Sensors In Wireless Sensor Networks Research Articles

An Analysis of Hierarchical Routing Strategy with Advanced Additional Sensors in WSNs

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

An Improved Coverage Hole Finding System for Critical Applications Based on Computational Geometric Techniques

Wireless Sensor Networks (WSNs) contain coverage holes caused by both random node sensor deployment and malfunctioning nodes. Because fixing the battery is challenging, collaborative discovery and assessment of coverage shortfalls, as well as getting rid of these holes, has been recognized as critical in WSNs. While placing nodes for sensors in a large-scale WSN is challenging. This research provides a cost-effective coverage hole detection approach based on collaborative distributed point placement. Create a polygon first by employing an angle estimate approach and neighbor data. Following that, a based on points hole identification technique is used to assess if a coverage issue appears in a large-scale WSN's supplied ROI. Furthermore, the region of the coverage hole is estimated using computational geometry-based polygonal triangulation methods. The accuracy of the method is tested here using statistical data. The results show that it outperforms earlier coverage hole-detecting algorithms. In particular, the method improves coverage rate by 75% when compared to conventional methodologies. It also lowers energy usage by 90%, adding to increased network lifetime. The quantitative favourable results demonstrate the effectiveness of the collaborative distributed point placement technique in detecting and successfully resolving coverage gaps in WSNs. In regards to coverage rate, energy consumption, and network longevity, the system being proposed beats previous coverage hole-detecting techniques.

E2DNE: Energy Efficient Dynamic Network Embedding in Virtualized Wireless Sensor Networks

Efficient utilization of resources is an important challenge in traditional non-virtualized Wireless Sensor Networks (WSNs), as the applications are embedded in the sensors, precluding the sensor nodes from being re-used by other applications. Inefficient utilization of resources results in high deployment and maintenance costs. Virtualization is a promising technology that allows multiple sensing tasks from diverse applications to be executed on the same deployed WSNs concurrently. However, given that the WSN sensors are constrained with limited energy, the allocations of physical and virtual resources to the applications in an efficient manner becomes a challenge, especially for mission-critical, delay-sensitive applications. In this paper, we address the problem of virtual network embedding in virtualized WSNs, aiming at minimizing the overall energy consumption while considering the end-to-end latency and bandwidth consumption as the service level agreement (SLA) constraints. We propose our Dynamic Network Embedding (DNE) heuristic for large-scale problem instants. The results reveal that our proposed heuristic leads to close-to-optimal solutions with satisfactory execution time. Our results indicate that the proposed heuristic achieves up to an 18% optimality gap in energy consumption in the small-scale scenario, and outperforms the existing benchmark by up to 58% in the large-scale scenario.

Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

In order to improve the quality of service and prolong the network lifetime of wireless sensor networks (WSNs), optimizing the network coverage rate and the sensor nodes moving distance in the secondary deployment process has become a crucial research object. Aiming at these two goals, this paper improves the classic multi-objective ant lion optimization algorithm, and proposes an improved multi-objective ant lion optimization algorithm based on fast non-dominated sorting (NSIMOALO). Firstly, we use the idea of fast non-dominated sorting algorithm and elite strategy of NSGA-II, which avoids the algorithm from falling into the local optimal solution and improves the solution accuracy of the algorithm. Secondly, a more reasonable congestion calculation equation is proposed, which greatly increases the diversity of the population. Finally, we introduce Lévy flight to update the ant position by dynamically changing the weight coefficients among the antlion, elite antlion and Lévy flight, which improves the global optimization ability of the algorithm. The standard function simulation results show that NSIMOALO algorithm has higher convergence and coverage. The algorithm is applied to WSNs sensor nodes deployment, and 80 sensor nodes are deployed in a monitoring area of 200m×300m. Compared with MOALO algorithm, NSGA-II and NSMOFPA, the coverage rate of NSIMOALO algorithm is increased by 12.753%, 12.413% and 4.492% respectively, and the sensor nodes average moving distance is decreased by 2.551m, 2.316m and 4.457m respectively.

