scholarly journals Energy-Efficient Uplink Resource Units Scheduling for Ultra-Reliable Communications in NB-IoT Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Jia-Ming Liang ◽  
Kun-Ru Wu ◽  
Jen-Jee Chen ◽  
Pei-Yi Liu ◽  
Yu-Chee Tseng
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Low Complexity ◽  
Critical Issue ◽  
Second Phase ◽  
Delay Constraint ◽  
Reliable Communications ◽  
Long Time ◽  
Iot Devices ◽  
Two Phases

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB-IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB-IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra-reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient, ultra-reliable, and low-complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

scholarly journals Physics-Based Simulations of Flow and Fire Development Downstream of a Canopy

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 683
Author(s):  
Gilbert Accary ◽  
Duncan Sutherland ◽  
Nicolas Frangieh ◽  
Khalid Moinuddin ◽  
Ibrahim Shamseddine ◽  
...  
Keyword(s):  
Turbulent Flow ◽  
Forest Canopy ◽  
Prescribed Burning ◽  
Second Phase ◽  
Flow Conditions ◽  
Developed Turbulence ◽  
Large Eddy ◽  
Long Time ◽  
Two Phases ◽  
The Impact

The behavior of a grassland fire propagating downstream of a forest canopy has been simulated numerically using the fully physics-based wildfire model FIRESTAR3D. This configuration reproduces quite accurately the situation encountered when a wildfire spreads from a forest to an open grassland, as can be the case in a fuel break or a clearing, or during a prescribed burning operation. One of the objectives of this study was to evaluate the impact of the presence of a canopy upstream of a grassfire, especially the modifications of the local wind conditions before and inside a clearing or a fuel break. The knowledge of this kind of information constitutes a major element in improving the safety conditions of forest managers and firefighters in charge of firefighting or prescribed burning operations in such configurations. Another objective was to study the behavior of the fire under realistic turbulent flow conditions, i.e., flow resulting from the interaction between an atmospheric boundary layer (ABL) with a surrounding canopy. Therefore, the study was divided into two phases. The first phase consisted of generating an ABL/canopy turbulent flow above a pine forest (10 m high, 200 m long) using periodic boundary conditions along the streamwise direction. Large Eddy Simulations (LES) were carried out for a sufficiently long time to achieve a quasi-fully developed turbulence. The second phase consisted of simulating the propagation of a surface fire through a grassland, bordered upstream by a forest section (having the same characteristics used for the first step), while imposing the turbulent flow obtained from the first step as a dynamic inlet condition to the domain. The simulations were carried out for a wind speed that ranged between 1 and 12 m/s; these values have allowed the simulations to cover the two regimes of propagation of surfaces fires, namely plume-dominated and wind-driven fires.

scholarly journals Energy-aware strategy for data forwarding in IoT ecosystem

2020 ◽  
Vol 10 (5) ◽  
pp. 4863 ◽  
Author(s):  
K. Nagarathna
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Selection Process ◽  
Path Selection ◽  
Destination Node ◽  
Data Forwarding ◽  
Energy Aware ◽  
Energy Efficient Routing ◽  
Research Attention ◽  
Iot Devices

The Internet of Things (IoT) is looming technology rapidly attracting many industries and drawing research attention. Although the scale of IoT-applications is very large, the capabilities of the IoT-devices are limited, especially in terms of energy. However, various research works have been done to alleviate these shortcomings, but the schemes introduced in the literature are complex and difficult to implement in practical scenarios. Therefore, considering the energy consumption of heterogeneous nodes in IoT eco-system, a simple energy-efficient routing technique is proposed. The proposed system has also employed an SDN controller that acts as a centralized manager to control and monitor network services, there by restricting the access of selfish nodes to the network. The proposed system constructs an analytical algorithm that provides reliable data transmission operations and controls energy consumption using a strategic mechanism where the path selection process is performed based on the remaining energy of adjacent nodes located in the direction of the destination node. The proposed energy-efficient data forwarding mechanism is compared with the existing AODV routing technique. The simulation result demonstrates that the protocol is superior to AODV in terms of packet delivery rate, throughput, and end-to-end delay.

