Resource-limited Sensor Research Articles

Online chatter detection via lightweight deep learning framework with efficient signal pre-processing

Deep learning methods have attracted considerable attention in the field of chatter detection due to their exceptional feature extraction and identification capabilities. However, it is challenging to adapt these methods to diverse machining conditions with limited sensor resources. In addition, the computational efficiency of these methods is compromised by their complexity, resulting in a reduced real-time performance for online chatter detection. To address them, this paper proposes a novel deep learning-based framework that combines an efficient signal pre-processing method with a novel lightweight parallel convolutional neural network (LPNN). A fast continuous wavelet transform algorithm is applied to the raw signal, effectively generating time–frequency maps that represent chatter characteristics. The LPNN incorporates the condensed block as a feature extraction unit, which is constructed using depthwise separable convolution. The introduction of the condensed block and the parallel structure increases the depth and width of the designed model while maintaining fewer parameters and higher computational efficiency. A transfer learning strategy is adopted to train the model, reducing the training time and data requirements for online chatter detection. Experimental results using large open-source turning datasets demonstrate the effectiveness of the proposed method in identifying chatter under various machining conditions. The proposed method outperforms other methods with a classification accuracy of 96.5% on the test set. Furthermore, the performance of the proposed method for online chatter detection is validated in our milling experiments, exhibiting enhanced real-time performance compared to other methods.

Lighting Model for Underwater Photogrammetric Captures

Abstract. Photogrammetry is an established technique for producing 3D representations of submerged structures in shallow, naturally lit environments. Natural light is not available in more extreme environments such as in the deep ocean or submerged caves, which are major applications for photogrammetric survey. Additionally, these environments are often accessed with resource-limited sensor platforms, necessitating efficient use of power constraining the level of artificial illumination that can be deployed. A method to estimate the amount of light needed to achieve sufficient image quality in underwater photogrammetric acquisition systems is presented.

Improved particle swarm optimization with reverse learning and neighbor adjustment for space surveillance network task scheduling

Space Surveillance Network (SSN) plays a crucial role in Space Domain Awareness (SDA) as it helps to maintain the catalog of Resident Space Objects (RSOs). Howe`ver, as the population of RSOs continues to grow, the contradiction between the increasing number of RSOs and limited sensor resources will be further apparent. How to effectively utilize existing sensor resources becomes a vital concern. This paper focuses on the Multi-sensor Collaborative Observation Scheduling (MCOS) in SSN. The workflow of SSN is summarized, and a Mathematical Programming Model (MPM) is established to maximize observation profit. Given the complexity of MPM, a modified Particle Swarm Optimization is proposed by combining Reverse learning, Neighbor adjustment, and Particle perturbation (RNP-PSO). In the MPM, multiple constraints such as observation time for each RSO, observation frequency, and sensor capability are considered, enabling a comprehensive description of the MCOS problem for the first time. The RNP-PSO incorporates a reverse learning algorithm to improve the quality of the initial population. Additionally, continuous mapping is added to the neighbor adjustment process to enhance the algorithm's search capabilities. Furthermore, a particle perturbation strategy is proposed to increase the diversity of solutions and avoid local optima. A series of experiments have been conducted to evaluate the performance of the RNP-PSO algorithm in solving the MCOS problem. The results demonstrate that compared to several other effective algorithms, the RNP-PSO algorithm significantly improves the Task Completion Rate (TCR) by approximately 17.01%, 19.1%, 13.53%, and 13.99%, while simultaneously reduces the Resource Consumption Rate (RCR) by about 55.48% and 36.38%. The experiments also include strategy and parameter sensitivity verification, which thoroughly validate the performance of the RNP-PSO algorithm in various aspects.

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

Prediction and classification of IoT sensor faults using hybrid deep learning model

