scholarly journals Onboard Spectral Analysis for Low-Complexity IoT Devices

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 43027-43045
Author(s):  
Simone Grimaldi ◽  
Lukas Martenvormfelde ◽  
Aamir Mahmood ◽  
Mikael Gidlund
Keyword(s):  
Spectral Analysis ◽  
Low Complexity ◽  
Iot Devices

scholarly journals Adaptive Load Balanced Routing in IOT Networks: A Distributed Learning Approach

2019 ◽  
Vol 3 (1) ◽  
pp. 1-5
Author(s):  
Rzgar Sirwan ◽  
Muzhir Ani
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Distributed Learning ◽  
Low Complexity ◽  
Learning Approach ◽  
Traffic Efficiency ◽  
Data Channel ◽  
Iot Devices ◽  
Balancing Efficiency ◽  
Networked Society

Facilitating large-scale load-efficient Internet of things (IoT) connectivity is a vital step toward realizing the networked society. Although legacy wide-area wireless systems are heavily based on network-side coordination, such centralized methods will become infeasible in the future, by the unbalanced signaling level and the expected increment in the number of IoT devices. In the present work, this problem is represented through self-coordinating for IoT networks and learning from past communications. In this regard, first, we assessed low-complexity distributed learning methods that can be applied to IoT communications. We presented a learning solution then, for adapting devices’ communication parameters to the environment to maximize the reliability and load balancing efficiency in data transmissions. Moreover, we used leveraging instruments from stochastic geometry to assess the behavior of the presented distributed learning solution against centralized coordinations. Ultimately, we analyzed the interplay amongst traffic efficiency, communications’ reliability against interference and noise over data channel, as well as reliability versus adversarial interference over feedback and data channels. The presented learning approach enhanced both reliability and traffic efficiency within IoT communications considerably. By such promising findings obtained via lightweight learning, our solution becomes promising in numerous low-power low-cost IoT uses.

scholarly journals Physical Unclonable Function and Hashing Are All You Need to Mutually Authenticate IoT Devices

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4361 ◽  
Author(s):  
Ahmed Mostafa ◽  
Suk Jin Lee ◽  
Yesem Kurt Peker
Keyword(s):  
Formal Analysis ◽  
Low Complexity ◽  
Memory Storage ◽  
Communication Overhead ◽  
Session Key ◽  
Efficient Manner ◽  
Business Environments ◽  
Authentication Mechanism ◽  
Iot Devices ◽  
Complexity Cost

Internet of Things (IoT) has become the driving force in modern day technology with an increasing and rapid urge to create an intelligent, efficient, and connected world. IoT is used in manufacturing, agriculture, transportation, education, healthcare and many other business environments as well as home automation. Authentication for IoT devices is essential because many of these devices establish communication with servers through public networks. A rigorous lightweight device authentication scheme is needed to secure its physical hardware from cloning or side-channel attacks and accommodate the limited storage and computational power of IoT devices in an efficient manner. In this paper, we introduce a lightweight mutual two-factor authentication mechanism where an IoT device and the server authenticate each other. The proposed mechanism exploits Physical Unclonable Functions (PUFs) and a hashing algorithm with the purpose of achieving a secure authentication and session key agreement between the IoT device and the server. We conduct a type of formal analysis to validate the protocol’s security. We also validate that the proposed authentication mechanism is secure against different types of attack scenarios and highly efficient in terms of memory storage, server capacity, and energy consumption with its low complexity cost and low communication overhead. In this sense, the proposed authentication mechanism is very appealing and suitable for resource-constrained and security-critical environments.

scholarly journals Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks

2021 ◽  
Vol 11 (5) ◽  
pp. 2163
Author(s):  
Yirga Yayeh Munaye ◽  
Rong-Terng Juang ◽  
Hsin-Piao Lin ◽  
Getaneh Berie Tarekegn ◽  
Ding-Bing Lin
Keyword(s):  
Reinforcement Learning ◽  
Resource Management ◽  
System Development ◽  
Real Data ◽  
Low Complexity ◽  
Base Station ◽  
Programming Tool ◽  
Iot Devices ◽  
Experimental Findings ◽  
Python Programming

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users’ resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.

scholarly journals Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5754
Author(s):  
Mariam Galal ◽  
Wai Pang Ng ◽  
Richard Binns ◽  
Ahmed Abd El Aziz
Keyword(s):  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Light Emitting Diode ◽  
Low Complexity ◽  
Light Emitting ◽  
Transmission Distance ◽  
Noise Characteristics ◽  
Data Rates ◽  
Photovoltaic Mode ◽  
Iot Devices

This paper proposes a low-complexity and energy-efficient light emitting diode (LED)-to-LED communication system for Internet of Things (IoT) devices with data rates up to 200 kbps over an error-free transmission distance up to 7 cm. The system is based on off-the-shelf red-green-blue (RGB) LEDs, of which the red sub-LED is employed as photodetector in photovoltaic mode while the green sub-LED is the transmitter. The LED photodetector is characterized in the terms of its noise characteristics and its response to the light intensity. The system performance is then analysed in terms of bandwidth, bit error rate (BER) and the signal to noise ratio (SNR). A matched filter is proposed, which optimises the performance and increases the error-free distance.

scholarly journals About the Development of a High-Speed Simplified Image Codec

2021 ◽  
pp. 61-66
Author(s):  
Yа. Luts ◽  
V. Luts
Keyword(s):  
Computational Complexity ◽  
Image Compression ◽  
High Speed ◽  
Data Transfer ◽  
Low Complexity ◽  
Video Codec ◽  
Efficiency Coefficient ◽  
Compression Algorithms ◽  
Prediction Algorithms ◽  
Iot Devices

