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Sensors for Green Computing
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Sensors for Green Computing

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

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 33758

Special Issue Editors

Dr. Jaime Lloret

E-Mail Website
Guest Editor
Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, 46022 Valencia, Spain
Interests: network protocols; network algorithms; wireless sensor networks; ad hoc networks; multimedia streaming
Special Issues, Collections and Topics in MDPI journals
Dr. Omprakash Kaiwartya

E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: green computing; Internet of Things; wireless sensor networks; internet of connected vehicles; heterogeneous networking; electronic vehicle charging infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

The connected world is enlarging rapidly due to the growing computing and communication capability in devices or things around us in daily life. The growing digitization towards computing and communication-enabled smart things is diversifying the network heterogeneousness around us. It is pressing towards the smaller and smarter devices enabled via wide range of heterogeneous network access technologies. The sensor enabled smaller devices are energy constrained due to their size oriented limited battery power. Further to this power constraint, the increasing demand of smarter devices enhances computing demand in large volume leading to higher energy consumption. Here, it is worth noting that the frequent battery charging in any devices reduce user interest and friendliness. It is not feasible even with wireless charging technology advancements considering serious safety warning by battery manufacturers towards non-operation of devices during battery charging.        

You are welcome to submit an unpublished original research work related to the theme of “Sensors for Green Computing” in heterogeneous networks.

Prof. Dr. Jaime Lloret Mauri
Dr. Omprakash Kaiwartya
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

  • Energy optimization framework for sensor enabled next generation heterogeneous networks
  • Cooperative scheduling of communication and computing for heterogeneous networks
  • Light-weight computing architectures for next generation heterogeneous networks
  • Interference aware communication models for next generation heterogeneous networks
  • Edge computing oriented decentralized computing architectures for heterogeneous networks
  • Quantity of energy oriented modeling for next generation heterogeneous networks
  • Energy oriented propagation models for next generation heterogeneous networks
  • Energy oriented frameworks for under water sensor enabled heterogeneous networks
  • Access control based energy models for sensor enabled heterogeneous body area networks
  • Energy saving in video oriented traffic data delivery in heterogeneous vehicular networks

Published Papers (6 papers)

