Topical Collection "Energy-efficient Internet of Things (IoT)"

Editor

Assoc. Prof. Mahasweta Sarkar
E-Mail Website
Guest Editor
San Diego State University, San Diego, California, USA 92181
Interests: wireless networks; brain computer interface; smart health; IoT; power efficiency and performance optimizations in different applications of wireless networks; smart MAC protocols for upcoming applications of wireless networks like vehicular networks; heterogeneity in 5G networks

Topical Collection Information

Dear Colleagues,

The Internet of things (IoT) envisions a world of supreme connectivity. With billions of devices connected to each other, IoT opts to create a world of a tremendous amount of data flow, in real-time, with the least amount of energy possible. Such promises of connectivity pave the pathway to innovative technological advances in the areas of smart health, smart homes, vehicular networks, e-commerce, automation, remote system monitoring, and operations. Simultaneously, such tremendous connectivity and data flow gives rise to issues of efficient data management, security, infrastructural architectures, latency management, and, above all, energy efficiency in these umpteen data flow. On the one hand, IoT can impact several critical applications in the energy sector of the world today, like monitoring the responsiveness of large power grids and managing energy resources and distributions across cities and countries. On the other hand, IoT can empower the required support in the form of real-time data analytics, optimizations to the energy distribution schemes, and the mitigation of unprecedented scenarios.

This Special Issue solicits novel work in terms of solutions and techniques for energy efficient IoT. We look forward to creating a forum where researchers in the domain of energy-efficient IoT can share their results, techniques, surveys, analyses, and discussions on energy efficiency in IoT. Topics of interest for this Special Issue are not limited strictly to traditional IoT problems, but also ones that address related fields such as (but not limited to):

  • IoT for smart energy systems (smart grid, smart homes, smart distribution, and consumption of power)
  • Energy efficient protocols for smart data transmission strategies in IoT applications
  • Energy efficient fog computing techniques for IoT applications
  • Energy efficient mobile edge computing algorithms for IoT applications
  • Energy efficient optimization and control schemes for smart energy systems
  • Energy efficient algorithms for cyber security and data privacy in IoT applications
  • Energy efficient co-existence of IoT with other transmission technologies like 5G

Assoc. Prof. Mahasweta Sarkar
Guest Editor

Manuscript Submission Information

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Keywords

  • IoT
  • energy efficiency
  • fog computing
  • mobile edge computing
  • cybersecurity
  • latency
  • resource allocation
  • heterogeneity
  • wireless
  • security

Published Papers (12 papers)

2021

Jump to: 2020, 2019

Article
Energy-Efficient Word-Serial Processor for Field Multiplication and Squaring Suitable for Lightweight Authentication Schemes in RFID-Based IoT Applications
Appl. Sci. 2021, 11(15), 6938; https://doi.org/10.3390/app11156938 - 28 Jul 2021
Viewed by 275
Abstract
Radio-Frequency Identification (RFID) technology is a crucial technology used in many IoT applications such as healthcare, asset tracking, logistics, supply chain management, assembly, manufacturing, and payment systems. Nonetheless, RFID-based IoT applications have many security and privacy issues restricting their use on a large [...] Read more.
Radio-Frequency Identification (RFID) technology is a crucial technology used in many IoT applications such as healthcare, asset tracking, logistics, supply chain management, assembly, manufacturing, and payment systems. Nonetheless, RFID-based IoT applications have many security and privacy issues restricting their use on a large scale. Many authors have proposed lightweight RFID authentication schemes based on Elliptic Curve Cryptography (ECC) with a low-cost implementation to solve these issues. Finite-field multiplication are at the heart of these schemes, and their implementation significantly affects the system’s overall performance. This article presents a formal methodology for developing a word-based serial-in/serial-out semisystolic processor that shares hardware resources for multiplication and squaring operations in GF(2n). The processor concurrently executes both operations and hence reduces the execution time. Furthermore, sharing the hardware resources provides savings in the area and consumed energy. The acquired implementation results for the field size n=409 indicate that the proposed structure achieves a significant reduction in the area–time product and consumed energy over the previously published designs by at least 32.3% and 70%, respectively. The achieved results make the proposed design more suitable to realize cryptographic primitives in resource-constrained RFID devices. Full article
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2020

