Smart Judiciary System: A Smart Dust Based IoT Application

Jain, Shelendra Kumar; Kesswani, Nishtha

doi:10.1007/978-981-13-8300-7_11

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve these problems, technology of disposable IoT that has reduced size, price, and weight and has been constantly studied and developed. In the late 1990s, The University of California developed a very small sensor with a size of 1-2 mm called "Smart Dust" [14]. It has a technology that can detect and manage surrounding physical information (temperature, humidity, acceleration, etc.)…”

Section: Disposable Iot Sensorsmentioning

confidence: 99%

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Cho

Lee

2020

Applied Sciences

View full text Add to dashboard Cite

Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the Internet of Things (IoT). The main aims of the autonomous things, and one of advanced of IoT, is that it creates a flexible environment that enables movement and communication between objects anytime, anywhere, by saving computing power and utilizing efficient wireless communication capability. However, the existing LEACH method is only based on the model with a static topology, but a case for a disposable sensor is included in an autonomous thing’s environment. With the increase of interest in disposable sensors which constantly change their locations during the operation, dynamic topology changes should be considered in LEACH. This study suggests the probing model for randomly moving nodes, implementing a change in the position of a node depending on the environment, such as strong winds. In addition, as a method to quickly adapt to the change in node location and construct a new topology, we propose Q-learning LEACH based on Q-table reinforcement learning and Fuzzy-LEACH based on Fuzzifier method. Then, we compared the results of the dynamic and static topology model with existing LEACH on the aspects of energy loss, number of alive nodes, and throughput. By comparison, all types of LEACH showed sensitivity results on the dynamic location of each node, while Q-LEACH shows best performance of all.

show abstract

Section: Disposable Iot Sensorsmentioning

confidence: 99%

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Cho

Lee

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…They discussed how smart dust technology can help in border surveillance against terrorism. Jain et al [35] proposed a smart judiciary system using smart dust technology. In their work, they discussed how the technology can be used for detecting and reducing crimes in smart cities.…”

Section: Introductionmentioning

confidence: 99%

Accurate Location Estimation of Smart Dusts Using Machine Learning

Bashir¹,

Malik²,

Lai³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors. This raises many challenges including how to estimate the location of events sensed by the smart dusts. Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces. The study proposes a novel machine learning based localization algorithm for estimating the location of events. The approach utilizes timestamps (time of arrival) of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the location. Due to the unavailability of real smart dusts, we built a simulator for analysing the accuracy of the proposed approach for monitoring forest fire. The experiments on the simulator show reasonable accuracy of the approach.

show abstract

“…IoT applications like smart city, smart healthcare systems, smart building, smart transport and smart environment [3], industrial, agriculture, supply chain management [4], smart B Shelendra Kumar Jain shelendra23@hotmail.com 1 Department of Computer Science, Central University of Rajasthan, NH-8, Bandar Sindri, Dist-Ajmer, Rajasthan 305817, India retail, location-based services, etc. may deal with sensitive data such as health information, financial information [5], location footprints, Personally Identifiable Information (PII) [6], data of personal life, etc.…”

Section: Introductionmentioning

confidence: 99%

A noise-based privacy preserving model for Internet of Things

Jain

Kesswani

2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

With the ever-increasing number of devices, the Internet of Things facilitates the connection between the devices in the hyper-connected world. As the number of interconnected devices increases, sensitive data disclosure becomes an important issue that needs to be addressed. In order to prevent the disclosure of sensitive data, effective and feasible privacy preservation strategies are necessary. A noise-based privacy-preserving model has been proposed in this article. The components of the noise-based privacy-preserving model include Multilevel Noise Treatment for data collection; user preferences-based data classifier to classify sensitive and non-sensitive data; Noise Removal and Fuzzification Mechanism for data access and user-customized privacy preservation mechanism. Experiments have been conducted to evaluate the performance and feasibility of the proposed model. The results have been compared with existing approaches. The experimental results show an improvement in the proposed noise-based privacy-preserving model in terms of computational overhead. The comparative analysis indicates that the proposed model without the fuzzifier has around 52–77% less computational overhead than the Data access control scheme and 46–70% less computational overhead compared to the Dynamic Privacy Protection model. The proposed model with the fuzzifier has around 48–73% less computational overhead compared to the Data access control scheme and 31–63% less computational overhead compared to the Dynamic Privacy Protection model. Furthermore, the privacy analysis has been done with the relevant approaches. The results indicate that the proposed model can customize privacy as per the users’ preferences and at the same time takes less execution time which reduces the overhead on the resource constraint IoT devices.

show abstract

Smart Judiciary System: A Smart Dust Based IoT Application

Cited by 7 publications

References 12 publications

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

Accurate Location Estimation of Smart Dusts Using Machine Learning

A noise-based privacy preserving model for Internet of Things

Contact Info

Product

Resources

About