scholarly journals Energy Optimization in a Multihop Sensor Network for a WBAN Application

2021 ◽  
Vol 25 (1) ◽  
pp. 3-10
Author(s):  
Vishakha Tyagi ◽  
◽  
Sindhu Hak Gupta ◽  
Monica Kaushik ◽  
◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Transmission ◽  
Energy Efficient ◽  
Data Packet ◽  
Packet Size ◽  
Body Area ◽  
Swarm Optimization ◽  
Posture Change ◽  
Long Time ◽  
Efficient Data

Movement and posture change of human body plays a crucial role in energy consumption while data transmission between strategically deployed nodes in wireless body area networks (WBANs). The majority of energy is used in transmission rather than processing of the data. Nodes within body are there for long time and need to be energy efficient so that the network lifetime is increased. In this paper, we propose an energy efficient data transmission for multi-hop network that uses particle swarm optimization (PSO) for optimizing the parameters on which energy consumption relies. An energy efficient data transmission and reception takes place by altering the parameters like node to node distance and packet size of data. The obtained results show a significant reduction of energy consumed by reducing the packet size and keeping the node-to-sink distance a constant value. The total energy consumed per hop per bit length of data packet Emh/L shows 75% optimization. The energy consumed in data transmission per bit length of data E tx /L and the energy consumed for data received per bit length of data packet E rx /L is optimized by approximately 70% and 50% respectively for hope count 2 to 5.

Data Warehousing for Association Mining

2010 ◽  
pp. 592-597
Author(s):  
Yuefeng Li
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Warehousing ◽  
Association Mining ◽  
Second Phase ◽  
Frequent Patterns ◽  
Search Spaces ◽  
Decision Attributes ◽  
Long Time ◽  
Two Phases

With the phenomenal growth of electronic data and information, there are many demands for developments of efficient and effective systems (tools) to address the issue of performing data mining tasks on data warehouses or multidimensional databases. Association rules describe associations between itemsets (i.e., sets of data items) (or granules). Association mining (or called association rule mining) finds interesting or useful association rules in databases, which is the crucial technique for the development of data mining. Association mining can be used in many application areas, for example, the discovery of associations between customers’ locations and shopping behaviours in market basket analysis. Association mining includes two phases. The first phase is called pattern mining that is the discovery of frequent patterns. The second phase is called rule generation that is the discovery of the interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns that also include much noise as well (Pei and Han, 2002). The second phase is also a time consuming activity (Han and Kamber, 2000) and can generate many redundant rules (Zaki, 2004) (Xu and Li, 2007). To reduce search spaces, user constraintbased techniques attempt to find knowledge that meet some sorts of constraints. There are two interesting concepts that have been used in user constraint-based techniques: meta-rules (Han and Kamber, 2000) and granule mining (Li et al., 2006). The aim of this chapter is to present the latest research results about data warehousing techniques that can be used for improving the performance of association mining. The chapter will introduce two important approaches based on user constraint-based techniques. The first approach requests users to inputs their meta-rules that describe their desires for certain data dimensions. It then creates data cubes based these meta-rules and then provides interesting association rules. The second approach firstly requests users to provide condition and decision attributes that used to describe the antecedent and consequence of rules, respectively. It then finds all possible data granules based condition attributes and decision attributes. It also creates a multi-tier structure to store the associations between granules, and association mappings to provide interesting rules.

scholarly journals Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1540 ◽  
Author(s):  
Ramadhani Sinde ◽  
Feroza Begum ◽  
Karoli Njau ◽  
Shubi Kaijage
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Second Phase ◽  
Network Delay ◽  
Duty Cycling ◽  
Hybrid Particle Swarm Optimization

Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E2S-DRL) algorithm in WSN. E2S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E2S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E2S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E2S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods.

scholarly journals EE-ACML: Energy-Efficient Adiabatic CMOS/MTJ Logic for CPA-Resistant IoT Devices

