In underwater acoustic sensor networks (UASNs), source nodes serving as data centers hold significant commercial value and strategic importance, and the leakage of their location information may result in immeasurable negative consequences. Presently, the methods employed to protect the location privacy of source nodes within UASNs face challenges such as limited network security duration, high node energy consumption, and prolonged data transmission delays. Additionally, security research has predominantly focused on passive attacks, with insufficient provisions against active threats. To address these issues, this study proposes a polygonal phantom source position privacy protection algorithm based on a secure zone (PPSZ) in autonomous underwater vehicle (AUV)-aided UASNs. First, a polygonal secure zone is defined with the source node at its center. Phantom nodes are strategically selected from nodes situated outside this zone, leveraging the relative angles between nodes to deter passive attacks while mitigating data transmission delays. Next, the selection of relay nodes is optimized using the Q-learning algorithm, where each node adjusts its selection strategy based on real-time feedback, further lowering node energy consumption. Finally, auxiliary nodes are deployed using a nonuniform clustering strategy to collectively transmit interference signals, effectively disrupt active attacks, and ensure the secure transmission of source data. Simulation results demonstrate that the PPSZ algorithm can better balance the relationships among safety time, node energy consumption, and data transmission delays.

Currently, the source location privacy (SLP) becomes a hot research interest in network security of Underwater Acoustic Sensor Networks (UASNs), and existing schemes are mostly proposed for a given scenario. Introducing source location privacy technologies inevitably increase the energy consumption of nodes, while they are widely deployed in available studies, resulting in massive energy wastage. Therefore, an adaptive scheme for protecting source location privacy (APSLP) in UASNs is proposed. The APSLP scheme first analyzes the possible locations of the adversary by trust method. Then, considering the lagging nature of the trust method, which means that the adversary may not stay in locations given by trust method, a hidden Markov-based backtracking method is proposed and location privacy methods are functioned according to the backtracking result. The simulation shows that even though the security level of the APSLP scheme is not the largest, the efficiency is the highest, approximately an increase of 69.1$\%$and 10.3$\%$compared with two comparison algorithms, respectively.

In the development of Internet of Underwater Things (IoUT), the unknown nature of the underwater environment is a challenging issue. In various domains related to IoUT, utilizing autonomous underwater vehicles (AUVs) for unmanned and autonomous missions has become an inevitable trend. Considering the particularity of underwater environments, this study proposes a hybrid-algorithm-based full coverage search approach to searching moving targets in unknown underwater environments. This approach combines the improved Voronoi clustering strategy, the improved artificial bee colony (ABC) algorithm, the improved line-of-sight (LOS) technique, and the artificial potential field (APF) method to enhance the efficiency of underwater full coverage search (FCS). First, the improved Voronoi clustering strategy is employed to partition the entire underwater region and allocate each part to an AUV. Second, to enhance the search capability of AUVs, a full-dimensional ABC algorithm with adaptive factor is designed to plan global paths for AUVs to search for targets, and the paths are further smoothed using the improved acrlong SLOS technique. During the navigation of the AUVs along the global paths, obstacles may be detected; thus, the APF method is utilized to dynamically plan local paths for AUVs to avoid obstacles. Experimental results demonstrate that the proposed approach significantly improves the efficiency of underwater FCS.

Based on the review of existing source location privacy protection technologies and research on underwater data transmission, numerous scholars have performed extensive work in the field of source location privacy protection. Further, current methods for protecting the source node location privacy in Internet of Underwater Things (IoUT), especially in underwater acoustic sensor networks (UASNs), suffer from several issues, including high data transmission energy consumption, short network lifespan, and inability to ensure data accuracy. Moreover, current common security research on UASNs considers only passive attacks and has fewer countermeasures for active attacks. To address these challenges, this article proposes a source location privacy protection algorithm based on polyhedral phantom routing in UASNs (PPR-USLP). First, the polyhedral phantom routing algorithm based on platonic solids is employed to introduce phantom nodes, which increases path diversity and protects the privacy of the source node, thereby thwarting passive attacks from adversaries and prolonging the life cycle of the network. Second, suitable relay nodes are selected by considering the peripheral status of the nodes, the data are collected by autonomous underwater vehicles (AUVs) to reduce the waiting time of sensor nodes and reduce the energy consumption of data transmission. Furthermore, to ensure data integrity and defend against active attacks from adversaries, this article combines error correction coding, which enhances network resilience and security while improving throughput and achieving load balancing. The simulation results demonstrate that PPR-USLP exhibits favorable performance in terms of energy consumption, safety time, and data accuracy, effectively safeguarding the privacy of the underwater source locations.

