scholarly journals A Comprehensive Review on Energy Harvesting Integration in IoT Systems from MAC Layer Perspective: Challenges and Opportunities

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3097
Author(s):  
Golshan Famitafreshi ◽  
M. Shahwaiz Afaqui ◽  
Joan Melià-Seguí
Keyword(s):  
Energy Consumption ◽  
Energy Harvesting ◽  
Energy Requirements ◽  
Mac Layer ◽  
Unified Framework ◽  
Essential Information ◽  
Survey Paper ◽  
Hybrid Approaches ◽  
Smart Healthcare ◽  
Iot Devices

The Internet of Things (IoT) is revolutionizing technology in a wide variety of areas, from smart healthcare to smart transportation. Due to the increasing trend in the number of IoT devices and their different levels of energy requirements, one of the significant concerns in IoT implementations is powering up the IoT devices with conventional limited lifetime batteries. One efficient solution to prolong the lifespan of these implementations is to integrate energy harvesting technologies into IoT systems. However, due to the characteristics of the energy harvesting technologies and the different energy requirements of the IoT systems, this integration is a challenging issue. Since Medium Access Control (MAC) layer operations are the most energy-consuming processes in wireless communications, they have undergone different modifications and enhancements in the literature to address this issue. Despite the essential role of the MAC layer to efficiently optimize the energy consumption in IoT systems, there is a gap in the literature to systematically understand the possible MAC layer improvements allowing energy harvesting integration. In this survey paper, we provide a unified framework for different wireless technologies to measure their energy consumption from a MAC operation-based perspective, returning the essential information to select the suitable energy harvesters for different communication technologies within IoT systems. Our analyses show that only 23% of the presented protocols in the literature fulfill Energy Neutral Operation (ENO) condition. Moreover, 48% of them are based on the hybrid approaches, which shows its capability to be adapted to energy harvesting. We expect this survey paper to lead researchers in academia and industry to understand the current state-of-the-art of energy harvesting MAC protocols for IoT and improve the early adoption of these protocols in IoT systems.

scholarly journals Towards Improving TSCH Energy Efficiency: An Analytical Approach to a Practical Implementation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6047
Author(s):  
Marcos A. Sordi ◽  
Ohara K. Rayel ◽  
Guilherme L. Moritz ◽  
João L. Rebelatto
Keyword(s):  
Energy Consumption ◽  
Ieee 802.15.4 ◽  
Practical Implementation ◽  
Mac Layer ◽  
Medium Access ◽  
Energy Consumption Model ◽  
Channel Hopping ◽  
Time Scheduling ◽  
Energy Constrained ◽  
Iot Devices

The IEEE 802.15.4-2015 standard defines a number of Medium Access Control (MAC) layer protocols for low power wireless communications, which are desirable for energy-constrained Internet of Things (IoT) devices. Originally defined in the IEEE 802.15.4e amendment, the Time Slotted Channel Hopping (TSCH) has recently been attracting attention from the research community due to its reduced contention (time scheduling) and robustness against fading (channel hopping). However, it requires a certain level of synchronization between the nodes, which can increase the energy consumption. In this work, we implement the Guard Beacon (GB) strategy, aiming at reducing the guard time usually implemented to compensate for imperfect synchronization. Moreover, besides presenting a realistic energy consumption model for a Contiki Operating System-based TSCH network, we show through analytical and practical results that, without the proposed scheme, the power consumption can be more than 13% higher.

scholarly journals The Smart Meter Challenge: Feasibility of Autonomous Indoor IoT Devices Depending on Its Energy Harvesting Source and IoT Wireless Technology

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7433
Author(s):  
Edgar Saavedra ◽  
Laura Mascaraque ◽  
Gonzalo Calderon ◽  
Guillermo del Campo ◽  
Asuncion Santamaria
Keyword(s):  
Energy Consumption ◽  
Energy Harvesting ◽  
Wireless Technology ◽  
Smart Meters ◽  
Wireless Technologies ◽  
Service Interruption ◽  
Communication Processes ◽  
Qualified Personnel ◽  
Iot Devices ◽  
Application Requirements

Most smart meters are connected and powered by the electric mains, requiring the service interruption and qualified personnel for their installation. Wireless technologies and energy harvesting techniques have been proved as alternatives for communications and power supply, respectively. In this work, we analyse the energy consumption of the most used IoT wireless technologies nowadays: Sigfox, LoRaWAN, NB-IoT, Wi-Fi, BLE. Smart meters’ energy consumption accounts for metering, standby and communication processes. Experimental measurements show that communication consumption may vary upon the specific characteristics of each wireless communication technology—payload, connection establishment, transmission time. Results show that the selection of a specific technology will depend on the application requirements (message payload, metering period) and location constraints (communication range, infrastructure availability). Besides, we compare the performance of the most suitable energy harvesting (EH) techniques for smart meters: photovoltaic (PV), radiofrequency (RF) and magnetic induction (MIEH). Thus, EH technique selection will depend on the availability of each source at the smart meter’s location. The most appropriate combination of IoT wireless technology and EH technique must be selected accordingly to the very use case requirements and constraints.

