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Coverage maintenance is a bottleneck restricting the development of underwater acoustic sensor networks (UASNs). Since the energy of the nodes is limited, the coverage of UASNs may gradually decrease as the network operates. Thus, energy-saving coverage control is crucial for UASNs. To solve the above problems, this paper proposes a coverage-control strategy (referred to as ESACC) that establishes a sleep–wake scheduling mechanism based on the redundancy of deployment nodes. The strategy has two main parts: (1) Node sleep scheduling based on a memetic algorithm. To ensure network monitoring performance, only some nodes are scheduled to work, with redundant nodes in a low-power hibernation state, reducing energy consumption and prolonging the network lifetime. The goal of node scheduling is to find a minimum set of nodes that can cover the monitoring area, and a memetic algorithm can solve this problem. (2) Wake-up scheme. During network operation, sleeping nodes are woken to cover the dead nodes and maintain high coverage. This scheme not only reduces the network energy consumption but takes into account the monitoring coverage of the network. The experimental data show that ESACC performs better than current algorithms, and can improve the network life cycle while ensuring high coverage.

In current years, there is an sudden increase of wireless data Traffic which has resulted in a huge scale dense deployment of small cells, with which the increasing cost of energy has concerned a set of research interest. I present the wireless network which consists of distributed sensor nodes. The function of distributed sensor nodes is to manage the physical condition and to generate the traffic system. Each and every nodes in WSN will sends and receives the packet ensuing in depletion of bandwidth. However, bandwidth sensor nodes are not an productive by saving the energy. Thus, sleep/wake-up scheduling is one of the vital problems in the wireless sensor network. Because, they are fixed and it cannot be recharge again. An energy efficient sleep/wake-up scheduling approach is proposed. The aim of this approach is to save the energy of each nodes and to increase their lifetimes.

Energy-efficient coverage remains a critical challenge in wireless sensor networks (WSNs), particularly under probabilistic sensing models and resource-constrained environments. To address this, we propose a novel sleep scheduling algorithm that integrates node utility with a priority strategy for critical coverage targets. Our approach begins by constructing a hierarchical disjoint cover set (H-DCS) to reduce computational complexity and decouple global coverage constraints. We then introduce a utility-prioritized key target optimization (UPKO) framework, which dynamically balances node residual energy against coverage contribution, while ensuring that targets with minimal predicted lifetime are prioritized. The integrated algorithm, termed UCTF-SS, selectively activates a subset of nodes to maintain full coverage while maximizing network lifetime. Extensive simulations across multiple network scales and parameter settings demonstrate that UCTF-SS significantly outperforms existing methods, including MUA-WPT and GA-based scheduling, in terms of energy consumption, coverage sustainability, and network longevity. The proposed method also exhibits strong scalability and adaptability to large-scale deployments, offering a practical and efficient solution for energy-aware WSN operations.

Compressive data gathering (CDG) is an adequate method to reduce the amount of data transmission, thereby decreasing energy expenditure for wireless sensor networks (WSNs). Sleep scheduling integrated with CDG can further promote energy efficiency. Most of existing sleep scheduling methods for CDG were formulated as centralized optimization problems which introduced many extra control message exchanges. Meanwhile, a few distributed methods usually adopted stochastic decision which could not adapt to variance in residual energy of nodes. A part of nodes were prone to prematurely run out of energy. In this paper, a reinforcement learning-based sleep scheduling algorithm for CDG (RLSSA-CDG) is proposed. Active nodes selection is modeled as a finite Markov decision process. The mode-free Q learning algorithm is used to search optimal decision strategies. Residual energy of nodes and sampling uniformity are considered into the reward function of the Q learning algorithm for load balance of energy consumption and accurate data reconstruction. It is a distributed algorithm that avoids large amounts of control message exchanges. Each node takes part in one step of the decision process. Thus, computation overhead for sensor nodes is affordable. Simulation experiments are carried out on the MATLAB platform to validate the effectiveness of the proposed RLSSA-CDG against the distributed random sleep scheduling algorithm for CDG (DSSA-CDG) and the original sparse-CDG algorithm without sleep scheduling. The simulation results indicate that the proposed RLSSA-CDG outperforms the two contrast algorithms in terms of energy consumption, network lifetime, and data recovery accuracy. The proposed RLSSA-CDG reduces energy consumption by 4.64% and 42.42%, respectively, compared to the DSSA-CDG and the original sparse-CDG, prolongs life span by 57.3%, and promotes data recovery accuracy by 84.7% compared to the DSSA-CDG.