With the increase in the status of the ocean, the development of ocean resources is a priority, as is ocean security. Ocean security contains several aspects, such as equipment security, data security, military security, and so on. Location privacy protection of ocean equipment during data collection missions in unknown environments is investigated and a multiple autonomous underwater vehicles (AUVs) collaboration-assisted location privacy-preserving scheme is proposed in this thesis. In an unknown environment, several AUVs gather together to form a swarm to perform regional detection and defense missions. The swarm may face potential active and passive attacks, and the leader AUV is in the priority of protection. In this case, the follower AUVs deploy nodes in an unknown environment to make a rough sense. Then, the nodes build a Voronoi diagram based on their own perceptions to form a graph. Based on this graph, the AUV divides the nodes into regions by density-based spatial clustering of applications with noise (DBSCAN) and uses rapidly-exploring random trees (RRT) to plan the path in an unknown environment. Finally, the fake data along with the randomness of the moving follower AUV contribute to the location privacy-preserving during the regional detection and defense mission. Compared with the previous two schemes, the simulation results of the proposed method show some advantages in certain metrics.

In the process of data acquisition of underwater acoustic sensor networks (UASNs), the safety of the network is threatened by the disclosure of source node location information. So how to protect the security and privacy of source node location is the main challenge faced by UASN security. To realize this taeget, a hierarchical structure-based algorithm for protecting source location privacy (HSSLP) is proposed in this paper. Firstly, it is proposed to divide UASNs into dynamic and static layers based on Ekman drift model. Location privacy protection schemes suitable for source nodes located in different layers have been proposed separately. In the static layer, k-means clustering separates the nodes into groups, and the source node’s location privacy is protected using fake source node and phantom nodes, while auxiliary cluster head and sleep scheduling mechanism are used to save node energy. Nodes in the dynamic layer, whose positions are prone to change, are no longer clustered. The source node makes use of inducing nodes to take adversaries away from the source node, enhancing the privacy and security of the source node with minimal energy expenditure. Finally, autonomous underwater vehicles (AUV) need to support the cluster head in collecting data combined in the static layer and data uploaded in the dynamic layer. Based on the communication range of AUV, the network is segmented into areas, and when the AUV receives warning messages while traveling, it changes its route to lead the adversary to an area remote from the source node. Simulation results show that the proposed algorithm owns the capacity to balance the relationship between network security, transmission delay, and node energy consumption. To be more specific, the HSSLP algorithm improves the safety time by about 50$\%$, reduces the delay by about 20$\%$and saves the node energy by about 36$\%$as compared to the DIS-PLP algorithm.

Path planning is a basic requirement for Autonomous Underwater Vehicles (AUVs) to accomplish underwater missions. However, previous studies often have limitations, such as ignoring the basic condition that the AUV operates in an ocean current environment and discretizing its actions without considering the action space, which results in the simulation being far from the actual situation. To solve the above problems, this paper proposes a method of using a Parametrized Deep Q-Network (PDQN) to output hybrid actions for path planning, which can output a hybrid action space based on the current local observation, flexibly avoid obstacles under limited sensor observations, and realize the refined operation of AUV actions. According to the setup of the simulation environment, the AUV needs to visit multiple target nodes underwater and decelerate within the communication range of the nodes to have enough time to communicate with the nodes. The PDQN enables the AUV to easily learn the connection between the current state and discrete actions. It outputs the corresponding continuous actions based on the current discrete actions, which realizes a time-saving strategy of accelerating and then decelerating among the nodes. Meanwhile, we also utilize the actual current data and terrain data to restore the simulation environment as accurately as possible, and the simulation results prove the superiority and robustness of the algorithm.

