Elastic neural network method for multi-target tracking task allocation in wireless sensor network

Liu, Mei; Li, Haihao; Shen, Yi; Fan, Jianfeng; Huang, Shuangning

doi:10.1016/j.camwa.2008.10.050

Cited by 19 publications

(4 citation statements)

References 10 publications

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“…It differs from a neural-based feed-forward network that doesn't rely on previous data, thus losing control over the variant behavior of the datasets. In a loose sense, RNN [7,8] works on remembrance of the previous data/info to which it is exposed and produces dynamic models on it-i.e., models that change with time-in that way yield to maximum efficiency. [9] investigate the potential of edge computing to address the challenges in integrating AI and IoT in rural settings.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Neural Network-Based Soil Parameters Predictive Coordination Algorithm for Energy Efficient Wireless Sensor Network

Sharma

Tomar²

2023

Preprint

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Wireless Sensor Network (WSN) is the best implementation of Information Technology in the agricultural sector. With interest blooming in them, and evolving technology, sensor networks are not only capable of giving the real-time nutrient index of the soil, but could also help to regulate all agricultural processes. However, WSN comes with a major limitation, i.e., energy. Based on experimental data collected from various soil forms, and analyzed using advanced data analytics, the Nutrient Index is somehow linked and doesn’t show frequent change with time. Therefore, by using predictive neural network techniques, one can easily get detailed info with primary inputs received from WSN. There is no need to make the WSN run for the entire day. Instead, for making an energy-efficient WSN, NR-MDEC protocol is used along with a coordination algorithm which enhances the efficiency dramatically.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Neural Network-Based Soil Parameters Predictive Coordination Algorithm for Energy Efficient Wireless Sensor Network

Sharma

Tomar²

2023

Preprint

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“…Multiple elastic NN modules (MEMs) [25] remain an enhancement to SOM. MEMs normalize the self-organizing paradigm's parameters for facilitating the administration of high-level intricate optimization issues like computer vision.…”

Section: Gil and Hanmentioning

confidence: 99%

An Optimal Cluster Head Selection with Trusted Path Routing and Classification of Intrusion in WSN Employing CHLNNet

Obaid¹,

Awad²,

Khaleel³

2022

ISI

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A wireless sensor network consists of a large number of sensors dispersed across a large area. These are used in broad areas including queue management, military applications, ecological applications, and others. This method, which combines deep learning and optimisation strategies with a focus on attack identification, is still under testing. The nodes will first be distributed randomly, centred on the network's dimension, under a system paradigm. Comparison sets are produced by use of an energy-related timer. Later, the geographical comparison, the quality of the link between the cluster head (CH) and cluster member (CM) nodes, and the node's remaining network energy will all be taken into account when analysing the transmission probability. The CH will determine how to manage the trust. The node will be chosen as CH after it meets the criteria for trust coverage. This will be chosen as CM if the situation is still unsatisfactory. The Dempster-Shaft theory and multi-dimensional trust criteria will be used to determine the cluster pathways' (CP) optimal range for effective data transfer, with residual energy and distance being the key constraints. Cascaded Hermite Laguerre Neural Network will classify and identify the attack if the best and most reliable path is still chosen (CHLNNet). This proposed approach will be compared against three sophisticated methodologies with regard to several parameters. As a result, the suggested CHLNNet technique achieves 91.4% of malicious detection rate, 28.2% average latency, 94.8% throughput, 23% end-to-end delay, and 31.4% routing overhead.

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“…Finite-state machine (FSM) means the finite state set, and the mathematical model of the behaviours between these states such as transfer and motion etc; the object only remains in a specific state at any time [5][6]. In any state, if some event occurs, it will select a method to solve it or determine whether to transfer it to the next state based on the difference between the current state and the input event.…”

Section: Fsm-based Active Tracking Modelmentioning

confidence: 99%

A Logistics Status Real-time Tracking System Based on Wireless Sensor Network

Chen

2018

Int. J. Onl. Eng.

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Abstract-The progress in the economic society has driven the rapid development of the logistics industry. In the face of increasingly enormous and complicated logistics systems, it is of great practical significance to study how to effectively control the logistics process, minimize the labour cost and provide real-time monitoring data for users. For this purpose, this paper designs an active logistics status tracking (ALST) system platform based on Wireless sensor network (WSN). This paper, by fully integrating the characteristics of WSN, designs the system architecture based on the design philosophy of active logistics status tracking method; it also constructs and realizes the hardware function modules such as wireless sensor node, vehicle gateway, user terminal and backend server etc., and adopts the finite-state machine (FSM)-based active tracking model to maintain the logistics object status; and then it carries out programming of wireless sensor node active state tracking in TinyOS2.x, and finally performs system test under normal operation. The active logistics status tracking system can be widely applied in the logistics industry, to optimize the logistics process, reduce losses and improve logistics operation efficiency.Keywords-wireless sensor network (WSN), active logistics status tracking (ALST), finite-state machine (FSM), tinyos.2 IntroductionWith the increasingly complex logistics system nowadays, it is of great significance to study how to effectively control the logistics process, save labour costs and provide real-time monitoring data for users [1][2]. In the traditional logistics tracking method, the centralized control and optimization methods were often applied, which could optimize the operation of logistics system to some degree, but cannot satisfy the monitoring demands of the rapidly changing logistics system due to its technical limitations [3][4]. Now, in terms of information acquisition and network deployment, the WSN has been freed from the limitations of infrastructures in the traditional network, and can be customized to the demands of logistics operation. Compared with the traditional technology, it can not only collect and transmit data, but also perceive the status information of things and surrounding environment with certain computing 58

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Elastic neural network method for multi-target tracking task allocation in wireless sensor network

Cited by 19 publications

References 10 publications

Neural Network-Based Soil Parameters Predictive Coordination Algorithm for Energy Efficient Wireless Sensor Network

Neural Network-Based Soil Parameters Predictive Coordination Algorithm for Energy Efficient Wireless Sensor Network

An Optimal Cluster Head Selection with Trusted Path Routing and Classification of Intrusion in WSN Employing CHLNNet

A Logistics Status Real-time Tracking System Based on Wireless Sensor Network

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