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22 pages, 956 KiB

Open AccessArticle

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

by Vassilis Asteriou, Konstantinos Kantelis, Georgia A. Beletsioti, Anastasios Valkanis, Petros Nicopolitidis and Georgios Papadimitriou

Sensors 2024, 24(7), 2222; https://doi.org/10.3390/s24072222 - 30 Mar 2024

Viewed by 387

Abstract

Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with [...] Read more.

Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs’ physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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16 pages, 1317 KiB

Open AccessArticle

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System

by Bin Wu, Nobuo Funabiki, Sujan Chandra Roy, Md. Mahbubur Rahman, Dezheng Kong and Shihao Fang

Sensors 2024, 24(7), 2173; https://doi.org/10.3390/s24072173 - 28 Mar 2024

Viewed by 467

Abstract

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the [...] Read more.

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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16 pages, 777 KiB

Open AccessArticle

Low-Latency Wireless Network Extension for Industrial Internet of Things

by Michael Fletcher, Eric Paulz, Devin Ridge and Alan J. Michaels

Sensors 2024, 24(7), 2113; https://doi.org/10.3390/s24072113 - 26 Mar 2024

Viewed by 671

Abstract

The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical [...] Read more.

The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical traffic arrives rapidly across multiple hops to enable numerous IIoT use cases. IIoT architectures are migrating toward wirelessly connected edges, creating a desire to extend TSN-like functionality to a wireless format. Existing protocols possess inherent challenges to achieving this prioritized low-latency communication, ranging from rigidly scheduled time division transmissions, scalability/jitter of carrier-sense multiple access (CSMA) protocols, and encryption-induced latency. This paper presents a hardware-validated low-latency technique built upon receiver-assigned code division multiple access (RA-CDMA) techniques to implement a secure wireless TSN-like extension suitable for the IIoT. Results from our hardware prototype, constructed on the IntelFPGA Arria 10 platform, show that (sub-)millisecond single-hop latencies can be achieved for each of the available message types, ranging from 12 bits up to 224 bits of payload. By achieving one-way transmission of under 1 ms, a reliable wireless TSN extension with comparable timelines to 802.1Q and/or 5G is achievable and proven in concept through our hardware prototype. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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43 pages, 11786 KiB

Open AccessArticle

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

by Anurag Sinha, Turki Aljrees, Saroj Kumar Pandey, Ankit Kumar, Pallab Banerjee, Biresh Kumar, Kamred Udham Singh, Teekam Singh and Pooja Jha

Sensors 2024, 24(1), 18; https://doi.org/10.3390/s24010018 - 19 Dec 2023

Cited by 1 | Viewed by 1428

Abstract

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy [...] Read more.

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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27 pages, 3962 KiB

Open AccessArticle

Automatic Passenger Counting on the Edge via Unsupervised Clustering

by Giorgio Delzanno, Luca Caputo, Daniele D’Agostino, Daniele Grosso, Abdul Hannan Mustajab, Luca Bixio and Matteo Rulli

Sensors 2023, 23(11), 5210; https://doi.org/10.3390/s23115210 - 30 May 2023

Cited by 1 | Viewed by 1344

Abstract

We present a device- and network-based solution for automatic passnger counting that operates on the edge in real time. The proposed solution consists of a low-cost WiFi scanner device equipped with custom algorithms for dealing with MAC address randomization. Our low-cost scanner is [...] Read more.

We present a device- and network-based solution for automatic passnger counting that operates on the edge in real time. The proposed solution consists of a low-cost WiFi scanner device equipped with custom algorithms for dealing with MAC address randomization. Our low-cost scanner is able to capture and analyze 802.11 probe requests emitted by passengers’ devices such as laptops, smartphones, and tablets. The device is configured with a Python data-processing pipeline that combines data coming from different types of sensors and processes them on the fly. For the analysis task, we have devised a lightweight version of the DBSCAN algorithm. Our software artifact is designed in a modular way in order to accommodate possible extensions of the pipeline, e.g., either additional filters or data sources. Furthermore, we exploit multi-threading and multi-processing for speeding up the entire computation. The proposed solution has been tested with different types of mobile devices, obtaining promising experimental results. In this paper, we present the key ingredients of our edge computing solution. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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28 pages, 1828 KiB