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7651
Author(s):  
Zachary Kahleifeh ◽  
Himanshu Thapliyal
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Energy Savings ◽  
Tunnel Junctions ◽  
Leakage Power ◽  
Side Channel ◽  
Clock Generator ◽  
Side Channel Attacks ◽  
Adiabatic Logic ◽  
Iot Devices

Internet of Things (IoT) devices have strict energy constraints as they often operate on a battery supply. The cryptographic operations within IoT devices consume substantial energy and are vulnerable to a class of hardware attacks known as side-channel attacks. To reduce the energy consumption and defend against side-channel attacks, we propose combining adiabatic logic and Magnetic Tunnel Junctions to form our novel Energy Efficient-Adiabatic CMOS/MTJ Logic (EE-ACML). EE-ACML is shown to be both low energy and secure when compared to existing CMOS/MTJ architectures. EE-ACML reduces dynamic energy consumption with adiabatic logic, while MTJs reduce the leakage power of a circuit. To show practical functionality and energy savings, we designed one round of PRESENT-80 with the proposed EE-ACML integrated with an adiabatic clock generator. The proposed EE-ACML-based PRESENT-80 showed energy savings of 67.24% at 25 MHz and 86.5% at 100 MHz when compared with a previously proposed CMOS/MTJ circuit. Furthermore, we performed a CPA attack on our proposed design, and the key was kept secret.

The Research of MAC Protocol Based on Fire System for Wireless Sensor Networks

2011 ◽  
Vol 383-390 ◽  
pp. 4446-4450
Author(s):  
Qing Hui Wang ◽  
Yong Huan Ji ◽  
Hong Yan Guo
Keyword(s):  
Wireless Sensor Networks ◽  
Computer Simulation ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Duty Cycle ◽  
Energy Efficient ◽  
Mac Protocol ◽  
Critical Issue ◽  
Wireless Sensor ◽  
Transmission Period

Energy efficiency is a critical issue for sensornetwork. In this paper, we propose an energy efficient context adaptive MAC protocol for wireless sensor networks. The existing approaches try to minimize energy consumption by controlling the duty cycle of transmission period. The New-MAC forecast energy according to the Markov chain, then adjusts the duty cycle through the energy size, then adjusts the duty cycle through the energy size. Computer simulation using NS2 reveals that the proposed protocol significantly reduces the energy consumption compared with the existing S-MAC.

Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors

2021 ◽  
Vol 17 (3) ◽  
pp. 1-28
Author(s):  
Yunji Liang ◽  
Xin Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Xiaolong Zheng ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Low Power ◽  
Energy Efficient ◽  
Learning Strategy ◽  
Heterogeneous Data ◽  
Convolutional Network ◽  
Heterogeneous Sensors ◽  
Correlation Learning ◽  
Collaborative Sensing ◽  
Iot Devices

With the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energy-efficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors . Finally, to decrease the sampling frequency of energy-intensive sensors , we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors . To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks.

Green and Energy-Efficient Computing Architecture for E-Learning

2016 ◽  
pp. 252-277
Author(s):  
K. Palanivel ◽  
S. Kuppuswami
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Data Centers ◽  
Critical Issue ◽  
Green Computing ◽  
Cloud Infrastructure ◽  
Computing Architecture ◽  
Energy Efficient Computing ◽  
E Learning

Information and Communication Technology (ICT) is one of the fast growing industries that facilitate many latest services to the users and therefore, the number of users is increasing rapidly. The usage of ICT and its life cycle produce hazardous substances that need to be addressed in efficient and green ways. The adoption of green computing involves many improvements and provide energy-efficiency services for data centers, power management and cloud computing. Cloud computing is a highly scalable and cost-effective infrastructure for running Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. Hence, energy-efficient solutions are required to minimize the impact of Cloud environment. E-learning methodology is an example of Green computing. Thus, it is proposed a Green Cloud Computing Architecture for e-Learning Applications that can lower expenses and reduce energy consumption.

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