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Wireless Sensing and Networking for the Internet of Things: 2nd Edition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 8113

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Prof. Dr. Zihuai Lin

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School of Electrical & Information Engineering, the University of Sydney, Camperdown, NSW 2006, Australia
Interests: Internet of Things (IoT); wireless sensor networks; wireless communications; communication theory; information theory
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Special Issue Information

Dear Colleagues,

In recent years, we have been witnessing the exponential proliferation of the Internet of Things (IoT)—networks of physical devices, vehicles, appliances and other items embedded with electronics, software, sensors, actuators, and connectivity that enables these objects to connect and exchange data. Enabling the introduction of highly efficient IoT, wireless sensing and network technologies will reduce the need for traditional processes that must currently be conducted manually, thus freeing up the precious resource of a dwindling working staff to do more meaningful and necessarily human-centered work.

This Special Issue aims to bring together innovative developments in areas related to IoT, wireless sensing, and networking, including but not limited to:

Wireless sensing for IoT;
Joint sensing and wireless communications;
MAC and network layer protocols for wireless sensor networks;
Cross-layer design approaches for wireless sensor networks;
Optimization for energy efficiency for wireless sensor networks;
Optimization in localization and tracking, AI-based indoor positioning;
Wireless artificial intelligence (AI) for IoT;
Industrial IoT (smart grid, healthcare IoT, intelligent transportation systems, etc.);
Wireless energy transfer and ambient backscatter communications;
Short code design for wireless sensor networks;
Distributed source channel network coding for wireless sensor networks.

Prof. Dr. Zihuai Lin
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Related Special Issue

Wireless Sensing and Networking for the Internet of Things in Sensors (19 articles)

Published Papers (5 papers)

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Research

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28 pages, 5219 KiB

Open AccessArticle

An Energy-Efficient LoRa Multi-Hop Protocol through Preamble Sampling for Remote Sensing

by Guus Leenders, Gilles Callebaut, Geoffrey Ottoy, Liesbet Van der Perre and Lieven De Strycker

Sensors 2023, 23(11), 4994; https://doi.org/10.3390/s23114994 - 23 May 2023

Cited by 1 | Viewed by 1711

Abstract

Internet of Things technologies open up new applications for remote monitoring of forests, fields, etc. These networks require autonomous operation: combining ultra-long-range connectivity with low energy consumption. While typical low-power wide-area networks offer long-range characteristics, they fall short in providing coverage for environmental tracking in ultra-remote areas spanning hundreds of square kilometers. This paper presents a multi-hop protocol to extend the sensor’s range, whilst still enabling low-power operation: maximizing sleep time by employing prolonged preamble sampling, and minimizing the transmit energy per actual payload bit through forwarded data aggregation. Real-life experiments, as well as large-scale simulations, prove the capabilities of the proposed multi-hop network protocol. By employing prolonged preamble sampling a node’s lifespan can be increased to up to 4 years when transmitting packages every 6 h, a significant improvement compared to only 2 days when continuously listening for incoming packages. By aggregating forwarded data, a node is able to further reduce its energy consumption by up to 61%. The reliability of the network is proven: 90% of nodes achieve a packet delivery ratio of at least 70%. The employed hardware platform, network protocol stack and simulation framework for optimization are released in open access. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things: 2nd Edition)

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24 pages, 1706 KiB

Open AccessArticle

Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks

by Jain-Shing Liu, Chun-Hung Lin, Yu-Chen Hu and Praveen Kumar Donta

Sensors 2023, 23(8), 3900; https://doi.org/10.3390/s23083900 - 11 Apr 2023

Cited by 2 | Viewed by 1645

Abstract

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things: 2nd Edition)