An Intelligent Energy-Efficient Clustering Technique to Maximize Wireless Sensor Network Lifetime

A wireless sensor network (WSN) is an environment monitoring network that collects and transmits data wirelessly to the base station. Due to their inability to be recharged or replaced, sensors face battery constraints. Considering how much energy is wasted by sensors in WSN, this is one of the most popular research topics in WSN. Protocols for improving energy efficiency have been developed to improve the network's energy dissipation and, ultimately, its lifetime. An energy-efficient and throughputenhancing clustering technique is presented in this paper, which is superior to existing protocols based on LEACH. A cluster head in LEACH elected from a cluster that includes nodes with small residual energy will lead to an early death of the network, which will adversely affect its efficiency. The proposed clustering technique, on the other hand, uses the remaining energy of the sensor to make the sensor a cluster head. The base station finds the shortest path between the cluster heads. Through this compound, power dissipation is reduced, which contributes to a longer network lifespan and higher throughput. According to simulations performed with the NS3 simulator, the proposed clustering technique achieves higher network lifetime and throughput compared to some recent clustering protocols, i.e. LEACH, BCE-LEACH, and MO-LEACH.

Adaptive Route Sink Relocation Using Cluster Head Chain Cycling Model in WSN

Wireless Sensor Networks (WSN) have revolutionized the processes involved in industrial communication. However, the most important challenge faced by WSN sensors is the presence of limited energy. Multiple research investigations have been conducted so far on how to prolong the energy in WSN. This phenomenon is a result of inability of the network to have battery powered-sensor terminal. Energy-efficient routing on packet flow is a parallel phenomenon to delay nature, whereas the primary energy gets wasted as a result of WSN holes. Energy holes are present in the vicinity of sink and it is an important efficient-routing protocol for WSNs. In order to solve the issues discussed above, an energy-efficient routing protocol is proposed in this study named as Adaptive Route Decision Sink Relocation Protocol using Cluster Head Chain Cycling approach (ARDSR-CHC2H). The proposed method aims at improved communication at sink-inviting routes. At this point, Cluster Head Node (CHN) is selected, since it consumes low energy and permits one node to communicate with others in two groups. The main purpose of the proposed model is to reduce energy consumption and define new interchange technology. A comparison of simulation results demonstrates that the proposed algorithm achieved low cluster creation time, better network error and high Packet Delivery Rate with less network failure.

Optimal Coverage in Wireless Sensor Network using Augmented Nature-Inspired Algorithm

One of the difficult problems that must be carefully considered before any network configuration is getting the best possible network coverage. The amount of redundant information that is sensed is decreased due to optimal network coverage, which also reduces the restricted energy consumption of battery-powered sensors. WSN sensors can sense, receive, and send data concurrently. Along with the energy limitation, accurate sensors and non-redundant data are a crucial challenge for WSNs. To maximize the ideal coverage and reduce the waste of the constrained sensor battery lifespan, all these actions must be accomplished. Augmented Nature-inspired algorithm is showing promise as a solution to the crucial problems in “Wireless Sensor Networks” (WSNs), particularly those related to the reduced sensor lifetime. For “Wireless Sensor Networks” (WSNs) to provide the best coverage, we focus on algorithms that are inspired by Augmented Nature in this research. In wireless sensor networks, the cluster head is chosen using the Diversity-Driven Multi-Parent Evolutionary Algorithm. For Data encryption Improved Identity Based Encryption (IIBE) is used. For centralized optimization and reducing coverage gaps in WSNs Time variant Particle Swarm Optimization (PSO) is used. The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the reduced sensor lifetime and redundant information in Wireless Sensor Networks (WSNs) as well as will give real and effective optimum coverage to the Wireless Sensor Networks (WSNs).

Energy efficient chaotic sparrow search algorithm based clustering protocol for wireless sensor network communications

Wireless sensor networks (WSN) include a collection of autonomous sensor nodes, which are generally deployed in a harsh or hostile environment to sense the physical parameters involved in the target region. It is highly utilized for target tracking, surveillance, and data gathering applications. Since the sensors in WSN operates on limited inbuilt batteries, it is needed to design energy efficient approaches to preserve energy and extend lifetime of the network. Earlier studies have reported that clustering is an effectual manner used for accomplishing energy efficiency and it is addressed with use of metaheuristic optimization algorithms. In this view, this paper present an energy efficient chaotic sparrow search algorithm based clustering (EE-CSSAC) technique for WSN. The proposed EE-CCSAC technique is dependent upon the integration of chaotic maps into the traditional sparrow search algorithm (SSA). In addition, the EE-CCSAC technique develops a fitness function with four variables to properly select the CHs and then organize clusters, thereby achieving energy efficiency. The experimental validation of the EE-CCSAC system takes place and the outcomes are related to recent clustering approaches. The simulation outcomes exhbited the effective outcome of the EE-CCSAC method on the other ones with respec to distinct estimation parameters.

