scholarly journals Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

2020 ◽  
Vol 10 (18) ◽  
pp. 6512
Author(s):  
Ray-I. Chang ◽  
Yu-Hsien Chu ◽  
Lien-Chen Wei ◽  
Chia-Hui Wang
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Efficient ◽  
Error Tolerance ◽  
Sensor Data ◽  
Compression Scheme ◽  
Total Size ◽  
Sensing Data ◽  
Bounded Error

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.

scholarly journals Fireflies Ant Optimized Reliable Quality Awareness Energy Efficient Routing Protocol for Managing Quality of Medical Data in Wireless Body Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 5007-5012
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Medical Data ◽  
Energy Efficient Routing ◽  
Packet Delivery ◽  
End To End Delay ◽  
End To End ◽  
Quality Awareness

The wireless body area network is one of effective wearable devices that have been used in medical applications for collecting patient information to providing the treatment incorrect time for avoiding seriousness. The collected data’s such as blood pressure, air flow, temperature, electromagnetic information is transmitted to the health care center via the wireless technology, which reduces the difficulties also helps to provide the immediate treatment. During the information transmission, the main issues are Quality of Service (QoS), low packet delivery, high energy consumption and end to end delay. So, in this paper introduces the Fireflies Ant Optimized, Reliable Quality Awareness, Energy Efficient Routing Protocol ((FAORQEER) for maintaining the quality of the recorded medical data. The network examines the optimal path by using the characteristics of fireflies and the network life time and energy of the network is managed by introducing an energy efficient method. The process then evaluates efficiency with test results about energy consumption, packet delivery ratio, end to end delay and QoS metric associated constraints.

scholarly journals WSN Clustering Based on EECI (Energy Efficient Clustering using Interconnection) Method

2019 ◽  
Vol 9 (1) ◽  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Data ◽  
Main Technique ◽  
Energy Efficient Clustering ◽  
Selection Of

in WSN, clustering gives an effective way to enhance the network lifetime. Moreover It has been observed that the clustering algorithm utilizes the two main technique first is selection of cluster head and cycling it periodically in order to distribute the energy among the clusters and this in terms increases the lifetime of network. Another challenge comes with this is minimize the energy consumption. In past several algorithm has been proposed to increase the lifetime of the network and energy consumption, however these methodologies lacks from efficiency. In this paper, we have proposed a methodologies named as EE-CI (Energy Efficient Clustering using Interconnection), along with the random updation. Here the networks are parted into different clusters, the cluster updation are done based on the CHC scheme. Moreover, in proposed methodology cluster updation and data sample is determined through the change in sensor data. Here we propose a method for sampling sensor and CHC for selecting the cluster head to balance the energy and improvise the energy efficiency. Moreover, the proposed methodology is evaluated and the result is demonstrated by considering the Leach as existing methodology, experiments results shows that the proposed methodology outperforms the existing methodology.

scholarly journals Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4273 ◽  
Author(s):  
Jianlin Liu ◽  
Fenxiong Chen ◽  
Dianhong Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Compression Ratio ◽  
Sensor Nodes ◽  
Learning Ability ◽  
Wireless Sensor ◽  
Compression Efficiency ◽  
Sensing Data

Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 °C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability.

scholarly journals E2-MACH: Energy efficient multi-attribute based clustering scheme for energy harvesting wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096804
Author(s):  
Inam Ul Haq ◽  
Qaisar Javaid ◽  
Zahid Ullah ◽  
Zafar Zaheer ◽  
Mohsin Raza ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Internet Of Things ◽  
Packet Loss ◽  
Energy Efficient ◽  
Cluster Head ◽  
Wireless Sensor

Internet of things have emerged enough due to its applications in a wide range of fields such as governance, industry, healthcare, and smart environments (home, smart, cities, and so on). Internet of things–based networks connect smart devices ubiquitously. In such scenario, the role of wireless sensor networks becomes vital in order to enhance the ubiquity of the Internet of things devices with lower cost and easy deployment. The sensor nodes are limited in terms of energy storage, processing, and data storage capabilities, while their radio frequencies are very sensitive to noise and interference. These factors consequently threaten the energy consumption, lifetime, and throughput of network. One way to cope with energy consumption issue is energy harvesting techniques used in wireless sensor network–based Internet of things. However, some recent studies addressed the problems of clustering and routing in energy harvesting wireless sensor networks which either concentrate on energy efficiency or quality of service. There is a need of an adequate approach that can perform efficiently in terms of energy utilization as well as to ensure the quality of service. In this article, a novel protocol named energy-efficient multi-attribute-based clustering scheme (E2-MACH) is proposed which addresses the energy efficiency and communication reliability. It uses selection criteria of reliable cluster head based on a weighted function defined by multiple attributes such as link statistics, neighborhood density, current residual energy, and the rate of energy harvesting of nodes. The consideration of such parameters in cluster head selection helps to preserve the node’s energy and reduce its consumption by sending data over links possessing better signal-to-noise ratio and hence ensure minimum packet loss. The minimized packet loss ratio contributes toward enhanced network throughput, energy consumption, and lifetime with better service availability for Internet of things applications. A set of experiments using network simulator 2 revealed that our proposed approach outperforms the state-of-the-art low-energy adaptive clustering hierarchy and other recent protocols in terms of first-node death, overall energy consumption, and network throughput.

