Hybrid Low-Power Wide-Area Mesh Network for IoT Applications

The rapid development of Internet of Things (IoT) has led to more and more devices using ISM frequency bands. Because they are not time synchronized, medium access collisions are unavoidable. The probability of such a collision is usually reasonably low; however, it increases with the number of transmitters competing for the same frequency channel. For this reason, ISM bands’ occupancy is regularly monitored by researchers. This paper presents the results of the measurement campaign during which a selected part of the 868 MHz ISM frequency band was monitored for the presence of transmissions in six locations in various residential areas in Warsaw, Poland. For the purpose of the campaign, a dedicated measurement set-up comprising a software-defined radio (SDR) module was assembled. The measurements results showed that the channel occupancy is in most cases lower than 1% with a maximum observed value of 2%. The paper presents selected characteristics of the detected signals. Additionally, distribution over time of the detected signals was used together with the Monte Carlo simulations to analyze how long idle time blocks are available for new transmitters that could be deployed in the band under testing.

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“…4.3 1 No bursts having properties of intentional transmission detected. 2 Doubtful observation due to low power.…”

Section: Case Study-location Dmentioning

confidence: 99%

Channel Occupancy Measurements in 868 MHz ISM Band in Residential Areas

Kozlowski

Kurek

2021

Sensors

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“…• Tradeoff between privacy and utility when analyzing data from multiple farms, similar to federated computing, used profitably in other fields, e.g., genomics [5]. • Network management for sparsely connected farms using newer networking solutions that are bandwidth-aware and do not require cell towers [6], [7]. • Drones and tractors for data ferrying when needed especially under sparse network connectivity [8], [9].…”

Section: Identified Gaps Motivating Latticementioning

confidence: 99%

“…Integrated network over heterogeneous wireless communication: We have designed an integrated network to enable both delay-sensitive communication and delay-tolerant communication (in an opportunistic manner) over heterogeneous wireless connectivity [7]. We consider both infrastructure-based and ad hoc modes.…”

Section: Edge Computing and Low-power Communication Technologiesmentioning

confidence: 99%

Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

Chaterji

DeLay

Evans

et al. 2021

IEEE Open J. Comput. Soc.

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Digital agriculture, with the incorporation of Internet-of-Things (IoT)-based technologies, presents the ability to observe and control a system at multiple levels (individual, local, regional, and global) and generate tools that allow for improved decision making and higher productivity. Recent advances in IoT hardware, e.g., networks of heterogeneous embedded devices, and software, e.g., lightweight computer vision algorithms and cloud optimization solutions, make it possible to collect data and efficiently process data from diverse sources in a connected (smart) farm. By interconnecting these IoT devices, often across large swaths of farmland, it is possible to collect data from multiple farming and food systems, and at different time scales, including in near real-time (i.e., with delays of only a few tens of seconds). This data can then be used for actionable insights, e.g., precise applications of soil supplements and reduced environmental footprint.Through LATTICE, we present an integrated vision for IoT solutions, data processing, and actionable analytics, with economics and policy considerations for digital agriculture. Our paper starts off with the types of datasets in typical field operations, followed by the lifecycle for the data and storage and fast information-retrieval solutions. It then goes on to describe the most prevalent and promising aspects of machine learning and cloud computing for digital agriculture. IoT devices that are capable of sensing different kinds of information connect with each other in a wireless sensor network setting. These form a rich source for optimizing IoT data collection and subsequent analysis. We discuss what algorithms are proving to be most impactful in this space, e.g., approximate data analytics and on-device/in-network processing. We conclude by discussing analytics for alternative agriculture for generation of biofuels and policy challenges in the implementation of digital agriculture in the wild.

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“…IoT systems are distributed dynamically and incorporate edge, cloud, and fog computing methods based on the allocation of information and computational resources [3]. IoT devices should cooperate with each other [4]. IoT devices communicate with each other through wireless communication systems and transfer information to a centralized system [5].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

et al. 2021

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In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

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Hybrid Low-Power Wide-Area Mesh Network for IoT Applications

Cited by 66 publications

References 14 publications

Channel Occupancy Measurements in 868 MHz ISM Band in Residential Areas

Channel Occupancy Measurements in 868 MHz ISM Band in Residential Areas

Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

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