Jump to: 2021, 2019

Article
Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
Appl. Sci. 2020, 10(18), 6512; https://doi.org/10.3390/app10186512 - 18 Sep 2020
Cited by 2 | Viewed by 875
Abstract
Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many [...] Read more.
Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many real-world applications, such as IEI (IoT of environmental intelligence), the required sensing data usually have limited error tolerance according to the QoS2 (quality of sensor service) or QoD (quality of decision-making) required. However, the bounded-error-pruned sensor data can achieve higher data correlation for better compression without jeopardizing QoS2/QoD. Moreover, the sensing data in widely spread sensors usually have strong temporal and spatial correlations. We thus propose a BESDC (bounded-error-pruned sensor data compression) scheme to achieve better leverage between the bounded error and compression ratio for sensor data. In this paper, our experiments on a sensor network of two-tier tree architecture consider four different environmental datasets including PM 2.5, CO, temperature and seismic wave with different scales of bounded errors. With the bounded errors required by the given IEI applications, our BESDC can reduce the total size of data transmission to minimize both energy consumption in sensor-tier devices and storage of fog-tier servers. The experimental results demonstrate that our BESDC can reduce transmission data by over 55% and save 50% energy consumption when assigning 1% of error tolerance within QoS2/QoD requirement. To the best of our knowledge, the proposed BESDC scheme can help other energy-efficient IoT schemes applying network topologies and routing protocols to further enhance energy-efficient IoT services. Full article
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Article
Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing
Appl. Sci. 2020, 10(17), 6057; https://doi.org/10.3390/app10176057 - 01 Sep 2020
Viewed by 501
Abstract
Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we [...] Read more.
Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In the scheduling stage, we first propose an efficient scheme based on the branch and bound method. In order to reduce complexity, we propose a heuristic algorithm that guarantees users’ deadlines. In the resource provisioning stage, we first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reduce the power consumption and guarantees the user’s delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm. Full article
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Article
Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
Appl. Sci. 2020, 10(14), 4866; https://doi.org/10.3390/app10144866 - 16 Jul 2020
Cited by 1 | Viewed by 580
Abstract
Energy consumption is a critical evaluation index of embedded systems, and it has impacts on battery-life, thermal design, as well as device security and reliability. Since energy is the time integral of power, power consumption should be considered, along with the impact of [...] Read more.
Energy consumption is a critical evaluation index of embedded systems, and it has impacts on battery-life, thermal design, as well as device security and reliability. Since energy is the time integral of power, power consumption should be considered, along with the impact of “time”; thus, we propose two designs of automatic energy consumption measuring platforms utilizing General Purpose Input/Output (GPIO). Using these designs, we developed software and introduced auxiliary hardware for solutions with better timing and synchronization. A series of test sets were designed to verify our designs’ capabilities and accuracy levels. Both of our designs showed an accuracy similar to that of traditional measuring methods, which can satisfy the needs of different occasions. In addition, our designs provide real-time energy consumption data, as well as unattended automated measurements. Full article
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Article
A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
Appl. Sci. 2020, 10(11), 3820; https://doi.org/10.3390/app10113820 - 30 May 2020
Cited by 2 | Viewed by 782
Abstract
Various countries in the world are vigorously developing energy-saving industries and attaching importance to the improvement of household energy efficiency, but it is difficult to evaluate user power consumption characteristics due to insufficient information and large data granularity. It is, however, possible to [...] Read more.
Various countries in the world are vigorously developing energy-saving industries and attaching importance to the improvement of household energy efficiency, but it is difficult to evaluate user power consumption characteristics due to insufficient information and large data granularity. It is, however, possible to evaluate the energy efficiency of household users via non-intrusive load monitoring (NILM). This paper explores the energy efficiency assessment of residential users and proposes a household energy efficiency assessment method based on NILM data. An energy efficiency assessment index of residents is provided by analyzing factors that affect residents’ energy efficiency. This index is clear, operable, and easy to obtain and quantify. Based on NILM information, clustering, and comprehensive evaluation, as well as combining the entropy weight method with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), a user’s energy efficiency can be evaluated and analyzed. Some case studies are provided to verify the validity of the proposed method based on non-intrusive information, to analyze the characteristics and deficiencies of the user’s energy consumption, and to give corresponding energy recommendations. Full article
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Article
Multilevel Task Offloading and Resource Optimization of Edge Computing Networks Considering UAV Relay and Green Energy
Appl. Sci. 2020, 10(7), 2592; https://doi.org/10.3390/app10072592 - 09 Apr 2020
Cited by 7 | Viewed by 732
Abstract
Unmanned aerial vehicle (UAV)-assisted relay mobile edge computing (MEC) network is a prominent concept, where network deployment is flexible and network coverage is wide. In scenarios such as emergency communications and low-cost coverage, optimization of offloading methods and resource utilization are important ways [...] Read more.
Unmanned aerial vehicle (UAV)-assisted relay mobile edge computing (MEC) network is a prominent concept, where network deployment is flexible and network coverage is wide. In scenarios such as emergency communications and low-cost coverage, optimization of offloading methods and resource utilization are important ways to improve system effectiveness due to limited terminal and UAV energy and hardware equipment. A multilevel edge computing network resource optimization model on the basis of UAV fusion that provides relay forwarding and offload services is established by considering the initial energy state of the UAV, the green energy charging function, and the reliability of computing offload. With normalized system utility function maximization as the goal, a Markov decision process algorithm meets the needs of the practical application scene and provides a flexible and effective unloading mode. This algorithm is adopted to solve the optimal offloading mode and the optimal resource utilization scheme. Simulations verify the effectiveness and reliability of the proposed multilevel offloading model. The proposed model can optimize system resource allocation and effectively improve the utility function and user experience of computing offloading systems. Full article
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Article
Delay and Energy Consumption Analysis of Frame Slotted ALOHA variants for Massive Data Collection in Internet-of-Things Scenarios
Appl. Sci. 2020, 10(1), 327; https://doi.org/10.3390/app10010327 - 01 Jan 2020
Cited by 1 | Viewed by 780
Abstract
This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive [...] Read more.
This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirements. Full article
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2019