The quality and reliability of internet of thing (IoT) ecosystems heavily rely on accurate and dependable sensor data. However, resource limited sensors are prone to failure due to various factors like environmental disturbances and electrical noise in which they can produce erroneous and faulty measurements. These can have significant consequences across different domains, including a threat to safety in critical systems. Though many researches have been conducted, the existing literature primarily focuses on fault detection in the sensor data, while fault detection is useful, it is still a reactive approach that identifies the faults after they have occurred, meaning that actions are taken after the fault has already impacted the system, potentially leading to negative consequences. In this study, a proactive approach has been proposed by developing a two-stage solution. In the first stage, a hybrid convolutional neural network-long short term memory (CNN-LSTM) model was trained to forecast sensor measurements based on historical data, while in the second stage, the forecasted measurements were passed to a hybrid convolutional neural network-multi layer perceptron (CNN-MLP) model that has been trained to recognize different types of sensor faults and classify the new measurements accordingly. By passing the forecasted sensor values as input to the classification model and categorizing them as normal, bias, drift, random or poly-drift, anticipated the potential faults before they manifest. The publicly available Intel Lab data raw dataset is used, which has been annotated and fault-injected. For regression, gated recurrent unit (GRU), Long short term memory (LSTM), bidirectional long short term memory (BiLSTM), convolutional neural network-gated recurrent unit (CNN-GRU), convolutional neural network-long short term memory (CNN-LSTM), and convolutional neural network-bidirectional long short term memory (CNN-BiLSTM), were evaluated and compared their performance using root mean squared error (RMSE), mean squared error (MSE) and mean absolute error (MAE) with 2-split time series cross-validation. CNN-LSTM outperformed the other models with a Mean Absolute Error of 2.0957 for a 45 time steps forecast. For the classification task, convolutional neural network (CNN), multi-layer perceptron (MLP), and convolutional neural network-multi layer perceptron (CNN-MLP) evaluated using the metrics accuracy, precision, recall, and F1-score with 5 and tenfold cross-validations. CNN-MLP outperformed the others with accuracy of 96.11% for bias, 99.33% for drift, and 98.61% for random and 98.81% for poly-drift. The average accuracy across the 4 faults is 98.21%, which is a 0.3% increase from the baseline work 97.91%. By adopting a proactive approach to sensor fault prediction and classification, this research aims to enhance the reliability and efficiency of IoT systems, allowing for preventive measures to be taken before faults have a detrimental impact.

APAP: An adaptive packet-reproduction and active packet-loss data collection protocol for WSNs

In lossy, large-scale wireless sensor networks (WSNs), data collection protocols face the challenge of balancing expected data transmission reliability and limited resources of sensors. Packet redundancy strategies have been widely employed to guarantee transmission reliability. However, these schemes often result in energy waste, especially in sink-neared hotspot districts. We propose the APAP to balance the two competing interests in energy-constrained WSNs. APAP mainly consists of two parts: adaptive packet-reproduction routing and active packet-loss mechanism. In the first part, we design a compensation function to compensate for packet losses and employ it in the packet-reproduction routing. Through available network parameters, such a function offers an optimized number of redundant packets. In the second part, we devise a novel distributed packet-loss mechanism to detect and intercept redundant packets in the hotspot area. Hotspot nodes record the simple traffic information in their vicinity on a local micro-table, these nodes transform their mode based on the records to reach a basic consensus with one-hop neighbors, they then collaboratively intercept excess packets from outside, thus the injection traffic can be mitigated. Such an interception area has the potential to be expanded and prolonged as the redundancy becomes more severe. Our mechanism imposes low-complexity requirements on general devices. Moreover, thorough mathematical analyses are offered for the performance of APAP. Simulation results indicate that with high reliability, the maximum node energy consumption is reduced by 44.7% to 66.3% compared to conventional protocols in one round. Besides, the network lifetime is prolonged by 91.1% to 200%.

A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT

The limited bandwidth and power resources of wireless sensors in distributed environments has resulted in new challenges in handling the ever-growing volume of transmissions generated by the Internet of Things (IoT) applications. To overcome these challenges, each sensor should compress and quantize its observations before sending them to a fusion center (FC) for global decision inference. Unfortunately, most of the conventional compression techniques and entropy quantizers only focus on reconstruction fidelity as a performance measure, neglecting the sensing goal. In this paper, we propose a joint design of compression mechanisms and entropy quantizers with the sensing goal for machine-to-machine (M2M) communications. We define a deep learning-based framework for compressing and quantizing observations from correlated sensors. Unlike traditional methods, our proposed method not only maximizes the reconstruction fidelity but also optimally compresses sensor observations in terms of the accuracy of the inferred decision (i.e., the sensing goal) at the FC. The proposed framework is widely applicable as it does not impose any assumptions on observation distribution. Furthermore, a novel loss function has been proposed to focus on learning complementary features at each sensor. Our experimental results demonstrate that the framework outperforms other benchmark models.