Underwater acoustic sensor networks (UASNs) are data driven, and the data generation is infeasible without source nodes, sensors, and equipment deployed underwater. However, underwater acoustic communication between nodes is especially vulnerable to malicious attacks, which could cause the source data packets to be tracked and indirectly expose the locations of source nodes. Once the source node is located, the security of the network and the monitored object will be considered threatened. A collision-free transmission-based source location privacy protection algorithm in UASNs under time slot allocation (CFTSLP-TSA) is proposed in this article. First, we select suitable fake source nodes to generate fake data packets, aiming at concealing the traffic of the source data packets. Then, different transmission time slots are arranged for the source and the fake data packets, in order to avoid interference in the transmission between one another. In addition, a handshake-based relay node selection strategy is presented. This not only makes the paths more diverse but also requires a higher requirement for the attacker to track the flow of source packets while the source packets are transmitted without collisions. The performance of the simulation shows that the CFTSLP-TSA produces both a greater source location privacy protection level and a better data packet delivery rate compared with the other state-of-the-art schemes.

In the Internet of Underwater Things (IoUT), nodes are usually deployed randomly. Effective discovery of randomly deployed neighbor nodes is the basis for network topology self-configuration, data routing, transmission, etc. Especially in the IoUT with the directional transmission, how to efficiently discover neighbors is a major challenge to be solved at present. Hence, in this study, the neighbor discovery problem is investigated. The proposed algorithm consists of two parts: 1) a basic quorum system-based neighbor discovery (QSND) algorithm and 2) an adaptive reinforcement learning-based neighbor discovery (RLND) algorithm. First, a directional transceiver beam scanning sequence is designed adopting a C-torus quorum system to complete the initial neighbor discovery. Then, a reinforcement learning-based adaptive beam adjustment method is investigated to adjust the number of directional beams to be scanned based on neighbor recommendations and prior knowledge, thereby reducing the number of time slots expected to be required for neighbor discovery. Finally, simulation results demonstrate that QSND and RLND outperforms other related algorithms in terms of neighbor discovery rate, neighbor discovery delay, energy usage, etc.

The Internet of Underwater Things (IoUT) has attracted a lot of attention because of its promising applications in underwater environmental monitoring; however, the characteristics of acoustic communication, e.g., long propagation delay and high attenuation, pose great challenges for efficient and reliable underwater data transmission. Currently, opportunistic routing is regarded as a key technology to improve the packet delivery ratio, because it can dynamically choose several forwarding nodes and leverage their cooperative forwarding to increase network throughput. However, choosing an excessive number of forwarding nodes may result in energy waste and lengthy communication delays. Therefore, an opportunistic routing based on directional transmission (ORDT) is studied to improve packet delivery timeliness and reliability and lower energy consumption. ORDT mainly contains three phases: 1) forwarding area division; 2) candidate forwarder selection; and 3) candidate forwarder coordination. The forwarding region is first established based on directional transmission. Only neighbors located in the forwarding region can forward packets. Following that, candidate forwarders in the forwarding region are chosen depending on their forwarding capability. Additionally, the chosen candidate’s time of holding packets is specified so that they might collaborate to reduce redundant data transmission and transmit packets to gateway nodes. Then, an autonomous underwater vehicle (AUV) is employed to gather packets from gateway nodes. Simulation findings demonstrate that ORDT performs better than existing opportunistic routing in terms of packet delivery success rate, transmission latency, and energy usage.

