions based on the relay network topology. A simulation is carried out to compare the delay performance of NDN-based and IP-based UASNs, and validate the result. It is believed that the study could provide a theoretical criterion for the selection of the direct or relay path to optimize energy consumption in the future deployment of NDN-based UASNs.