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Research

17 pages, 1962 KiB  
Article
Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
by Muhammad Anwar, Abdul Hanan Abdullah, Ayman Altameem, Kashif Naseer Qureshi, Farhan Masud, Muhammad Faheem, Yue Cao and Rupak Kharel
Sensors 2018, 18(10), 3237; https://doi.org/10.3390/s18103237 - 26 Sep 2018
Cited by 74 | Viewed by 5206
Abstract
Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters [...] Read more.
Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments. Full article
(This article belongs to the Special Issue Sensors for Green Computing)
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24 pages, 599 KiB  
Article
A Multi-Objective Demand Response Optimization Model for Scheduling Loads in a Home Energy Management System
by Jaclason M. Veras, Igor Rafael S. Silva, Plácido R. Pinheiro, Ricardo A. L. Rabêlo, Artur Felipe S. Veloso, Fábbio Anderson S. Borges and Joel J. P. C. Rodrigues
Sensors 2018, 18(10), 3207; https://doi.org/10.3390/s18103207 - 22 Sep 2018
Cited by 70 | Viewed by 7877
Abstract
Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim [...] Read more.
Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém—PA, Teresina—PI, Cuiabá—MT, Florianópolis—SC and São Paulo—SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina—PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers. Full article
(This article belongs to the Special Issue Sensors for Green Computing)
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19 pages, 3741 KiB  
Article
A Smart Congestion Control Mechanism for the Green IoT Sensor-Enabled Information-Centric Networking
by Rungrot Sukjaimuk, Quang Ngoc Nguyen and Takuro Sato
Sensors 2018, 18(9), 2889; https://doi.org/10.3390/s18092889 - 31 Aug 2018
Cited by 21 | Viewed by 5968
Abstract
Information-Centric Networking (ICN) is a new Internet architecture design, which is considered as the global-scale Future Internet (FI) paradigm. Though ICN offers considerable benefits over the existing IP-based Internet architecture, its practical deployment in real life still has many challenges, especially in the [...] Read more.
Information-Centric Networking (ICN) is a new Internet architecture design, which is considered as the global-scale Future Internet (FI) paradigm. Though ICN offers considerable benefits over the existing IP-based Internet architecture, its practical deployment in real life still has many challenges, especially in the case of high congestion and limited power in a sensor enabled-network for the Internet of Things (IoT) era. In this paper, we propose a smart congestion control mechanism to diminish the network congestion rate, reduce sensor power consumptions, and enhance the network performance of ICN at the same time to realize a complete green and efficient ICN-based sensor networking model. The proposed network system uses the chunk-by-chunk aggregated packets according to the content popularity to diminish the number of exchanged packets needed for data transmission. We also design the sensor power-based cache management strategy, and an adaptive Markov-based sensor scheduling policy with selective sensing algorithm to further maximize power savings for the sensors. The evaluation results using ndnSIM (a widely-used ICN simulator) show that the proposed model can provide higher network performance efficiency with lower energy consumption for the future Internet by achieving higher throughput with higher cache hit rate and lower Interest packet drop rate as we increase the number of IoT sensors in ICN. Full article
(This article belongs to the Special Issue Sensors for Green Computing)
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17 pages, 5101 KiB  
Article
Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks
by Laura García, Lorena Parra, Jose M. Jimenez and Jaime Lloret
Sensors 2018, 18(9), 2822; https://doi.org/10.3390/s18092822 - 27 Aug 2018
Cited by 9 | Viewed by 4175
Abstract
Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many [...] Read more.
Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many studies have been performed on wearable devices that monitor certain aspects of the health of people, especially for specific diseases. In this paper, we propose a non-invasive low-cost and low-energy physical wellbeing monitoring system that provides a wellness score based on the obtained data. We present the architecture of the system and the disposition of the sensors on the sock. The algorithm of the system is presented as well. The wellness threshold evaluation module allows determining if the monitored parameter is within healthy ranges. The message forwarding module allows decreasing the energy consumption of the system by detecting the presence of alerts or changes in the data. Finally, a simulation was performed in order to determine the energy consumption of the system. Results show that our algorithm allows saving 44.9% of the initial energy in 10,000 min for healthy people. Full article
(This article belongs to the Special Issue Sensors for Green Computing)
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17 pages, 3108 KiB  
Article
Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
by Sudeep Varshney, Chiranjeev Kumar, Abhishek Swaroop, Ashish Khanna, Deepak Gupta, Joel J. P. C. Rodrigues, Plácido R. Pinheiro and Victor Hugo C. De Albuquerque
Sensors 2018, 18(8), 2618; https://doi.org/10.3390/s18082618 - 10 Aug 2018
Cited by 26 | Viewed by 3752
Abstract
The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring [...] Read more.
The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all the three parameters. The most striking feature of the proposed approach is that optimization is more effective when the length of the pipeline is more as far as end-to-end delay is concerned. The lifetime of the network is significantly improved using the proposed approach, especially when the length of the pipeline is of medium size, which makes the proposed scheme suitable for energy efficient buildings. Full article
(This article belongs to the Special Issue Sensors for Green Computing)
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11 pages, 1294 KiB  
Article
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier
by Hesham El-Sayed, Sharmi Sankar, Yousef-Awwad Daraghmi, Prayag Tiwari, Ekarat Rattagan, Manoranjan Mohanty, Deepak Puthal and Mukesh Prasad
Sensors 2018, 18(6), 1696; https://doi.org/10.3390/s18061696 - 24 May 2018
Cited by 16 | Viewed by 5239
Abstract
Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, [...] Read more.
Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy. Full article
(This article belongs to the Special Issue Sensors for Green Computing)
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