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Wireless Sensor Networks in Smart Homes
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Wireless Sensor Networks in Smart Homes

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

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 25810

Special Issue Editors

Prof. Dr. Ilaria De Munari

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
Interests: design of digital systems; home automation systems; wireless sensors; wearable sensors; embedded systems (microcontrollers and FPGA)
Dr. Valentina Bianchi

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181A, 43124 Parma, Italy
Interests: wearable sensors; wireless sensor networks; digital system design
Special Issues, Collections and Topics in MDPI journals
Dr. Elena Villalba Mora

E-Mail Website
Guest Editor
Ageing Lab. Centre for Biomedical Technology & Department of Languages And Computer Systems And Software Engineering, Universidad Politécnica de Madrid (UPM), Madrid, Spain
Interests: wearable and ambient monitoring of functions as gait speed; functional tests as chair-to-stand tests; health related signals related to chronic conditions as heart failure; diabetes; frailty; parki; IoT and smart homes

Special Issue Information

Dear Colleagues,

Until some years ago, with the term “smart home” was intended a convenient home setup where appliances and home devices were automatically controlled remotely using a mobile or other networked device. Currently, the concept of smart home extends well beyond the simple automation or appliance control and focuses on how different technologies can be used and managed to foster the safety and security and comfort and health of the residents. The Smart Home today is the integration of heterogeneous sensors, interfaces, network technologies, and communication protocols in a holistic view, pursuing new systems designed for users’ well-being and energy sustainability.

In this context, wireless sensors become an attractive research field thanks to their several advantages. They can be easily installed, maintained, and connected in flexible, reconfigurable networks, and they can be adapted to the users’ needs.

Although the scientific advances in this field are remarkable, there are still some drawbacks deserving the attention of the researchers, since they may limit the application areas. Power consumption, data storage, computation capabilities, communication bandwidth, and vulnerability to security attacks are just some of the topics that need further investigation.

This Special Issue aims to publish original papers describing scientific methods and technologies that could improve efficiency, productivity, quality, and reliability of wireless sensors networks. It intends to provide a broad platform for publishing the many advances that have been currently achieved in the area of wireless sensors in smart homes, including new applications and services.

Prof. Dr. Ilaria De Munari
Dr. Valentina Bianchi
Dr. Elena Villalba Mora
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Keywords

  • Intelligent sensors for smart homes
  • Distributed, networked and collaborative systems
  • Cloud computing and big data
  • Wireless communication protocols and implementation
  • Internet of Things
  • Human interaction and usability
  • Energy efficiency in smart homes
  • Innovative wireless sensing and computing systems or prototypes
  • Ambient and active assisted living
  • Anomaly detection in smart home environment
  • Practical deployment and case studies

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Published Papers (6 papers)