Advancements in Neighboring-Based Energy-Efficient Routing Protocol (NBEER) for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have gained prominence in wireless sensor technology, featuring resource-limited sensor nodes deployed in challenging underwater environments. To address challenges like power consumption, network lifetime, node deployment, topology, and propagation delays, cooperative transmission protocols like co-operative (Co-UWSN) and co-operative energy-efficient routing (CEER) have been proposed. These protocols utilize broadcast capabilities and neighbor head node (NHN) selection for cooperative routing. This research introduces NBEER, a novel neighbor-based energy-efficient routing protocol tailored for UWSNs. NBEER aims to surpass the limitations of Co-UWSN and CEER by optimizing NHNS and cooperative mechanisms to achieve load balancing and enhance network performance. Through comprehensive MATLAB simulations, we evaluated NBEER against Co-UWSN and CEER, demonstrating its superior performance across various metrics. NBEER significantly maximizes end-to-end delay, reduces energy consumption, improves packet delivery ratio, extends network lifetime, and enhances total received packets analysis compared to the existing protocols.

Bandwidth and power efficient lightweight authentication scheme for healthcare system☆☆☆☆☆☆

With the emergence of the Internet of Things (IoT) and the rapid advancement in sensor technology, wireless medical sensor networks (WMSNs) have become increasingly important in improving health services. WMSNs are deployed for remote monitoring and collecting physiological data of patients for diagnosis and treatment. With the assistance of wearable and mobile devices, IoT-based innovative healthcare solution is extensively adopted to provide better, more precise, and quicker diagnoses. However, the WMSN of healthcare is bandwidth limited and has low latency, while the resource-limited sensors and tremendous IoT output cannot run on practiced security protocols that involve complex encryption/decryption operations. A security system that requires minimal computation and communication time to reduce delay and save the devices’ power source is much needed. Therefore, this article presents a lightweight and robust authentication protocol for the healthcare system that can be used with power and bandwidth-limited devices. Furthermore, this scheme uses random numbers instead of complex encryption/decryption to reduce computation time and energy consumption. The security and performance analysis results show that the proposed method performs better than its competitors regarding security, communication, and computation costs and prolongs the network lifetime.

EEGT: Energy Efficient Grid-Based Routing Protocol in Wireless Sensor Networks for IoT Applications

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network.

Blockchain-Enabled IoT for Rural Healthcare: Hybrid-Channel Communication with Digital Twinning

Internet of Things (IoT) and blockchains are enabling technologies for modern healthcare applications, offering the improved monitoring of patient health and higher data integrity guarantees. However, in rural settings, communication reliability can pose a challenge that constrains real-time data usage. Additionally, the limited computation and communication resources of IoT sensors also means that they may not participate directly in blockchain transactions, reducing trust. This paper proposes a solution to these challenges, enabling the use of blockchain-based IoT healthcare devices in low-bandwidth rural areas. This integrated system, named hybrid channel healthcare chain (HC2), uses two communication channels: short-range communication for device authorisation and bulk data transfer, and long-range the radio for light-weight monitoring and event notifications. Both channels leverage the same cryptographic identity information, and through the use of a cloud-based digital twin, the IoT device is able to sign its own transactions, without disclosing the key to said twin. Patient data are encrypted end to end between the IoT device and data store, with the blockchain providing a reliable record of the data lifecycle. We contribute a model, analytic evaluation and proof of concept for the HC2 system that demonstrates its suitability for the stated scenarios by reducing the number of long-range radio packets needed by 87× compared to a conventional approach.

Energy Consumption Minimization for Data Collection From Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design With DRL

Reliable and energy-efficient data collection from resource-limited sensors is essential to the success of future Internet of Things (IoT). In this article, we study the energy consumption minimization problem during the data collection from a generic wirelessly-powered sensor. Specifically, we determine the optimal data collection parameters, in terms of charging duration and charging power as well as sensor transmission rate, in real time according to the instantaneous channel condition while satisfying a certain latency constraint. For the scenario of ideal rate-adaptive transmission with linear energy harvesting, we derive closed-form expressions for optimal transmission parameters. We also establish the condition on channel quality for successful data collection under a latency constraint. For the more practical case of finite block-length transmission with nonlinear energy harvesting, we develop a deep reinforcement learning (DRL) solution for efficient online implementation. We also propose an online tuning scheme to cater for model inaccuracy and environment variation. The accuracy and effectiveness of our proposed approaches are verified by comparing with benchmark schemes. Our DRL-based approach has broad applicability and can solve other real-time optimal design problems in wireless communications.

