scholarly journals Resource Allocation for Machine-Type Communication of Energy-Harvesting Devices in Wi-Fi HaLow Networks

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2449
Author(s):  
Dmitry Bankov ◽  
Evgeny Khorov ◽  
Andrey Lyakhov ◽  
Jeroen Famaey
Keyword(s):  
Energy Harvesting ◽  
Data Transmission ◽  
Access Point ◽  
Optimal Number ◽  
Time Interval ◽  
Data Delivery ◽  
Machine Type ◽  
Machine Type Communication ◽  
Energy Harvesting Devices ◽  
The Internet Of Things

The recent Wi-Fi HaLow technology focuses on adopting Wi-Fi for the needs of the Internet of Things. A key feature of Wi-Fi HaLow is the Restricted Access Window (RAW) mechanism that allows an access point to divide the sensors into groups and to assign each group to an exclusively reserved time interval where only the stations of a particular group can transmit. In this work, we study how to optimally configure RAW in a scenario with a high number of energy harvesting sensor devices. For such a scenario, we consider a problem of device grouping and develop a model of data transmission, which takes into account the peculiarities of channel access and the fact that the devices can run out of energy within the allocated intervals. We show how to use the developed model in order to determine the optimal duration of RAW intervals and the optimal number of groups that provide the required probability of data delivery and minimize the amount of consumed channel resources. The numerical results show that the optimal RAW configuration can reduce the amount of consumed channel resources by almost 50%.

scholarly journals Performance Analysis of Wireless Information Surveillance in Machine-Type Communication at Finite Blocklength Regime

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3031
Author(s):  
Dong ◽  
Li ◽  
Yan
Keyword(s):  
Channel Coding ◽  
High Signal ◽  
Machine Type ◽  
Pervasive Sensing ◽  
Security Challenges ◽  
Machine Type Communications ◽  
Communication Devices ◽  
Machine Type Communication ◽  
And Control ◽  
The Internet Of Things

The Internet of Things (IoT) will feature pervasive sensing and control capabilities via the massive deployment of machine-type communication devices in order to greatly improve daily life. However, machine-type communications can be illegally used (e.g., by criminals or terrorists) which is difficult to monitor, and thus presents new security challenges. The information exchanged in machine-type communications is usually transmitted in short packets. Thus, this paper investigates a legitimate surveillance system via proactive eavesdropping at finite blocklength regime. Under the finite blocklength regime, we analyze the channel coding rate of the eavesdropping link and the suspicious link. We find that the legitimate monitor can still eavesdrop the information sent by the suspicious transmitter as the blocklength decreases, even when the eavesdropping is failed under the Shannon capacity regime. Moreover, we define a metric called the effective eavesdropping rate and study the monotonicity. From the analysis of monotonicity, the existence of a maximum effective eavesdropping rate for a moderate or even high signal-to-noise (SNR) is verified. Finally, numerical results are provided and discussed. In the simulation, we also find that the maximum effective eavesdropping rate slowly increases with the blocklength.

REHASH

2021 ◽  
Vol 5 (3) ◽  
pp. 1-42
Author(s):  
Abu Bakar ◽  
Alexander G. Ross ◽  
Kasim Sinan Yildirim ◽  
Josiah Hester
Keyword(s):  
Energy Harvesting ◽  
Activity Recognition ◽  
Learning Activity ◽  
Completion Rates ◽  
Energy Aware ◽  
General Application ◽  
World Energy ◽  
Adaptive Runtime System ◽  
Energy Harvesting Devices ◽  
The Internet Of Things