In wireless sensor networks, the high density of node’s distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead.

Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions.

Energy plays a major role in wireless sensor networks (WSNs), and measurements demonstrate that transmission consumes more energy than processing. Hence, organizing the transmission process and managing energy usage throughout the network are the main goals for maximizing the network’s lifetime. This paper proposes an algorithm called RLDCSSA-CDG, which is processed through the 3F phases: foundation, formation, and forwarding phases. Firstly, the network architecture is founded, and the cluster heads (CHs) are determined in the foundation phase. Secondly, sensor nodes are dynamically gathered into clusters for better communication in the formation phase. Finally, the transmitting process will be adequately organized based on an adaptive wake-up/sleep scheduling algorithm to transmit the data at the “right time” in the forwarding phase. The MATLAB platform was utilized to conduct simulation studies to validate the proposed RLDCSSA-CDG’s effectiveness. Compared to a very recent work called RLSSA and RLDCA for CDG, the proposed RLDCSSA-CDG reduces total data transmissions by 22.7% and 63.3% and energy consumption by 8.93% and 38.8%, respectively. It also achieves the lowest latency compared to the two contrastive algorithms. Furthermore, the proposed algorithm increases the whole network lifetime by 77.3% and promotes data recovery accuracy by 91.1% relative to the compared algorithms.

One of the fundamental problems in randomly deployed sensor networks is enhancing the network lifetime while providing full area coverage. The problem is more challenging when location information is not available. Scheduling the activities of sensor nodes in a way that each point of the area of interest is covered by at least one sensor node is a promising way when a smaller set of sensor nodes is scheduled autonomously. The autonomous sleep scheduling of sensor nodes can be efficiently achieved based on the topological properties of the sensor network in a distributed fashion. In this paper, we address the problem of autonomously scheduling of sensor nodes to provide full area coverage in wireless sensor networks, even when location information is unavailable. The goal is to prolong the network lifetime. The proposed method is based on homology. The idea is autonomous selection of the minimum number of active sensors with the highest level of energy based on the properties of the simplicial complex of the network. We formulate this problem as an integer programming problem. Then, we propose a distributed algorithm, which does not require the knowledge of the location of nodes or distance between them. Finally, we provide simulation results demonstrating the performance of the proposed algorithm.

Mobile Wireless Sensors Networks (MWSNs) are used in several applications presenting difficult/dangerous environment and/or requiring the movement of sensors after initial deployment. Optimizing the use of the limited energy resource in a MWSN is a key challenge for researchers to maintain longer network survival. This paper attempts to provide an energy-efficient data routing solution for large MWSNs. The aim of this work is to propose a cluster-based scheduling protocol for MWSN.  The network is firstly divided into an optimal number of clusters according to sensors connectivity. Secondly, a sleep scheduling algorithm is proposed to save the energy consumption by turning off the overlapped nodes in the sensing field. This method is distributed among sensor nodes in each cluster. It is based on the perimeter coverage level of mobile sensor nodes to schedule their activities according to their weights. The weight is used to balance the energy consumption for all sensor nodes in a cluster. The proposed approach ranges from sensors deployment, their organization to their operational mode. Experimental results demonstrate that the proposed cluster-based scheduling algorithm, based on the perimeter coverage of sensors, provides higher energy efficiency and longer lifetime coverage for MWSNs as compared to other protocols.