Elite Oppositional Farmland Fertility Optimization Based Node Localization Technique for Wireless Networks

Wireless networks include a set of nodes which are connected to one another via wireless links for communication purposes. Wireless sensor networks (WSN) are a type of wireless network, which utilizes sensor nodes to collect and communicate data. Node localization is a challenging problem in WSN which intends to determine the geographical coordinates of the sensors in WSN. It can be considered an optimization problem and can be addressed via metaheuristic algorithms. This study introduces an elite oppositional farmland fertility optimization-based node localization method for radio communication networks, called EOFFO-NLWN technique. It is the goal of the proposed EOFFO-NLWN technique to locate unknown nodes in the network by using anchor nodes as a starting point. As a result of merging the principles of elite oppositional-based learning (EOBL) and the agricultural fertility optimization algorithm (FFO), we have developed the EOFFO-NLWN approach, which is described in detail below. The EOBL concept makes it easier to populate the FFO algorithm’s population initialization, which results in an increase in the exploration rate. Various BNs and CRs were tested, and the findings revealed that the EOFFO-NLWN technique outperformed all other known techniques in all cases. A comprehensive experimental result analysis of the EOFFO-NLWN technique is performed under several measures, and the results described the sovereignty of the EOFFO-NLWN method associated to existing techniques.

Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.

A Quasistraight Line Routing Protocol for Square Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network (WSN) are both energy-constrained and vulnerable to faults and disasters. Communication between the sensor nodes is generally hop-by-hop, and the nodes are distributed throughout the area to be covered. Broadcast-based routing protocols are not preferable in sensor networks since broadcasting is considered costly in terms of battery power consumption. In this paper, a digital quasistraight line segment- (DQSS-) based approach is employed for the detection of quasistraight line segments, i.e., for quasistraight path finding between WSN sensors arranged in a square grid. Comparative results show that the method is comparable with the best-known straight line finding algorithm in terms of path lengths and computation time. Moreover, the proposed method is capable of avoiding dead nodes by updating DQSS parameters dynamically during path finding. Hence, the proposed method is promising to be used in WSN square grids as a quasistraight line routing protocol.

Utilizing Genetic Algorithm and Artificial Bee Colony Algorithm to Extend the WSN Lifetime

Extending the lifetime of Wireless Sensor Networks (WSN) is an important issue due to the mission assigned to these networks. The sensors collect data relevant to a specific field. Then, the sensors send the collected data to a base station where it is analyzed, and a suitable reaction can be taken. Sensors in WSN depend on a battery with limited energy to do their work. Data transmission and receiving consume energy, which may lead to the loss of the whole network or some of the essential nodes. For this reason, energy must be preserved as long as possible to prolong the network lifetime. Several types of research were presented with different approaches to minimize power consumption. In this paper, we present a hybrid technique that includes two population-based algorithms: genetic algorithm (GA) and artificial bee colony (ABC) with clustering approaches. This proposed novel technique aims to reduce the dissipation of power consumption per sensor node in the WSN, and as a consequence, the lifetime of the WSN is extended. The ABC algorithm was used to improve an initial population, which was used in the GA. Also, we used two approaches of clustering; clustering based on genetic algorithm and K-means clustering beside LEACH protocol. The experimental results show that the proposed approach approved its efficiency in lifetime extending through an increasing number of the operational nodes per round and transmission.