In order to develop a high-speed simplified image codec, an analysis of the influence of known image compression algorithms and other parameters on performance was done. The relevance and expediency of developing a high-speed simplified image codec for the Internet of Things in order to increase the level of autonomy of IoT devices, reduce the cost of construction and dissemination of IoT infrastructure were substantiated. The efficiency coefficient of image compression algorithms was introduced, which is determined by the ratio between the computational complexity of the algorithms and their contribution to the final result. Simplification and reduction of the number of algorithms for predicting pixel values ​​were proposed and substantiated, because at this stage a significant number of computational operations is added by the procedure of comparing different prediction algorithms with each other. It is proposed to use only one block integer transformation with fast low complexity algorithms of calculating, which will significantly reduce the complexity of the block transformation stage, including due to the lack of high computational complexity of the algorithm for comparing the quality of block transformations. At the stage of entropy coding, it is also proposed to use simplified algorithms, because the contribution of this stage to the overall result in the general background is quite small, and the computational complexity is high (50 – 70 % of all calculations). A new algorithm for progressive image transfer was proposed - the transfer of a reduced image followed by the transfer of the original image on demand. The considered approaches and algorithms for the development of high-speed simplified image codec can be applied to further development of high-speed simplified video codec. Keywords: computational complexity, fast transforms, computational efficiency, progressive data transfer, intra-prediction algorithms, simplified image codec, IoT.

scholarly journals Power Efficient Random Access for Massive NB-IoT Connectivity

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4944 ◽  
Author(s):  
Mamta Agiwal ◽  
Mukesh Kumar Maheshwari ◽  
Hu Jin
Keyword(s):  
Power Consumption ◽  
Average Power ◽  
Power Saving ◽  
Random Access ◽  
Reducing Power ◽  
Low Complexity ◽  
Power Efficient ◽  
Average Power Consumption ◽  
Channel Conditions ◽  
Iot Devices

Sensors enabled Internet of things (IoT) has become an integral part of the modern, digital and connected ecosystem. Narrowband IoT (NB-IoT) technology is one of its economical versions preferable when low power and resource limited sensors based applications are considered. One of the major characteristics of NB-IoT technology is its offer of reliable coverage enhancement (CE) which is achieved by repeating the transmission of signals. This repeated transmission of the same signal challenges power saving in low complexity NB-IoT devices. Additionally, the NB-IoT devices are expected to suffer from congestion due to simultaneous random access procedures (RAPs) from an enormous number of devices. Multiple RAP reattempts would further reduce the power saving in NB-IoT devices. We propose a novel power efficient RAP (PE-RAP) for reducing power consumption of NB-IoT devices in a highly congested environment. The existing RAP do not differentiate the failures due to poor channel conditions or due to collision. After the RAP failure either due to collision or poor channel, the devices can apply power ramping or can transit to a higher CE level with higher repetition configuration. In the proposed PE-RAP, the NB-IoT devices can re-ascertain the channel conditions after an RAP attempt failure such that the impediments due to poor channel are reduced. The power increments and repetition enhancements are applied only when necessary. We probabilistically obtain the chances of RAP reattempts. Subsequently, we evaluate the average power consumption by devices in different CE levels for different repetition configurations. We validate our analysis by simulation studies.

scholarly journals Joint Optimization for Visual Data Transmission in the Resource Constraint 5G IoT

2021 ◽  
Vol 256 ◽  
pp. 02014
Author(s):  
Jingce Xu ◽  
Xinsheng Zhang ◽  
Jianfei Lu
Keyword(s):  
Data Transmission ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Limited Resource ◽  
Low Complexity ◽  
Resource Constraint ◽  
Visual Data ◽  
Building Management ◽  
Iot Devices ◽  
Single Objective

With the integration of 5G and Internet of Things (IoT), the application of large-scale IoT devices networking is increasingly extensive, such as building management, property maintenance, autonomous vehicles, healthcare, and shopping to tourism. In these scenes, the volume of data transmission is quite large, especially visual data (image, video, et al). However, due to the limited resource of IoT devices, such as battery, power, bandwidth, visual data transmission is complex to optimize, single objective optimization is difficult to insure the optimal latency, throughput and power at the same time. In this paper, we propose a method to jointly optimize the resource allocation of visual data transmission in resource constraint 5G IoT. Instead of single objective optimization, we combine the bandwidth, power consumption and latency into a hybrid model, then propose a low-complexity algorithm to solve this multiple objective optimization problem. The simulation results demonstrate that the proposed method increases the comprehensive utility of visual data transmission in the resource constraint 5G-IoT by 35%-48% compared with existing approaches.

scholarly journals Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4603
Author(s):  
Chiwoo Cho ◽  
Wooyeol Choi ◽  
Taewoon Kim
Keyword(s):  
Decision Making ◽  
Low Power ◽  
Low Complexity ◽  
Sensor Data ◽  
Classification Problems ◽  
Decision Performance ◽  
Control Decision ◽  
Decision Making Models ◽  
Iot Devices ◽  
The Given

Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity.

scholarly journals Energy-Efficient Uplink Resource Units Scheduling for Ultra-Reliable Communications in NB-IoT Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Jia-Ming Liang ◽  
Kun-Ru Wu ◽  
Jen-Jee Chen ◽  
Pei-Yi Liu ◽  
Yu-Chee Tseng
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Low Complexity ◽  
Critical Issue ◽  
Second Phase ◽  
Delay Constraint ◽  
Reliable Communications ◽  
Long Time ◽  
Iot Devices ◽  
Two Phases

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB-IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB-IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra-reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient, ultra-reliable, and low-complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

Sign in / Sign up

Export Citation Format

Share Document