Maximizing network lifetime is a major objective for designing and deploying a wireless sensor network. Clustering sensor nodes is an effective topology control approach helping achieve this goal. In this paper, we present a new method to prolong the network lifetime based on the improved particle swarm optimization algorithm, which is an optimization method designed to select target nodes. The protocol takes into account both energy efficiency and transmission distance, and relay nodes are used to alleviate the excessive power consumption of the cluster heads. The proposed protocol results in better distributed sensors and a well-balanced clustering system enhancing the network's lifetime. We compare the proposed protocol with comparative protocols by varying a number of parameters, e.g., the number of nodes, the network area size, and the position of the base station. Simulation results show that the proposed protocol performs well against other comparative protocols in various scenarios.

Recent significant research on wireless sensor networks (WSNs) has led to the widespread adoption of software-defined WSNs (SDWSNs), which can be reconfigured even after deployment. In this paper, we propose an energy-efficient routing algorithm for SDWSNs. In this algorithm, to make the network to be functional, control nodes are selected to assign different tasks dynamically. The selection of control nodes is formulated as an NP-hard problem, taking into consideration of the residual energy of the nodes and the transmission distance. To tackle the NP-hard problem, an efficient particle swarm optimization algorithm is proposed. Simulation results show that the proposed algorithm performs well over other comparative algorithms under various scenarios.

Underwater images typically suffer from low visibility and severe colorcast due to scattering and absorption. In this letter, a novel method is proposed to handle the scattering and absorption problems of light with different wavelengths based on the color-line model. We filter out image patches that exhibit the characteristics of the color-line prior, and recover the colorline of the patches. Then, the local transmission for each patch is estimated based on the offsets of the color lines along the background-light vector from the origin. We also develop an optimization function to derive the local transmission and to obtain the solution in the underwater environment. Experimental results are presented to show that the proposed method can produce high quality underwater images with relatively genuine colors, natural appearance, and improved contrast and visibility.

As a promising technology in the Internet of Underwater Things, underwater sensor networks have drawn a widespread attention from both academia and industry. However, designing a routing protocol for underwater sensor networks is a great challenge due to high energy consumption and large latency in the underwater environment. This paper proposes a Q-learning-based localization-free anypath routing (QLFR) protocol to prolong the lifetime as well as reduce the end-to-end delay for underwater sensor networks. Aiming at optimal routing policies, the Q-value is calculated by jointly considering the residual energy and depth information of sensor nodes throughout the routing process. More specifically, we define two reward functions (i.e., depth-related and energy-related rewards) for Q-learning with the objective of reducing latency and extending network lifetime. In addition, a new holding time mechanism for packet forwarding is designed according to the priority of forwarding candidate nodes. Furthermore, mathematical analyses are presented to analyze the performance and computational complexity of the proposed routing protocol. Extensive simulation results demonstrate the superiority performance of the proposed routing protocol in terms of the end-to-end delay and the network lifetime.

This paper proposes a bottom-up saliency detection method via effective integration of regional saliency measure and object-level information using fuzzy theory. First, we generate an initial saliency map by fusing multiple prior maps. Second, to emphasize the object-level concept of saliency, we further generate many object proposals of the input image. A fuzzy set theory is then adopted to measure the objectness score of the object proposals and integrate them into an objectness map. Third, an optimization framework is proposed to effectively fuse various prior saliency cues and object-level information to produce a clean and uniform saliency map as well as to maintain the salient object completeness. Experimental studies in several benchmark datasets confirmed the superiority of the proposed method over state-of-the-art saliency detection methods.

Unmanned surface vehicles (USVs) have demonstrated impressive practical value and potential in Marine Internet of Things (MIoT) system. Although trajectory-tracking control is among the most common practical technology of USVs, various limitations remain unaddressed. Existing studies have employed simple mathematical models to simulate marine environment without utilizing actual data, resulting in a lack of environmental authenticity. Moreover, a complex marine environment increases the need for robustness and adaptability of the control policy. To overcome these limitations, this study proposes a deep reinforcement learning (DRL)-based policy for USV trajectory-tracking control, which can effectively adapt to complex environmental disturbances. First, we use actual marine data, including ocean currents and winds, to construct a time-varying multi-element marine environment model. Next, an effective Markov decision processes (MDPs) formulation integrating LOS guidance law is elaborately proposed, in which the composite reward function and state transition function are used to avoid ineffective exploration and achieve better convergence ability. Furthermore, a USV trajectory-tracking controller based on hybrid priority twin-delayed deep deterministic policy gradient (TD3) agent is designed; specifically, a hybrid priority experience replay mechanism is developed and integrated within the TD3. It evaluates the significance of an experience by weighing the temporal-difference (TD) error and reward value, thus enabling the USV agent to explore optimal control policies and further accelerate the network convergence. Experimental results show that our method achieves better trajectory-tracking performance than mainstream DRL-based and model-based control approaches, and adapts to different reference trajectories and different intensities of environmental disturbances with high tracking accuracy.