Underwater acoustic sensor networks (UASNs) are effective instruments for monitoring marine environments and surveying seabed resources, it is important to improve their security protection, including source location privacy protection. Numerous strategies have been presented by researchers to strengthen location privacy, however, the majority of these plans have expensive energy costs. Therefore, a backbone-network-construction-based multiautonomous underwater vehicle (AUV) collaboration source location privacy protection (BNCSLP) algorithm has been enhanced to address this issue. First, the nodes in the network are split into various clusters, and an entire network is segmented into various regions. The backbone network is built using clusters that house the source. To prevent the adversary’s tracking, the AUV alternately chooses alternative backbone nodes and relays the source and fake data. Then, the cluster head determines whether to update the clusters by calculating how similar the data are with nearby clusters. The nearest neighbor technique is used by the updated cluster to anticipate the data and to reduce the energy utilization of forwarding the data packets, resulting in that there is less chance of data packets being intercepted and the source location being revealed. Finally, the AUV replans the trajectory, which shortens the AUV’s traveling path because only fewer cluster heads need to be accessed, decreasing the time it takes for data to be transmitted.

The position of the source is sensitive and critical information in underwater acoustic sensor networks (UASNs). In this study, a network coding-based scheme called the stratified source location privacy protection scheme (SSLP-NC) with autonomous underwater vehicle (AUV) is suggested for a strong adversary that can decode data. First, for the adversary with passive attacks, several fake source selection algorithms are suggested for two circumstances where the source is in the shallow and deep sea, respectively. Each node then utilizes a pseudo-random number generator to create sequences on a regular basis so that the key data can be delivered to the sink without interference. Then, for the adversary with the active attack, the node encrypts the source and fake data using the pre-existing pseudo-random number sequence as an encoding vector to thwart the adversary’s decryption. Further, this work develops a relay node selection approach for transmitting the encoded data, which increases the variety of the data transmission pathways. Finally, this study includes a hole avoidance strategy that uses nodes or an AUV to address the potential hole issue. The simulation demonstrates that the SSLP-NC successfully fends off an adversary that can decode data packets, and performs better than the EECOR and DBR-MAC algorithms in terms of network safe time and packet delivery rate.

As the research topics in ocean emerge, underwater acoustic sensor networks (UASNs) have become ever more relevant. Consequently, challenges arise with the security and privacy of the UASNs. Compared to the active attacks, the characteristics of passive attacks are more difficult to discriminate. Thus, the focus of this study is on the passive attacks in UASNs, where a push-based probabilistic method for source location privacy protection (PP-SLPP) is proposed. The fake packet technology and the multipath technology are utilized in the PP-SLPP scheme to counter the passive attacks, so as to protect the source location privacy in UASNs. Moreover, the Ekman drift current model is employed to simulate the underwater environment. And the mean shift algorithm and the k-means algorithm are adopted in the dynamic layer and static layer of the Ekman drift current model, respectively, to increase the stability of the clusters. Finally, an autonomous underwater vehicle (AUV) swarm is implemented to collect data in clusters. Through the comparison with existing data collection schemes in UASNs, the simulation results have demonstrated that the PP-SLPP scheme can achieve a longer safety period, with a minor compromise of energy consumption and delay.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.

The Internet of Things (IoT) is a network of everything. In IoT, the charging problem of devices is an issue that needs urgent attention. Most previous studies about wireless charging algorithms are based on the ideal conditions, which may be not suitable for the actual scene. Therefore, focusing on that nodes are evenly distributed, which is a type of ideal conditions, this article proposes a mobile charging algorithm based on multicharger cooperation (MCCA) for IoT with the random deployment of nodes. The MCCA, which is based on a new uneven cluster method, mainly includes three parts. First, based on the relationship between the number of charging requests and the number of chargers, the base station chooses an appropriate charging request threshold and the number of chargers. Then, multiple chargers perform the collaborative charging scheduling based on the energy requirement of each cluster and the distance between clusters. Finally, the base station adjusts the charging sequence of each charger if there exists a conflict. In the end, the MCCA is verified by MATLAB and compared with various algorithms. The simulation results show that the MCCA can effectively balance the energy consumption, reduce the number of nonfunctional nodes, improve the charging efficiency, and extend the network lifetime.