A Novel Fault Diagnosis Technique for Wireless Sensor Network Using Feedforward Neural Network

In recent times, research on fault diagnosis for wireless sensor networks (WSNs) has been a challenging task. The sensors (SNs) in WSN are deployed in the environment through volcanoes, forests, highways, etc., for extended periods; hence, they are subjected to frequent and unexpected faults. Existing fault diagnosis techniques such as distributed, hybrid, and centralized require transmitting the data sensed by an SN to their neighboring SNs, cluster head, or base station to identify their fault status. Hence, these techniques are energy-inefficient and suffer from high communication overhead and delay, which decreases the overall lifetime of the network. Also, these existing techniques depend on the spatial–temporal correlation between the nodes to detect the faulty SNs in the network. Therefore, to address these issues, we propose a novel fault diagnosis technique where each SN detects its fault status using its own sensed data. The performance of the proposed approach has been compared with the existing fault diagnosis techniques to demonstrate its effectiveness. The experimental results show the efficiency of the proposed fault diagnosis technique over existing techniques in terms of the various performance metrics.

Directional antenna with reproduction optimisation (BFOA) used in mobile ad-hoc network

A re-configurable directional antenna with optimisation carries a proficient solution for decreasing the collision in wireless sensor networks (WSNs). Enhancement has various scenarios: an increased antenna gain which comes under certain circumstances expands the information correspondence extends to optimise the portable hubs requirement. The principal benefits of this work applying RDA with BFOA optimising algorithm for mobile ad-hoc network (MANET) or WSNs are respected meager. Based on received cycle (RDA-RICHER) medium access control rules for WSN sensor hubs devices, various methods are studying there-configurable directional antenna. This situation of the utilisation of the energy is just material to the constrained hub trusts with it. This confinement of the single expectation and energy effectiveness is the issue to be attempted in this work utilising bacteria foraging optimisation and assessing the optimised energy consumption, streamline idleness, received signal quality, and collision rates in percentage.

IPAWL: An integrated power aware Wireless sensor network and Location-Based social network for incidence reporting

One of the constraints in Wireless Sensor Network (WSN) is the limited power of distributed interconnected sensor nodes over the space. There are several proposed architectures for handling power consumption in a WSN. Traditional clustering and routing methods improve the Cluster Head (CH) selection of WSN internodes for balancing energy in the network. In such clustering methods, every message is transmitted from a source node to its CH, from the CH to other CHs, and finally to the BS (Base Station). Therefore, a high amount of energy should be consumed for routing when the source nodes are far from CHs or the BS. This paper proposes an integrated LBSN (Location-Based Social Network) and WSN routing and CH selection method for reducing routes from source nodes to BSs to reduce the energy consumption of WSN. In the integrated network, LBSN nodes can be added to the WSN as BS or WSN sensor nodes. In the first situation, the integrated network is called IPAWL I and the latter builds the IPAWL II network.To evaluate the method, we considered a scenario of smoke detection and reporting on the campus of the University of Calgary and generated a simulation dataset for the LBSN and WSN. Then, results of implementing IPAWL I, IPAWL II, and a stand-alone WSN on the dataset were compared in terms of network energy consumption and message transmission latency. Results demonstrated a 42% reduction in energy consumption in IPAWL I and 33% in IPAWL II. Moreover, a significant reduction of time latency was observed in bigger size networks.

Temperature and Humidity Optimization of Air Conditioner for Saving Electrical Energy Using Wireless Sensor Network Method

The consumption of electrical energy from year to year continues to increase. This is caused by the use of electrical energy and electrical equipment that inefficient. Hence, it is necessary to make efforts to save electricity efficiently. One of the efforts that have been made is research on the PPC (Programmable Power Controller) which functions as a controller for the use of electrical energy in the AC (Air Conditioner) device. Optimization of AC is also an effort to save electrical energy. This research focuses on technological efforts in the form of a room condition monitoring system design (temperature and humidity monitoring), AC mode setting scenarios, and room characterization for efficient use of electrical energy using the WSN (Wireless Sensor Network) method. The performance of the WSN sensor node as a monitoring device for room conditions which includes temperature and humidity has been successfully created using a temperature sensor, humidity sensor, signal conditioning circuit, and a programmed microcontroller. WSN sensor node results show that the DHT21 temperature and humidity sensor, which has a temperature range is 15°C-90°C, minimum humidity of 20% RH, and a maximum of 90% RH, testing works well. Meanwhile, the average time needed to carry out the node selection process until it is connected to the selected node when there is a default node, and no default node is 9.068 seconds and 9.968 seconds. Moreover, the implementation result of this device in the room area is in general, during working hours from 09:00 to 17:00, humidity conditions range from 30% - 75%, and temperatures range from 20°C - 40°C close to normal limits according to ASHRAE standard 55. In the next research, we focus on using the WSN relay to completely aim the function of the WSN sensor as a tool to reduce the electrical energy for monitoring and controlling the temperature and humidity AC.