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26 pages, 782 KiB

Open AccessArticle

Data Acquisition Control for UAV-Enabled Wireless Rechargeable Sensor Networks

by Ikjune Yoon

Sensors 2023, 23(7), 3582; https://doi.org/10.3390/s23073582 - 29 Mar 2023

Viewed by 1317

Abstract

In the realm of Internet of Things (IoT), wireless sensor networks (WSNs) have been the subject of ongoing research into the use of energy harvesting to capture ambient energy, and wireless power transfer (WPT) via a mobile charger to overcome the energy limitations of sensors. Moreover, to mitigate energy imbalance and reduce the number of hops, strategies have been developed to leverage cars or unmanned aerial vehicles (UAVs) as mobile sinks. The primary objective of this work is to increase network lifetime by reducing energy consumption of hotspot nodes and increasing the amount of data acquired from all sensors in an environment that combines the methods mentioned above.To achieve this objective, the proposed method involves developing multiple minimum depth trees (MDTs) for all nodes, considering the energy of the UAV and sensor nodes. Parent nodes prevent their own energy depletion and ensure data transmission without imbalance by adaptively controlling the data sensed at the nodes and their child nodes. Consequently, the energy depletion of nodes in hotspots is prevented, more sensory data is acquired, and balanced data collection from all nodes is achieved. Simulation results demonstrate that the proposed scheme outperforms other state-of-the-art methods in terms of reduced energy depletion, increased network connectivity, and the amount of data collected at the sink node. This scheme will be applied to applications that collect environmental data outdoors, such as climate measurement, to collect data uniformly and increase the lifespan of the network, thereby reducing network maintenance costs while collecting data effectively. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things: 2nd Edition)

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29 pages, 2824 KiB

Open AccessArticle

Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks

by Imane Cheikh, Essaid Sabir, Rachid Aouami, Sébastien Roy and Mohamed Sadik

Sensors 2022, 22(23), 9455; https://doi.org/10.3390/s22239455 - 03 Dec 2022

Cited by 1 | Viewed by 1120

Abstract

The operational and technological structures of radio access networks have undergone tremendous changes in recent years. A displacement of priority from capacity–coverage optimization (to ensure data freshness) has emerged. Multiple radio access technology (multi-RAT) is a solution that addresses the exponential growth of traffic demands, providing degrees of freedom in meeting various performance goals, including energy efficiencies in IoT networks. The purpose of the present study was to investigate the possibility of leveraging multi-RAT to reduce each user’s transmission delay while preserving the requisite quality of service (QoS) and maintaining the freshness of the received information via the age of information (AoI) metric. First, we investigated the coordination between a multi-hop network and a cellular network. Each IoT device served as an information source that generated packets (transmitting them toward the base station) and a relay (for packets generated upstream). We created a queuing system that included the network and MAC layers. We propose a framework comprised of various models and tools for forecasting network performances in terms of the end-to-end delay of ongoing flows and AoI. Finally, to highlight the benefits of our framework, we performed comprehensive simulations. In discussing these numerical results, insights regarding various aspects and metrics (parameter tuning, expected QoS, and performance) are made apparent. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things: 2nd Edition)

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Review

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23 pages, 363 KiB

Open AccessReview

Survey on Multi-Objective Task Allocation Algorithms for IoT Networks

by Dominik Weikert, Christoph Steup and Sanaz Mostaghim

Sensors 2023, 23(1), 142; https://doi.org/10.3390/s23010142 - 23 Dec 2022

Viewed by 1512

Abstract

The Internet of Things (IoT) has been an area of growing research interest for decades. Task allocation is an important problem for the optimized operation of Internet-of-Things networks. This paper provides an overview of recent research in the field of Internet-of-Things task allocation optimization. First, the task allocation problem for the IoT itself is analyzed and divided into distinct sub-problem categories, such as deployment optimization, static or dynamic optimization as well as single- or multi-objective optimization. Following that, the commonly used optimization objectives are explained. Various recent works in the field of task allocation optimization are then summarized and catalogued according to the problem categories. Finally, the paper concludes with a qualitative analysis of the categorized approaches and a description of open problems and highlights promising directions for future research. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things: 2nd Edition)

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