Jump to: 2021, 2020

Article
The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning
Appl. Sci. 2019, 9(22), 4938; https://doi.org/10.3390/app9224938 - 16 Nov 2019
Cited by 4 | Viewed by 1349
Abstract
The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and [...] Read more.
The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios. Full article
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Article
A Novel Sensing Strategy Based on Energy Detector for Spectrum Sensing
Appl. Sci. 2019, 9(21), 4634; https://doi.org/10.3390/app9214634 - 31 Oct 2019
Cited by 3 | Viewed by 735
Abstract
Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in [...] Read more.
Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in this paper, where an improved tradeoff among detection probability, false-alarm probability and available throughput is obtained based on the energy detector. We provide the optimal sensing performance and exhibit its superiority in theory compared with the classical scheme. Finally, simulations validate the conclusions drawn in this paper. Full article
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Article
A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks
Appl. Sci. 2019, 9(21), 4564; https://doi.org/10.3390/app9214564 - 27 Oct 2019
Cited by 2 | Viewed by 954
Abstract
Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks [...] Read more.
Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density. Full article
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Article
Using a GPU to Accelerate a Longwave Radiative Transfer Model with Efficient CUDA-Based Methods
Appl. Sci. 2019, 9(19), 4039; https://doi.org/10.3390/app9194039 - 27 Sep 2019
Cited by 4 | Viewed by 660
Abstract
Climatic simulations rely heavily on high-performance computing. As one of the atmospheric radiative transfer models, the rapid radiative transfer model for general circulation models (RRTMG) is used to calculate the radiative transfer of electromagnetic radiation through a planetary atmosphere. Radiation physics is one [...] Read more.
Climatic simulations rely heavily on high-performance computing. As one of the atmospheric radiative transfer models, the rapid radiative transfer model for general circulation models (RRTMG) is used to calculate the radiative transfer of electromagnetic radiation through a planetary atmosphere. Radiation physics is one of the most time-consuming physical processes, so the RRTMG presents large-scale and long-term simulation challenges to the development of efficient parallel algorithms that fit well into multicore clusters. This paper presents a method for improving the calculative efficiency of radiation physics, an RRTMG long-wave radiation scheme (RRTMG_LW) that is accelerated on a graphics processing unit (GPU). First, a GPU-based acceleration algorithm with one-dimensional domain decomposition is proposed. Then, a second acceleration algorithm with two-dimensional domain decomposition is presented. After the two algorithms were implemented in Compute Unified Device Architecture (CUDA) Fortran, a GPU version of the RRTMG_LW, namely G-RRTMG_LW, was developed. Results demonstrated that the proposed acceleration algorithms were effective and that the G-RRTMG_LW achieved a significant speedup. In the case without I/O transfer, the 2-D G-RRTMG_LW on one K40 GPU obtained a speed increase of 18.52× over the baseline performance on a single Intel Xeon E5-2680 CPU core. Full article
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Review
Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
Appl. Sci. 2019, 9(14), 2896; https://doi.org/10.3390/app9142896 - 19 Jul 2019
Cited by 6 | Viewed by 1279
Abstract
Wireless sensor networks (WSNs) have become an important and promising technology owing to their wide range of applications in disaster response, battle field surveillance, wildfire monitoring, radioactivity monitoring, etc. In WSNs, routing plays a significant role in delivery latency, energy consumption, and packet [...] Read more.
Wireless sensor networks (WSNs) have become an important and promising technology owing to their wide range of applications in disaster response, battle field surveillance, wildfire monitoring, radioactivity monitoring, etc. In WSNs, routing plays a significant role in delivery latency, energy consumption, and packet delivery ratio. Furthermore, as these applications are used in critical operations with limited irreplaceable batteries, routing protocols are required to be flawless as well as energy efficient. The dynamic environment also requires intelligent and adaptive routing. Game theory is widely used for designing routing protocols in WSNs to achieve not only reduced energy consumption but also increased packet delivery ratio. The core features of efficiently designed game theory-based routing protocols include optimal cluster head selection in hierarchical routing, energy-efficient and delay-aware route discovery, fault-tolerant data delivery, and coalition forming and grouping among nodes for stringent data transfer. In this paper, different routing protocols based on various types of games are extensively reviewed, which have been reported so far for improving energy consumption, delay, route establishment time, packet delivery ratio, and network lifetime. The different game theory-based routing protocols are qualitatively compared with each other in terms of major features, advantages, limitations, and key characteristics. For each protocol, possible applications and future improvements are summarized. Certain important open concerns and challenges are also discussed, along with future research directions. Full article
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