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24 pages, 6798 KiB  
Review
Auto-Configuration in Wireless Sensor Networks: A Review
by Ngoc-Thanh Dinh and Younghan Kim
Sensors 2019, 19(19), 4281; https://doi.org/10.3390/s19194281 - 2 Oct 2019
Cited by 7 | Viewed by 4762
Abstract
Wireless sensor network (WSN) studies have been carried out for multiple years. At this stage, many real WSNs have been deployed. Therefore, configuration and updating are critical issues. In this paper, we discuss the issues of configuring and updating a wireless sensor network [...] Read more.
Wireless sensor network (WSN) studies have been carried out for multiple years. At this stage, many real WSNs have been deployed. Therefore, configuration and updating are critical issues. In this paper, we discuss the issues of configuring and updating a wireless sensor network (WSN). Due to a large number of sensor nodes, in addition to the limited resources of each node, manual configuring turns out to be impossible. Therefore, various auto-configuration approaches have been proposed to address the above challenges. In this survey, we present a comprehensive review of auto-configuration mechanisms with the taxonomy of classifications of the existing studies. For each category, we discuss and compare the advantages and disadvantages of related schemes. Lastly, future works are discussed for the remaining issues in this topic. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)
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34 pages, 1014 KiB  
Article
Improving Route Selections in ZigBee Wireless Sensor Networks
by Srikar Meka and Benedito Fonseca, Jr.
Sensors 2020, 20(1), 164; https://doi.org/10.3390/s20010164 - 26 Dec 2019
Cited by 25 | Viewed by 5271
Abstract
The ZigBee wireless communication specifications forecast the use of multihop routes between nodes and define that nodes select their routes based on their costs. The specifications define how to compute a route cost from the probability of successfully transmitting on each of the [...] Read more.
The ZigBee wireless communication specifications forecast the use of multihop routes between nodes and define that nodes select their routes based on their costs. The specifications define how to compute a route cost from the probability of successfully transmitting on each of the routes’ links; and it is recommended that such probabilities be obtained by counting received link status messages or averaging link quality indicators from received packets. In this paper, we study the performance of these two recommended procedures, show that they can lead to degraded route selections, and propose a procedure that can improve route selections without modifications to the ZigBee protocol or frame formats. Our procedure estimates the probability of successful transmission on each link, based on information from the medium access layer during unicast packet transmissions, and includes a modification into how ZigBee nodes treat routing messages internally in order to reduce variations in the link cost estimates. Focusing on a home environment with one or two hops, our simulation results show that, in several scenarios, our procedure performs better than either of the two procedures recommended in the ZigBee specifications. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)
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48 pages, 3940 KiB  
Article
Discovering Human Activities from Binary Data in Smart Homes
by Mohamed Eldib, Wilfried Philips and Hamid Aghajan
Sensors 2020, 20(9), 2513; https://doi.org/10.3390/s20092513 - 29 Apr 2020
Cited by 6 | Viewed by 3327
Abstract
With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist [...] Read more.
With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)
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26 pages, 17082 KiB  
Article
Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
by Eden Teshome, Diana Deac, Steffen Thielemans, Matthias Carlier, Kris Steenhaut, An Braeken and Virgil Dobrota
Sensors 2021, 21(5), 1771; https://doi.org/10.3390/s21051771 - 4 Mar 2021
Cited by 4 | Viewed by 4091
Abstract
Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding [...] Read more.
Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding is seen as a valuable means to reduce traffic. A promising approach to multicast, based on the Routing Protocol for Low Power and Lossy Networks (RPL) is Bidirectional Multicast RPL Forwarding (BMRF). This paper aimed to analyze the performance of BMRF over TSCH. The authors investigated how an adequate TSCH scheduler can help to achieve a requested quality of service (QoS). A theoretical model for the delay and energy consumption of BMRF over TSCH is presented. Next, BMRF’s link layer (LL) unicast and LL broadcast forwarding modes were analyzed on restricted and realistic topologies. On topologies with increased interference, BMRF’s LL broadcast on top of TSCH causes high energy consumption, mainly because of the amount of energy needed to run the schedule, but it significantly improves packet delivery ratio and delay compared to ContikiMAC under the same conditions. In most cases, the LL unicast was found to outperform the LL broadcast, but the latter can be beneficial to certain applications, especially those sensitive to delays. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)
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23 pages, 3904 KiB  
Article
Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine
by Nurfazrina M. Zamry, Anazida Zainal, Murad A. Rassam, Eman H. Alkhammash, Fuad A. Ghaleb and Faisal Saeed
Sensors 2021, 21(23), 8017; https://doi.org/10.3390/s21238017 - 30 Nov 2021
Cited by 14 | Viewed by 2996
Abstract
Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for [...] Read more.
Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)
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19 pages, 2259 KiB  
Article
Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
by Xiao Yan, Cheng Huang, Jianyuan Gan and Xiaobei Wu
Sensors 2022, 22(2), 478; https://doi.org/10.3390/s22020478 - 9 Jan 2022
Cited by 32 | Viewed by 3332
Abstract
Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to [...] Read more.
Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)
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