scholarly journals Deep CNN for Indoor Localization in IoT-Sensor Systems

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3127 ◽  
Author(s):  
Wafa Njima ◽  
Iness Ahriz ◽  
Rafik Zayani ◽  
Michel Terre ◽  
Ridha Bouallegue
Keyword(s):  
Computational Complexity ◽  
Indoor Localization ◽  
Parameters Optimization ◽  
Radio Image ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Deep Cnn ◽  
Iot Devices ◽  
The Internet Of Things ◽  
High Computational Complexity

Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.

scholarly journals Indoor Localization Using Bayesian Filter

2019 ◽  
Author(s):  
Karthik Muthineni ◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Communication Technology ◽  
Indoor Localization ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Ambient Intelligent ◽  
Unknown Node ◽  
Visualization Of Data ◽  
Log Normal ◽  
Environment Parameters

Ambient Intelligent (AmI) Wireless Sensor Networks (WSN) provide intelligent services based on user and environment data obtained by sensors. Such networks are developed to give environmental monitoring and indoor localization services. In this work, Zigbee which is a wireless communication technology is used for localization based on Received Signal Strength Indicator (RSSI) method. In practice, Extended Kalman Filter (EKF) is adapted to filter RSSI values influenced by multi-path fading and noise. Log-Normal Shadowing Method (LNSM) together with the Trilateration method was implemented to locate the position of the unknown node or entity. In addition, Cramer Rao Lower Bound (CRLB) is derived for the position estimation, that can be used to evaluate the performance of the system in terms of localization accuracy. Along with indoor localization, the deployed WSN could also monitor environment parameters like temperature and humidity surrounding entity using Digital Humidity and Temperature (DHT11) sensor. Using Zigbee location coordinates of entity and environment parameters are transmitted to remote desktop where visualization of data is done using Matrix Laboratory (MATLAB).

scholarly journals Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

2019 ◽  
Vol 9 (12) ◽  
pp. 2414 ◽  
Author(s):  
Wafa Njima ◽  
Rafik Zayani ◽  
Iness Ahriz ◽  
Michel Terre ◽  
Ridha Bouallegue
Keyword(s):  
Gradient Descent ◽  
Indoor Localization ◽  
Matrix Completion ◽  
Stochastic Gradient Descent ◽  
Mathematical Concepts ◽  
Localization Accuracy ◽  
Moment Estimation ◽  
Localization Scheme ◽  
Accelerated Gradient ◽  
The Internet Of Things

In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.

scholarly journals QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices

2021 ◽  
Vol 5 (4) ◽  
pp. 1-30
Author(s):  
Saideep Tiku ◽  
Prathmesh Kale ◽  
Sudeep Pasricha
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Indoor Localization ◽  
Cyber Physical Systems ◽  
Learning Models ◽  
Crucial Aspect ◽  
Localization Accuracy ◽  
Physical Systems ◽  
Accurate Localization ◽  
Localization Model

Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future. Such services are poised to reinvent the process of navigation and tracking of people and assets in a variety of indoor and subterranean environments. The growing ownership of computationally capable smartphones has laid the foundations of portable fingerprinting-based indoor localization through deep learning. However, as the demand for accurate localization increases, the computational complexity of the associated deep learning models increases as well. We present an approach for reducing the computational requirements of a deep learning-based indoor localization framework while maintaining localization accuracy targets. Our proposed methodology is deployed and validated across multiple smartphones and is shown to deliver up to 42% reduction in prediction latency and 45% reduction in prediction energy as compared to the best-known baseline deep learning-based indoor localization model.

Cryptanalysis of a novel bitwise XOR rotational algorithm and security for IoT devices

2021 ◽  
Vol 25 (1) ◽  
pp. 139-147
Author(s):  
Seema Nath ◽  
Subhranil Som ◽  
Mukesh Chandra Negi
Keyword(s):  
Computational Complexity ◽  
Internet Of Things ◽  
Advanced Encryption Standard ◽  
The Internet ◽  
Key Generation ◽  
Standard Algorithm ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

The internet of things (IoT) is a multiple devices, which connects with the internet for communication, in order to obtain the updated from the cloud. The fog can act as a controller and it is located between the IoT devices and cloud. The major attacks like de-synchronization, and disclosure has arises in the devices, this has been prevented. The major contribution in this work is key generation and authentication, for key generation the “advanced encryption standard algorithm” is developed, in which the new and old keys are generated. The encryption is done under the source side, and decryption is done under the device side. The fog security is maintained through “device tag, and bit wise XOR rotational algorithm”. The security, and the computational complexity is defined in this work and it is given in table format. The implementations are carried out in the MATLAB R2016 a. The proposed algorithm is compared with the existing protocols like LMAP, M2AP, EMAP, SASI, and RAPP, from the comparison the proposed methodology makes the better knowledge about the security and prevents from various attacks.