Collision-Free Motion Algorithms for Sensors Automated Deployment to Enable a Smart Environmental Sensing-Net

As natural habitats protection has become a global priority, smart sensing-nets are ever-increasingly needed for effective environmental observation. In a practical monitoring network, it is critical to deploy sensors with sufficient automated intelligence and motion flexibility. Recent advances in robotics and sensors technology have enabled automated mobile sensors deployment in a smart sensing-net. Existing deployment algorithms can be employed to calculate adequate destinations (goals) for sensors to perform respective monitoring tasks. However, given the calculated goal positions, the problem of how to actually coordinate a fleet of robots and schedule moving paths from random initials to reach their goals safely, without collisions, remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we investigate this problem and propose polynomial-time collision-free motion algorithms based on batched movements to ensure all the mobile sensors reach their goals successfully without incurring collisions. We observe that the grouping (batching) strategy is similar to the coloring procedure in graph theory. By constructing a conflict graph, we model the collision-free path scheduling as the well-known k-coloring problem, from which we reduce to our k-batching problem (determining the minimum number of required batches for a successful deployment) and prove its NP completeness. Since the k-batching problem is intractable, we develop CFMA (collision-free motion algorithm), a simple yet effective batching (coloring) heuristic mechanism, to approximate the optimal solution. Performance results show that our motion algorithms outperform other existing path-scheduling mechanisms by producing 100% sensors reachability (success probability of goals reaching), time-bounded deployment latency with low computation complexity, and reduced energy consumption. Note to Practitioners— This research was originally motivated by an oceanography project, which studied marine microbes by sending a team of tiny robots (sensors) randomly scattered on the ocean floor. For hard-to-access habitats like deserts or oceans, where manual placement of sensors is costly or impossible, automatically scheduling robots movements to calculated positions from random initials is essential for an effective monitoring. Our contribution is unique in two ways. First, traditional path-planning research focuses more on independent robots navigating the environment, whereas we target on the network automation problem, coordinating a fleet of robots working together to perform an environmental sensing task. Second, we observe that existing movement methods are too complicated and energy-consuming due to the calculation on-the-go nature. To provide a practical solution, we suggest and design our motion algorithms from a new perspective: pre-scheduled batched movements. Our approach requires a central server for grouping (batching) calculations, but can effectively reduce computation complexity and save significant energy expenditure exerted on resource-limited sensors. The proposed collision-free motion algorithm (CFMA) is a suboptimal yet efficient batching solution, which can be easily applied in real-life open-space monitoring networks. Insights from this foundational research will facilitate future opportunities to implement and verify our method in operational monitoring testbeds.

Aggregation-Scheduling Based Mechanism for Energy-Efficient Multivariate Sensor Networks

Recently, the world has witnessed a technology revolution in many sectors and fields with the aim to enhance the quality of life and the human security. Particularly, the integration of small sensing devices into a wide range of objects has enabled the deployment of new services and applications that made our lives smarter and safety. With such sensing-based technology, we can adequately monitor, access harsh environments, understand various phenomena and make a decision accordingly. However, sensing-based applications face several challenges beginning from the data collection at the sensors themselves until the decision-making process at the end-user. In this study, we focus on the energy conservation as major challenge in sensor applications due to the limited sensor resources that are not always possible to be replaced or recharged. We propose an aggregation-scheduling based mechanism called as AGING for Energy-Efficient multivariate sensor networks (MSN). Mainly, AGING is based on the cluster network scheme and consists into two phases: aggregation and scheduling. The aggregation phase is applied at each node and aims to reduce the huge amount of data sent periodically to the cluster-head (CH) based on a user-defined score table and a multi-aggregation mechanism. The second phase is applied at CH and uses a new scheduling strategy to switch nodes generating similar data into sleep/active modes; the CH first converts the correlated nodes into a graph before applying a coloring-map algorithm and a scheduling strategy to select the set of active nodes in next periods. Through simulations on real sensor data, we show the relevance of our mechanism in terms of saving the node energies and prolong the network lifetime compared to other existing techniques.

A novel routing protocol based on grey wolf optimization and Q learning for wireless body area network

Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result analysis shows its efficiency in terms of energy for WBAN.

Localization in Mobile Sensor Networks using Kalman Filter

Objectives: The objectives of this study is to propose a localization scheme that meets the requirements of Mobile Sensor Networks (MSNs) in terms of high localization accuracy, lower power consumption and high network coverage. Methods: In this study, a localization scheme has been proposed to allow sensor nodes to estimate their locations using information transmitted by a set of anchor nodes that know their own locations. The proposed scheme performs localization process in a more general network environment where no special hardware for ranging is available, the prior deployment of anchor nodes is unknown, the anchor nodes density is low, the node distribution is irregular, and where sensor nodes and anchors can move uncontrollably. The proposed scheme applies the Kalman Filter (KF) technique to combine the information from anchor nodes to estimate the location of sensor nodes. The KF takes into account the different uncertainties and error sources that disturb the localization process. Findings: The proposed scheme has been compared with the two classes of Flexible Optimal Kalman Filtering FOKF algorithm (FOKFO and FOKF-R) under different mobility models and extensive simulations in large scale network. The results show that the proposed scheme has a lower localization error and lower communication cost than the FOKF algorithm which leads to maximizing network lifetime and energy saving. The proposed scheme has an improved percentage of localization accuracy than FOKF-O and FOKF-R by 5.0% and 4.0% respectively, also it has an improved percentage of coverage than FOKF-O and FOKF-R by 10.0% and 5.0% respectively, finally it has a less number of transmitted beacon messages than FOKF-O and FOKFR by 171 and 113 packets respectively which leads to maximizing network lifetime. Novelty/Applications: The proposed scheme is considered a suitable localization solution for resource-limited sensor nodes in terms of memory limitations, energy consumption and localization accuracy. Keywords: Localization; Mobile Sensor Networks; Kalman Filter; FOKFO; FOKFR