Battery-free sensing devices harvest energy from their surrounding environment to perform sensing, computation, and communication. This enables previously impossible applications in the Internet-of-Things. A core challenge for these devices is maintaining usefulness despite erratic, random or irregular energy availability; which causes inconsistent execution, loss of service and power failures. Adapting execution (degrading or upgrading) seems promising as a way to stave off power failures, meet deadlines, or increase throughput. However, because of constrained resources and limited local information, it is a challenge to decide when would be the best time to adapt, and how exactly to adapt execution. In this paper, we systematically explore the fundamental mechanisms of energy-aware adaptation, and propose heuristic adaptation as a method for modulating the performance of tasks to enable higher sensor coverage, completion rates, or throughput, depending on the application. We build a task based adaptive runtime system for intermittently powered sensors embodying this concept. We complement this runtime with a user facing simulator that enables programmers to conceptualize the tradeoffs they make when choosing what tasks to adapt, and how, relative to real world energy harvesting environment traces. While we target battery-free, intermittently powered sensors, we see general application to all energy harvesting devices. We explore heuristic adaptation with varied energy harvesting modalities and diverse applications: machine learning, activity recognition, and greenhouse monitoring, and find that the adaptive version of our ML app performs up to 46% more classifications with only a 5% drop in accuracy; the activity recognition app captures 76% more classifications with only nominal down-sampling; and find that heuristic adaptation leads to higher throughput versus non-adaptive in all cases.

scholarly journals Bagging Ensembled Perceptron Classifier for Seamless Mobility System in Heterogeneous Network

2020 ◽  
Vol 9 (4) ◽  
pp. 567-574
Keyword(s):  
Data Transmission ◽  
Heterogeneous Network ◽  
Loss Rate ◽  
Data Communication ◽  
Access Point ◽  
Delivery Rate ◽  
Data Delivery ◽  
Mobile Nodes ◽  
Seamless Mobility ◽  
Handover Process

Wireless mobile devices require a handover decision system to get a seamless connection in a heterogeneous wireless networking environment. The handover process is one of the most significant processes in a cellular network. Few research works have been developed for providing seamless connectivity using different handover techniques. But, controlling data traffic during the process of seamless mobile data connectivity was not solved. So, there is a necessity to introduce a new model to control the traffic and improving the seamless mobility management in heterogeneous network. A new model called Bagging Ensembled Perceptron Classification based Seamless Mobility (BEPC-SM) introduced to achieve higher data delivery rate with minimum packet loss rate and data transmission delay by means of classifying the mobile nodes in heterogeneous network. In BEPC-SM model, randomly considers a number of mobile nodes in the heterogeneous network as input. Then, BEPC-SM model determines signal strength for each mobile node in a heterogeneous network. Bagging Ensembled Perceptron Classification algorithm is used in BEPC-SM model with the aim of accurately classifying all mobile nodes as strong or weak strength node with a lower amount of time consumption. After that, the distance between the weak strength node and the access point in the network is measured. Lastly, BEPC-SM Model selects the nearby access point with maximum bandwidth availability for each weak strength node in the network to perform the handover process. Thus, the performance of seamless data communication in a heterogeneous network is improved in BEPC-SM model. The BEPC-SM model is used in traffic-aware seamless data communication in a heterogeneous network. Simulation evaluation of the BEPC-SM Model is carried out on factors such as data delivery rate, packet loss rate, data transmission delay with respect to a number of data packets. The simulation result depicts that the BEPC-SM Model is able to increases the data delivery rate and also reduces delay when compared to state-of-the-art works.

scholarly journals Review of random multiple access methods for massive machine type communication

2020 ◽  
pp. 54-67
Author(s):  
Nikolay Matveev ◽  
Andrey Turlikov
Keyword(s):  
Conflict Resolution ◽  
Comparative Analysis ◽  
Error Correction ◽  
Multiple Access ◽  
Random Access ◽  
Machine Type ◽  
Access System ◽  
Machine Type Communication ◽  
Multiple Access System ◽  
The Internet Of Things