The Industrial Internet of Things (IIoT) involves the use of large numbers of sensing nodes, which should meet the requirements for industrial use, such as real-time performance monitoring and high reliability and stability. However, owing to single-station allocation, inappropriate mobile charger (MC) allocation and unreasonable route planning, conventional methods may lead to local blockages, incomplete charging coverage, and high energy consumption because of additional movement. Hence, we propose a multistation-based collaborative charging strategy, termed MCCS, to overcome these problems. In MCCS, the energy sources are static charging stations. Furthermore, MCs that consist of primary and senior chargers act as the transmission media. Specifically, the senior chargers, which are charged by the stations, transmit energy to the primary chargers, which then transmit energy to the sensor nodes. The following steps are involved in MCCS. To begin with, MCCS divides the sensor nodes into various categories based on a self-organizing feature mapping neural network in order to ensure appropriate primary charger allocation. Next, a genetic algorithm is used to generate the optimal routes for the MCs. Finally, MCCS allocates the senior chargers and sets up the charging stations. Simulations were conducted to evaluate MCCS, which exhibited better performance in terms of efficiency, energy consumed in movement, and charging energy loss as compared with existing strategies.

Among several new technologies, such as social and cognitive mobile computing, wireless sensor networks (WSNs) constitute the founding pillar of the industrial Internet of Things. These networks are expected to play an increasingly important role in our daily lives. Social and cognitive mobile computing requires the sharing of data recorded by sensor nodes. However, the data can be vulnerable to attacks. It is of utmost importance to protect the users privacy while ensuring the security of the WSNs. This investigation is focused on the source-location privacy (SLP) of WSNs. This article proposes a dynamic multipath privacy-preserving routing (DMPPR) scheme based on multiple sinks for protecting the privacy. Different from single sink schemes, the technique of using multiple sink nodes to protect SLP is discussed in this article. Furthermore, a packet-slicing transmission scheme that generates a large number of dynamic routings based on multiple sink nodes is adopted for transmitting the packets. Local adversaries are considered, and to cope with these adversaries, a transmission loop, constructed using real and fake packets, is proposed to confuse the adversaries during the source detection process. The aim is to break the sociality between the sensor nodes. Simulations performed in MATLAB show that the proposed method outperforms similar existing schemes in terms of the secure time, adversary's capture probability, and node utilization ratio. Moreover, the DMPPR scheme also reduces energy consumption by allowing more nodes in the nonhotspot areas to participate in the packet transmission process.

The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.

Wireless sensor networks (WSNs) are an important core of the Industrial Internet of Things (IIoT). Wireless rechargeable sensor networks (WRSNs) are sensor networks that are charged by mobile chargers (MCs), and can achieve self-sufficiency. Therefore, the development of WRSNs has begun to attract widespread attention in recent years. Most of the existing energy replenishment algorithms for MCs use one or more MCs to serve the whole network in WRSNs. However, a single MC is not suitable for large-scale network environments, and multiple MCs make the network cost too high. Thus, this paper proposes a collaborative charging algorithm based on network density clustering (CCA-NDC) in WRSNs. This algorithm uses the mean-shift algorithm based on density to cluster, and then the mother wireless charger vehicle (MWCV) carries multiple sub wireless charger vehicles (SWCVs) to charge the nodes in each cluster by using a gradient descent optimization algorithm. The experimental results confirm that the proposed algorithm can effectively replenish the energy of the network and make the network more stable.

An exciting network called smart IoT has great potential to improve the level of our daily activities and the communication. Source location privacy is one of the critical problems in the wireless sensor network (WSN). Privacy protections, especially source location protection, prevent sensor nodes from revealing valuable information about targets. In this paper, we first discuss about the current security architecture and attack modes. Then we propose a scheme based on cloud for protecting source location, which is named CPSLP. This proposed CPSLP scheme transforms the location of the hotspot to cause an obvious traffic inconsistency. We adopt multiple sinks to change the destination of packet randomly in each transmission. The intermediate node makes routing path more varied. The simulation results demonstrate that our scheme can confuse the detection of adversary and reduce the capture probability.