Agricultural Application of Web of Things Architecture

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 61-63 ◽  
Author(s):  
Akihiro Fujii
Keyword(s):  
Internet Of Things ◽  
Agricultural Sector ◽  
Electronic Device ◽  
Specific Work ◽  
Science And Engineering ◽  
Unique Identifier ◽  
Agricultural Application ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Type Pressure

The Internet of Things (IoT) is a term that describes a system of computing devices, digital machines, objects, animals or people that are interrelated. Each of the interrelated 'things' are given a unique identifier and the ability to transfer data over a network that does not require human-to-human or human-to-computer interaction. Examples of IoT in practice include a human with a heart monitor implant, an animal with a biochip transponder (an electronic device inserted under the skin that gives the animal a unique identification number) and a car that has built-in sensors which can alert the driver about any problems, such as when the type pressure is low. The concept of a network of devices was established as early as 1982, although the term 'Internet of Things' was almost certainly first coined by Kevin Ashton in 1999. Since then, IoT devices have become ubiquitous, certainly in some parts of the world. Although there have been significant developments in the technology associated with IoT, the concept is far from being fully realised. Indeed, the potential for the reach of IoT extends to areas which some would find surprising. Researchers at the Faculty of Science and Engineering, Hosei University in Japan, are exploring using IoT in the agricultural sector, with some specific work on the production of melons. For the advancement of IoT in agriculture, difficult and important issues are implementation of subtle activities into computers procedure. The researchers challenges are going on.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

scholarly journals Secure the IoT Networks as Epidemic Containment Game

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 156
Author(s):  
Juntao Zhu ◽  
Hong Ding ◽  
Yuchen Tao ◽  
Zhen Wang ◽  
Lanping Yu
Keyword(s):  
Heuristic Algorithms ◽  
Heuristic Method ◽  
Exact Algorithms ◽  
Epidemic Threshold ◽  
Solution Quality ◽  
Sis Model ◽  
Linear Programming Formulation ◽  
Iot Devices ◽  
General Graphs ◽  
The Internet Of Things

The spread of a computer virus among the Internet of Things (IoT) devices can be modeled as an Epidemic Containment (EC) game, where each owner decides the strategy, e.g., installing anti-virus software, to maximize his utility against the susceptible-infected-susceptible (SIS) model of the epidemics on graphs. The EC game’s canonical solution concepts are the Minimum/Maximum Nash Equilibria (MinNE/MaxNE). However, computing the exact MinNE/MaxNE is NP-hard, and only several heuristic algorithms are proposed to approximate the MinNE/MaxNE. To calculate the exact MinNE/MaxNE, we provide a thorough analysis of some special graphs and propose scalable and exact algorithms for general graphs. Especially, our contributions are four-fold. First, we analytically give the MinNE/MaxNE for EC on special graphs based on spectral radius. Second, we provide an integer linear programming formulation (ILP) to determine MinNE/MaxNE for the general graphs with the small epidemic threshold. Third, we propose a branch-and-bound (BnB) framework to compute the exact MinNE/MaxNE in the general graphs with several heuristic methods to branch the variables. Fourth, we adopt NetShiled (NetS) method to approximate the MinNE to improve the scalability. Extensive experiments demonstrate that our BnB algorithm can outperform the naive enumeration method in scalability, and the NetS can improve the scalability significantly and outperform the previous heuristic method in solution quality.

scholarly journals RSSI-Based for Device-Free Localization Using Deep Learning Technique

Smart Cities ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 444-455
Author(s):  
Abdul Syafiq Abdull Sukor ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
Norasmadi Abdul Rahim ◽  
Sukhairi Sudin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Statistical Features ◽  
Received Signal Strength Indicator ◽  
Privacy Concerns ◽  
Physical Discomfort ◽  
Learning Technique ◽  
Device Free ◽  
The Internet Of Things

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

Sign in / Sign up

Export Citation Format

Share Document