A Survey on Mobile Agent Itinerary Planning for Information Fusion in Wireless Sensor Networks

Fissaoui, Mohamed El; Beni‐Hssane, Abderrahim; Ouhmad, Slimane; Makkaoui, Khalid El

doi:10.1007/s11831-020-09417-1

Cited by 16 publications

(6 citation statements)

References 41 publications

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“…One drawback of this approach is that it compromises query results for energy efficiency and is suitable only for those applications in which the accuracy of the query results is not a matter of concern. A few prominent data prediction models proposed for wireless sensor networks include AutoRegressive Integrated Moving Average (ARIMA) technique [107], least mean square technique [108]- [110], dual prediction scheme [111]- [113].…”

Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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“…The late fusion or decision fusion combines the unimodel decisions to conclude the final decision [20]. The late-fusion models are more accessible to simulate than early fusion models, especially when the modalities have inconstant sampling rate and data dimensionality [21,22]. In [23], a feature transformation network has been designed to learn the RGB and depth features.…”

Section: Data Fusion Importancementioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

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Autonomous Driving Vehicle (ADV) services have become a prominent motif in intelligent vehicle technology by adapting deep learning features. Automated driverless services are a hercules task due to the dynamic driving environment and the performance is deliberately reliant on the quality of data fusion from sensors. Therefore, considering advanced 3D LiDAR sensors is essential to measure the surrounding with 360 • coverage. However, accomplishing maximum autonomy is the main challenge because of debilitated and complex driving environmenst. Deep Learning (DL) based models potentially impact surrounding measures for object detection, classification, and tracking. This study describes the importance of DL-LiDAR strategies to formulate ADV research challenges followed by a comprehensive analysis of Semantic Segmentation, Data Fusion, Data Representation, Feature Extraction, Dynamic Object Detection, and Autonomous Driving-Multi-Objective Tracking mechanisms. Unlike existing review papers, we examine various deep learning influenced approaches and describe their precarious measurements. Also, we analyze the impact of systematic methods based on open-source data sets for objectx (x=classification, localization, tracking) consolidation. Most of the summaries confine scalability, and feasibility of DL techniques in ADV to accomplish an intelligent driving system without human interference. Open research problems are discussed in the last section of the article.

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“…However, the performance of an MA mainly depends upon the assigned plan provided by an itinerary. In addition, the size of a single mobile code may itself become a bottleneck and may contribute to latency and less scalability for a plethora of IoT devices [63]. Therefore, it is important to pay attention to the length of itineraries, such as long itineraries that may lead to traveling delays [64].…”

Section: Mobile Code and Itinerary Planningmentioning

confidence: 99%

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

Tariq

Asim

Khan

et al. 2020

Sensors

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A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

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A Survey on Mobile Agent Itinerary Planning for Information Fusion in Wireless Sensor Networks

Cited by 16 publications

References 41 publications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

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