Introduction: Intensive research is currently underway in the field of data transmission systems for the Internet of Things in relation to various scenarios of Massive Machine Type Communication. The presence of a large number of devices in such systems necessitates the use the methods of random multiple access to a common communication channel. It is proposed in some works to increase the channel utilization efficiency by the use of error correction coding methods for conflict resolution (Coded Random Access). The vast variety of options for using such communication systems has made it impossible to compare algorithms implementing this approach under the same conditions. This is a problem that restrains the development of both the theory and practice of using error correction code methods for conflict resolution. Purpose: Developing a unified approach to the description of random multiple access algorithms; performing, on the base on this approach, a review and comparative analysis of algorithms in which error correction code methods are used for conflict resolution. Results: A model of a random multiple access system is formulated in the form of a set of assumptions that reflect both the features of various scenarios of Massive Machine Type Communication and the main features of random multiple access algorithms, including Coded Random Access approaches. The system models are classified by the following features: 1) a finite or infinite number of subscribers; 2) stable, unstable or metastable systems; 3) systems with retransmissions or without them; 4) systems with losses or without them. For a lossy system, the main characteristics are Throughput (the proportion of successfully delivered messages) and Packet Loss Rate (probability of a message loss). For a lossless system, the basic characteristics are the algorithm speed and the average delay. A systematic review and comparative analysis of Coded Random Access algorithms have been carried out. The result of the comparative analysis is presented in a visual tabular form. Practical relevance: The proposed model of a random multiple access system can be used as a methodological basis for research and development of random multiple access algorithms for both existing and new scenarios of Massive Machine Type Communication. The systematic results of the review allow us to identify the promising areas of research in the field of data transmission systems for the Internet of Things.

scholarly journals Sistem Pemantau Gas di Tempat Pembuangan Sampah Akhir Berbasis Internet of Things

2018 ◽  
Vol 6 (3) ◽  
pp. 100-105
Author(s):  
Fathur Zaini Rachman
Keyword(s):  
Carbon Dioxide ◽  
Social Media ◽  
Internet Of Things ◽  
Environmental Data ◽  
The Internet ◽  
Time Interval ◽  
Data Delivery ◽  
Internet Connection ◽  
Carbon Dioxide Co2 ◽  
The Internet Of Things

This research developed a gas monitoring system in the final waste disposal. The system has implemented the Internet of Things (IoT) using the ESP8266 Wi-Fi module to transmit methane (CH4) and carbon dioxide (CO2) data concentration, as well as temperature and humidity to the ThingSpeak server. Users can monitor and access these environmental data through social media Twitter and websites from anywhere. The fastest data delivery can be obtained with a time interval of 16 seconds on each data packet sent when there is an Internet connection.

Analysis of machine-type communication data transmission by multicasting technology in 5G wireless networks

2019 ◽  
Author(s):  
Vitalii Beschastnyi ◽  
Valeria Savich ◽  
Daria Ostrikova ◽  
Irina Gudkova ◽  
Giuseppe Araniti ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Data Transmission ◽  
Machine Type ◽  
Machine Type Communication

scholarly journals A Reinforcement Learning Approach to Access Management in Wireless Cellular Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jihun Moon ◽  
Yujin Lim
Keyword(s):  
Reinforcement Learning ◽  
Data Transmission ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Collision Rate ◽  
Access Management ◽  
Machine Type ◽  
Access Delay ◽  
Machine Type Communication ◽  
3Rd Generation Partnership Project

In smart city applications, huge numbers of devices need to be connected in an autonomous manner. 3rd Generation Partnership Project (3GPP) specifies that Machine Type Communication (MTC) should be used to handle data transmission among a large number of devices. However, the data transmission rates are highly variable, and this brings about a congestion problem. To tackle this problem, the use of Access Class Barring (ACB) is recommended to restrict the number of access attempts allowed in data transmission by utilizing strategic parameters. In this paper, we model the problem of determining the strategic parameters with a reinforcement learning algorithm. In our model, the system evolves to minimize both the collision rate and the access delay. The experimental results show that our scheme improves system performance in terms of the access success rate, the failure rate, the collision rate, and the access delay.

Massive Machine Type Communication and the Internet of Things

2019 ◽  
pp. 377-439
Author(s):  
Devaki Chandramouli ◽  
Betsy Covell ◽  
Volker Held ◽  
Hannu Hietalahti ◽  
Jürgen Hofmann ◽  
...  
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Machine Type ◽  
Machine Type Communication ◽  
The Internet Of Things
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