Improving Energy Efficiency in Internet of Things using Artificial Bee Colony Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
M. Sivaram ◽  
V. Porkodi ◽  
Amin Salih Mohammed ◽  
S. Anbu Karuppusamy
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Artificial Bee Colony ◽  
Trust Model ◽  
Sensor Nodes ◽  
Remote Areas ◽  
Bee Colony ◽  
Important Concern ◽  
Wide Range ◽  
Iot Devices

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities help to reduce the constraint demands of the current security protocols. Aim: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem in IoT and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, energy efficiency in WSNs has been an important concern. Methods: Hence, in this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4798
Author(s):  
Fangni Chen ◽  
Anding Wang ◽  
Yu Zhang ◽  
Zhengwei Ni ◽  
Jingyu Hua
Keyword(s):  
Energy Consumption ◽  
Transmission Rate ◽  
Edge Computing ◽  
Energy Harvest ◽  
Total System ◽  
Mobile Edge Computing ◽  
Weight Factor ◽  
Iot Devices ◽  
System Variables ◽  
The Relationship

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

scholarly journals Performance of falling film plate evaporators in reconstructed multiple-effect evaporation station in sugar factory

2006 ◽  
Vol 10 (4) ◽  
pp. 55-61 ◽  
Author(s):  
Zoltan Zavargo ◽  
Aleksandar Jokic ◽  
Bojana Prodanic ◽  
Jasna Grbic ◽  
Rada Jeftic-Mucibabic
Keyword(s):  
Heat Transfer ◽  
European Union ◽  
Energy Consumption ◽  
General Trend ◽  
Regional Level ◽  
Energy Requirements ◽  
Falling Film ◽  
Evaporation Process ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients

General trend of free trade in regional level as well as in the direction of European Union has motivated sugar factories located in Serbia to invest into technologies that are more efficient in order to make their products more competitive at the markets in Europe. The aim of this work was to evaluate effects of falling film plate evaporators on the energy consumption of evaporation plant, as well as to validate performance of this type of evaporators. It was found that this type of evaporator decreased energy requirements and in the same time evaporation process was more effective due to high values of heat transfer coefficients. .

scholarly journals A New Method for the Calculation of Energy and Power Requirements of Bucket Wheel Excavators

2019 ◽  
Vol 10 (1) ◽  
pp. 15-20
Author(s):  
József András ◽  
József Kovács ◽  
Endre András ◽  
Ildikó Kertész ◽  
Ovidiu Bogdan Tomus
Keyword(s):  
Energy Consumption ◽  
Numerical Method ◽  
Horizontal Plane ◽  
Vertical Plane ◽  
New Method ◽  
Energy Requirements ◽  
Belt Conveyor ◽  
Analytical Formulas ◽  
Power And Energy ◽  
Continuous Working

Abstract The bucket wheel excavator (BWE) is a continuous working rock harvesting device which removes the rock by means of buckets armoured with teeth, mounted on the wheel and which transfers rock on a main hauling system (generally a belt conveyor). The wheel rotates in a vertical plane and swings in the horizontal plane and raised / descended in the vertical plane by a boom. In this paper we propose a graphical-numerical method in order to calculate the power and energy requirements of the main harvesting structure (the bucket wheel) of the BWE. This approach - based on virtual models of the main working units of bucket wheel excavators and their working processes - is more convenient than those based on analytical formulas and simplification hypotheses, and leads to improved operation, reduced energy consumption, increased productivity and optimal use of available actuating power.

scholarly journals Smart Energy Consumption of IoT With Millimeter-Wave Cognitive Radiofor 5G Cellular Network

2021 ◽  
Author(s):  
Dan Ye
Keyword(s):  
Energy Consumption ◽  
Cognitive Radio ◽  
Energy Saving ◽  
Millimeter Wave ◽  
Resource Sharing ◽  
Network Capacity ◽  
Visible Light Communications ◽  
Radio Access ◽  
Data Rates ◽  
Iot Devices

Abstract Millimeter-wave technology is rising as a crucial component for 5G radio access and other emerging ancillary wireless networks including Gb/s device-to-device communication and mobile backhaul. This paper envisions that millimeter-wave cognitive radio in 5G network is a proposed smart energy consumption solution of Internet of Things (IoT) devices. Improving resource efficiency and enhancing data rates, resource sharing is a proposed advantage over millimeter wave cognitive radio in 5G IoT network. IoT Fog collaboration is proposed to apply artificial intelligence techniques to offer important energy-saving services allowing integrated systems to perceive, reason, learn, and act intelligently in intelligent gateway control. Smart energy meters are the current energy-saving utility in the flexible deployment of IoT architecture. NarrowBand IoT (NB-IoT) delivers Low Power Wide Area access (LPWA) to a new generation of connected things in the race to 5G IoT network, reducing energy computation and achieving promising network capacity. The renewable energy strategy is a proposed energy-efficiency solution in IoT network, maximizing the power supply while minimizing power consumption. A novel kind of visible light communications (VLC) is proposed to enable mmWave cognitive radio receiver in 5G IoT network. Simulation results show the proposed solution can reap the benefits of higher data rates, more IoT device connectivity, and lower energy consumption.

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