Energy Efficient Target Tracking with Collision Avoidance in WSNs

Kumar, Santosh; Sudhir,; Tiwari, Umesh

doi:10.1007/s11277-018-5944-6

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…However, this algorithm was not evaluated under various application scenarios. To evaluate under multiple application scenarios, Kumar and Tiwari 3 developed a target tracking technique to track the target and transmit data to the base station. Here, the network coding model was used for encrypting and decrypting the data packets.…”

Section: Motivationsmentioning

confidence: 99%

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Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Khare

Selvakumar

Dugyala

2023

Int J Communication

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Summary Wireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930.

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Section: Motivationsmentioning

confidence: 99%

“…Accordingly, these sensor components have less computational capability. However, the nodes in this network not only gather the information but also perform multihop data transmission for processing the data with the base node 3 . WSN does not require any human intervention; it has the capability of self configure.…”

Section: Introductionmentioning

confidence: 99%

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Khare

Selvakumar

Dugyala

2023

Int J Communication

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“…Therefore, these sensor components possess a reduced computational capacity. Nevertheless, the nodes within this network are responsible for both information gathering and multi-hop data transfer in order to analyze the data alongside the base node [5]. Wireless Sensor Networks (WSNs) has the unique characteristic of being able to operate autonomously without the need for human involvement.…”

Section: Introductionmentioning

confidence: 99%

Collision Mitigation by Dolphin Ant Lion Optimizer Pre-Packet Scheduling Approach

Raman Dugyala

2023

tjjpt

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A wireless sensor network (WSN) consists of distributed autonomous sensors deployed over a large geographical area. Wireless Sensor Networks (WSNs) are composed of large-scale sensors that are specifically assigned to do particular tasks, with a significant portion of these tasks involving reporting and monitoring activities. Nevertheless, because to the potential expansion of the network to several sensor nodes, the likelihood of collision is significantly increased. This research presents an innovative approach for conducting collision detection and mitigation in wireless sensor networks (WSN). The first step involves conducting a simulation of Wireless Sensor Networks (WSN), followed by the utilization of the Fractional Artificial Bee Colony (FABC) algorithm for the selection of cluster heads. In this context, the network-based parameter is derived by considering factors such as the Received Signal Strength Index (RSSI), priority level, delivery rate, and energy consumption. The Deep Recurrent Neural Network (DRNN) has been modified to suit the task of collision detection. The training of the deep recurrent neural network (DRNN) is conducted via the Lion Crow Search optimizer (LCSO). Following the completion of collision detection, the subsequent step involves the implementation of a collision mitigation process utilizing a pre-scheduling technique known as Dolphin Ant Lion Optimizer (Dolphin ALO). In this context, the evaluation of fitness encompasses many factors related to collision mitigation, including energy consumption, Sleep Index (SI), delivery rate, priority level, E-waste management, and E-save measures. The approach presented in this study demonstrated superior performance in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and collision detection rate. Specifically, it achieved the least energy consumption of 0.185, the greatest throughput of 0.815, the highest PDR of 0.815, and the highest collision detection rate of 0.930.

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“…As a result, it is essential to efficiently reduce energy consumption because energy conservation leads to an increased battery lifespan. There are various energy consumption reduction methods used in recent years (e.g., clustering, support vector machine) [ 13 , 14 ]. However, large-scale functional implementation of these approaches precludes more time and resources.…”

Section: Introductionmentioning

confidence: 99%

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Sultan

Waleed

Pyun

et al. 2021

Sensors

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The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

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Energy Efficient Target Tracking with Collision Avoidance in WSNs

Cited by 8 publications

References 15 publications

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Collision Mitigation by Dolphin Ant Lion Optimizer Pre-Packet Scheduling Approach

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

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