scholarly journals CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3445 ◽  
Author(s):  
Jianlin Liu ◽  
Fenxiong Chen ◽  
Jun Yan ◽  
Dianhong Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Wireless Sensor ◽  
Reconstruction Accuracy ◽  
Sensing Data ◽  
Proposed Model

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.

scholarly journals IWSNs with On-Sensor Data Processing for Energy Efficient Machine Fault Diagnosis

2019 ◽  
Vol 15 (08) ◽  
pp. 42
Author(s):  
Liqun Hou ◽  
Junteng Hao ◽  
Yongguang Ma ◽  
Neil Bergmann
Keyword(s):  
Energy Consumption ◽  
Fault Diagnosis ◽  
Data Processing ◽  
Energy Efficient ◽  
Sensor Data ◽  
Support Vector ◽  
Font Size ◽  
Machine Fault Diagnosis ◽  
Transmission Data ◽  
Machine Fault

<span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">Machine fault diagnosis systems need to collect and transmit dynamic signals, like vibration and current, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. Large amounts of transmission data will </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">increase the energy consumption and </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">shorten the lifetime of energy-constrained IWSN node</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s as well</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">.</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">To address th</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> tension</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> when implementing machine fault diagnosis applications in </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">IWSNs</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">, this paper proposes a</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">n</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">energy efficient </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.</span>

scholarly journals An Energy-Efficient Secure Forwarding Scheme for QoS Guarantee in Wireless Sensor Networks

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1418
Author(s):  
Dongwan Kim ◽  
Jaekeun Yun ◽  
Daehee Kim
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Qos Guarantee ◽  
End To End ◽  
Qos Requirement

Many wireless sensor network (WSN) applications require both security and quality-of-service (QoS) to be guaranteed simultaneously. However, ensuring both security and QoS is not trivial in WSNs since security normally has bad impact on QoS. In this paper, we propose an energy-efficient secure forwarding method that minimizes energy consumption while meeting both security and QoS requirements at the same time. To accomplish this goal, we alternatively use hop-by-hop security for conserving energy through data aggregation and end-to-end security to satisfy the QoS requirement. We further analyze why hop-by-hop security with data aggregation provide better energy efficiency than end-to-end security without aggregation in detail. The simulation result shows that our proposed method outperforms other scheme in terms of energy efficiency while meeting both QoS and security.

scholarly journals Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2523 ◽  
Author(s):  
Lin ◽  
Liao ◽  
Leu
Keyword(s):  
Data Compression ◽  
Linear Approximation ◽  
Data Centers ◽  
Piecewise Linear ◽  
Sensor Data ◽  
Piecewise Linear Approximation ◽  
Smart Devices ◽  
Bounded Error ◽  
Real World Datasets ◽  
Lower Transmission

Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restriction on the position of line segment endpoints. Swing-RR (Resolution Reduction) is then proposed. It has O(1) complexity in both space and time per data record. In other words, Swing-RR is suitable for compressing sensor data, particularly when the volume of the data is huge. Our experiment results on real world datasets show that the size of compressed data is significantly reduced. The energy consumed follows. When using minimal resolution, Swing-RR has achieved the best compression ratios for all tested datasets. Consequently, fewer bits are transmitted through networks and less disk space is required to store the data in data centers, thus consuming less data transmission and storage power.

scholarly journals Survey on Energy Efficient Routing Issues in IOMT

2019 ◽  
pp. 112-117
Author(s):  
Varun C R ◽  
Satish Hakkalli ◽  
Pavankumar Naik
Keyword(s):  
Routing Protocol ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Link Quality ◽  
Sensor Data ◽  
Traffic Patterns ◽  
Energy Aware ◽  
Energy Efficient Routing ◽  
Network Topologies

IOT is going to offer large number of applications in various environments for improving the quality of our lives. Routing issues become more and more challenging for low-power and lossy radio-links, multi-hop mesh topologies, the battery supplied nodes and frequently changed network topologies. So that IOT routing protocols has some challenges like traffic Patterns, Energy efficiency, Scalability, Mobility Energy-aware metrics for routing protocol in IOT are node energy, throughput, latency, link quality. The multimedia transmission in IOT depends on a routing protocol to determine stable and resource efficient path, and to provide varying levels of QoS/QoE based on different requirements. The Quality- of-Service (QoS)/Quality-of- experience (QoE ) guarantee for multimedia in internet of things . The current research and development activities have been restricted to scalar sensor data based IOT systems and overlooked the challenges of provisioning multimedia devices over IOT.

scholarly journals Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 89
Author(s):  
Florian Grützmacher ◽  
Albert Hein ◽  
Thomas Kirste ◽  
Christian Haubelt
Keyword(s):  
Energy Consumption ◽  
Activity Recognition ◽  
Human Activity ◽  
Energy Efficient ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
System Level ◽  
Estimation Methods ◽  
Level Energy ◽  
Wearable Sensor

The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

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