Open AccessArticle

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

by Safuriyawu Ahmed, Frédéric Le Mouël, Nicolas Stouls and Gislain Lipeme Kouyi

Sensors 2023, 23(9), 4298; https://doi.org/10.3390/s23094298 - 26 Apr 2023

Cited by 1 | Viewed by 1708

Abstract

Crude oil leakages and spills (OLS) are some of the problems attributed to pipeline failures in the oil and gas industry’s midstream sector. Consequently, they are monitored via several leakage detection and localisation techniques (LDTs) comprising classical methods and, recently, Internet of Things [...] Read more.

Crude oil leakages and spills (OLS) are some of the problems attributed to pipeline failures in the oil and gas industry’s midstream sector. Consequently, they are monitored via several leakage detection and localisation techniques (LDTs) comprising classical methods and, recently, Internet of Things (IoT)-based systems via wireless sensor networks (WSNs). Although the latter techniques are proven to be more efficient, they are susceptible to other types of failures such as high false alarms or single point of failure (SPOF) due to their centralised implementations. Therefore, in this work, we present a hybrid distributed leakage detection and localisation technique (HyDiLLEch), which combines multiple classical LDTs. The technique is implemented in two versions, a single-hop and a double-hop version. The evaluation of the results is based on the resilience to SPOFs, the accuracy of detection and localisation, and communication efficiency. The results obtained from the placement strategy and the distributed spatial data correlation include increased sensitivity to leakage detection and localisation and the elimination of the SPOF related to the centralised LDTs by increasing the number of node-detecting and localising (NDL) leakages to four and six in the single-hop and double-hop versions, respectively. In addition, the accuracy of leakages is improved from 0 to 32 m in nodes that were physically close to the leakage points while keeping the communication overhead minimal. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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22 pages, 877 KiB

Open AccessArticle

Implicit Overhearing Node-Based Multi-Hop Communication Scheme in IoT LoRa Networks

by Dick Mugerwa, Youngju Nam, Hyunseok Choi, Yongje Shin and Euisin Lee

Sensors 2023, 23(8), 3874; https://doi.org/10.3390/s23083874 - 10 Apr 2023

Cited by 3 | Viewed by 1714

Abstract

Long range (LoRa) is a low-power wide-area technology because it is eminent for robust long-distance, low-bitrate, and low-power communications in the unlicensed sub-GHz spectrum used for the Internet of things (IoT) networks. Recently, several multi-hop LoRa networks have proposed schemes with explicit relay [...] Read more.

Long range (LoRa) is a low-power wide-area technology because it is eminent for robust long-distance, low-bitrate, and low-power communications in the unlicensed sub-GHz spectrum used for the Internet of things (IoT) networks. Recently, several multi-hop LoRa networks have proposed schemes with explicit relay nodes to partially mitigate the path loss and longer transmission time bottlenecks of the conventional single-hop LoRa by focusing more on coverage expansion. However, they do not consider improving the packet delivery success ratio (PDSR) and the packet reduction ratio (PRR) by using the overhearing technique. Thus, this paper proposes an implicit overhearing node-based multi-hop communication (IOMC) scheme in IoT LoRa networks, which exploits implicit relay nodes for performing the overhearing to promote relay operation while satisfying the duty cycle regulation. In IOMC, implicit relay nodes are selected as overhearing nodes (OHs) among end devices with a low spreading factor (SF) in order to improve PDSR and PRR for distant end devices (EDs). A theoretical framework for designing and determining the OH nodes to execute the relay operations was developed with consideration of the LoRaWAN MAC protocol. Simulation results verify that IOMC significantly increases the probability of successful transmission, performs best in high node density, and is more resilient to poor RSSI than the existing schemes. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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25 pages, 796 KiB

Open AccessArticle

Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks

by Thanh-Nam Tran, Thanh-Long Nguyen, Vinh Truong Hoang and Miroslav Voznak

Sensors 2023, 23(4), 2345; https://doi.org/10.3390/s23042345 - 20 Feb 2023

Cited by 3 | Viewed by 1870

Abstract

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To [...] Read more.

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN. Full article

(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)

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