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks.

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities

Wireless Sensor Networks (WSNs) are the main component in the Internet of Things (IoT) and smart cities to sense our environment, gather essential and meaningful data, and forward it to the base station (BS). Nowadays, IoT utilizes WSN as a necessary platform for sensing and communication of the data. One of the main strategies to minimize consumption of energy in a WSN with highly dense sensors is to maximize sleeping sensors at each time. This way implies to schedule the activity of sensors, i.e. determining when a sensor node is kept idle (sleep mode) and when a sensor node is activated to sense environment (active mode). Due to heterogeneity of the sensor nodes in WSNs-based IoT for smart cities, one approach for scheduling the sensing activity is clustering the sensors into K mutually different subsets, so that every subset of sensors alone can cover all targets of the network. In this case, we can solve finding the maximum number of sensor subsets, or equivalently sensor covers problem, by conversion it to SET K-COVER problem. In this paper, an energy aware Grouping Memetic Algorithm (GMA) is proposed for solving the SET K-COVER problem. The proposed GMA varies in four general ways from any of the other evolutionary algorithms for solving the SET K-COVER problem. First, to transform solution structures linked to the SET K-COVER problem into chromosome genes, a new encoding scheme was being used. Second, specific genetic operators appropriate for the chromosomes are used, based on the encoding used. Third, to direct the search process in the solution space of the SET K-COVER problem, a novel proper fitness function is proposed. Fourth, a local improvement algorithm which uses a sensor dominance rule is proposed. This study, carried out detailed experiments of different numbers of targets and different numbers of sensors in WSNs to assess the proposed algorithm. In several instances of the problem, the experiments demonstrate that the algorithm works considerably better in terms of solution quality than many other heuristics and evolutionary algorithms and significantly outperformed the evolutionary algorithms in terms of runtime, suggesting the usefulness of the proposed algorithm to increase the lifetime of WSN in IoT and smart cities.

Fusion protocol for improving coverage and connectivity WSNs

The authors proposed a fusion protocol using multilevel clustering (FPMC), which is completely dependent on the increased coverage and wireless sensor network (WSN) reliability-clustering framework to eliminate duplication. Therefore, a static sensor for a point of interest is allocated where possible so that the particular area can be protected. Furthermore, FPMC ensures that only one sensor node is allocated to cover a specific point of interest. The network coverage, energy usage, mean movement of WSNs nodes and sensor nodes are active in each loop due to which efficiency of the proposed protocol was assessed under various densities, and its performance was also analysed. Evaluations have shown that with restricted active nodes and sensor nodes, the FPMC can increase the networking coverage. The FPMC should be stressed as reducing the overlap level. The proposed protocol is based on the fusion protocol using multi-level clustering for improving coverage and connectivity WSNs.

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

With the development of Internet of Things (IoT) technologies, environmental monitoring systems using wireless sensor networks (WSNs) have received considerable attention. Reliable object detection and tracking is an important research issue in various WSN applications, such as environment and disaster monitoring, disaster propagation tracking, and intruder monitoring and tracking. Generally, because batteries are used as energy sources for sensors in WSNs, a highly energy-efficient operation is needed to prolong the life of the sensors and networks. To save energy, sensors usually manage multi-mode sensing operations, in which they periodically switch between active and inactive periods. A tradeoff exists between object detection accuracy and energy efficiency when we select a sensing schedule. Depending on the object speed, direction, and sensor deployment topology, different sensing schedules should be dynamically applied to individual sensors. In this paper, we propose a novel recurrent neural network (RNN)-based dynamic duty cycle control method for sensor nodes. For RNN training, a target optimal duty cycle for a given network condition is derived from the proposed digital twin-space analytic solution. Simulation results show that the proposed model provides accurate object detection performance and achieves high energy efficiency.