Potential Energy Efficient Data Fusion Model for Wireless Sensor Networks

Wireless sensor networks have made a significant contribution to wireless sensor communication system based on resource constraints and limited computational sensors. Over the last decade, several focused research efforts have been made to investigate and provide solutions to problems relating to the energy efficiency data fusion aggregation in Wireless sensor networks. However, the problem of designing routes that are energy efficient has not been resolved. It is rather a tough effort to guarantee that the lifespan of a sensor is prolonged for a longer period because of the restricted computational capabilities of sensors, which are often coupled with energy constraints. The findings of this work present an enhanced energy-efficient technique for communication in sensor networks which consists of three distinct innovative frameworks. The suggested framework known as Data Fusion with Potential Energy Efficiency (DFWPEE) is responsible for the optimization of energy. The proposed work reduces energy consumption by using probabilistic methods and clustering. During the data fusion process, the Multiple Zone Data Fusion (MZDF) architecture uses a globular topology that helps with load balancing. The strategy presents an innovative routing approach that is used to aid in the performance of energy efficient routing in large-scale wireless sensor networks. By introducing the idea of routing agents, the framework for the Tree-Based Fusion Technique (TBFT), as suggested, comes up with an innovative method for dynamic reconfiguration. The plan enables the system to determine which sensor has a higher rate of energy dissipation and then immediately transfers the job of data fusion to a node that is more energy efficient. This threshold-based technique enables a sensor to perform both the role of a cluster head and the function of a member node. The node behaves as a cluster head until it achieves its threshold remnant energy and functions as a member node after it passes the threshold residual energy. Both of these roles may be played simultaneously. The mathematical modeling was done using the conventional radio energy model which improved the dependability of attained results. The proposed system delivers enhanced energy efficient communication performance when measured against existing implemented standards for energy efficient schemes. The enhanced technique uses nearly half as much energy as LEACH while focusing on reducing the overall time taken for the process to complete leading to enhanced performance.

Distributed set-membership estimation for state-saturated systems with mixed time-delays via a dynamic event-triggered scheme

This paper is focused on the distributed estimation issue in the form of set-membership (SM) for a class of discrete time-varying systems suffering mix-time-delays and state-saturations. The phenomena of time-delays and state-saturations are introduced to better describe insightful engineering. During local measurements transmission between sensors over a resource-limited sensor network, to prevent data collisions and resource-consumption, a newly dynamic event-triggering strategy (DETS) is designed to dispatch the local measurements transmission for each sensor to its neighbors. Compared with the most existing static ETSs, this DETs can mitigate the total number of triggering times and enlarge interval time between consecutive triggering instants. Then, some novel adequate criteria for designing the desired event-based SM estimators are derived such that the plant’s true state always resides in each sensor’s ellipsoidal region regardless of the simultaneous presence of bounded noises, mixed time delays and state-saturations. Subsequently, a recursive optimization algorithm is formulated such that the minimal ellipsoids, the estimators gains and event-triggering weighted matrices are acquired simultaneously. A verification simulation is presented to illustrate the advantages of the design approach of the developed state estimator.

An adaptive multi-sensor resource allocation algorithm based on efficiency function

In this paper, combined with the Kalman filtering algorithm, the linear weighted sum of the normalized prediction information increment DI and the norm of target filter error covariance matrix ║ cP ║ is used to represent the pairing function of the target-sensor combination and the assignment and automatic updating of the pairing function between sensor and target are realized. Based on the analytic hierarchy process (AHP), an adaptive sensor resource allocation algorithm is proposed. The simulation results show that the algorithm can effectively integrate various evaluation factors and is compatible with different types of targets. The algorithm has simple structure and good versatility. The limited sensor resources can be dynamically adjusted according to the priority of targets, making the sensor allocation strategy more reasonable and effective.

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

Large‐scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs’ lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs’ lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).