scholarly journals Ping-Pong Free Advanced and Energy Efficient Sensor Relocation for IoT-Sensory Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5654
Author(s):  
Moonseong Kim ◽  
Sooyeon Park ◽  
Woochan Lee
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Energy Efficient ◽  
Essential Role ◽  
Sensor Nodes ◽  
Ping Pong ◽  
Iot Devices ◽  
Artificial Network ◽  
Specific Cluster ◽  
Big Data Technology

With the growing interest in big data technology, mobile IoT devices play an essential role in data collection. Generally, IoT sensor nodes are randomly distributed to areas where data cannot be easily collected. Subsequently, when data collection is impossible (i.e., sensing holes occurrence situation) due to improper placement of sensors or energy exhaustion of sensors, the sensors should be relocated. The cluster header in the sensing hole sends requests to neighboring cluster headers for the sensors to be relocated. However, it can be possible that sensors in the specific cluster zones near the sensing hole are continuously requested to move. With this knowledge, there can be a ping-pong problem, where the cluster headers in the neighboring sensing holes repeatedly request the movement of the sensors in the counterpart sensing hole. In this paper, we first proposed the near-uniform selection and movement scheme of the sensors to be relocated. By this scheme, the energy consumption of the sensors can be equalized, and the sensing capability can be extended. Thus the network lifetime can be extended. Next, the proposed relocation protocol resolves a ping-pong problem using queues with request scheduling. Another crucial contribution of this paper is that performance was analyzed using the fully-customed OMNeT++ simulator to reflect actual environmental conditions, not under over-simplified artificial network conditions. The proposed relocation protocol demonstrates a uniform and energy-efficient movement with ping-pong free capability.

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

Secure Energy Efficient Data Collection Scheme Over Wireless Multimedia Sensor Networks

2016 ◽  
Vol 13 (10) ◽  
pp. 7375-7384
Author(s):  
Hyunsung Kim ◽  
Sung Woon Lee
Keyword(s):  
Energy Efficiency ◽  
Sensor Networks ◽  
Data Collection ◽  
Energy Efficient ◽  
Wireless Multimedia Sensor Networks ◽  
Sensor Nodes ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Efficient Data ◽  
Efficient Data Collection

A secure data collection in wireless multimedia sensor networks (WMSNs) has given attention to one of security issues. WMSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. For the energy efficiency, WMSNs adopt mobile sinks to collect data from sensor nodes. Thus, how to gather data securely and efficiently is an important issue WMSNs. In this paper, we propose a secure energy efficient data collection scheme over WMSNs, which are based on Bilinear pairing and symmetric key cryptosystem. First of all, we devise a security model based on a hierarchical key structure for the security mechanisms, authentication, key agreement, confidentiality, and integrity. Based on the model, we propose a secure energy efficient data collection scheme, which could establish secure session in one round. The proposed scheme could efficiently remedy security and efficiency problems in the previous data collection schemes over WMSNs. It has only about 18% of overhead for the security but also has energy efficiency compared with the other related schemes.

scholarly journals Efficient Data Collection using Mobile Sink Scheme in IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1360-1371
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Collection ◽  
Large Scale ◽  
Performance Metrics ◽  
Route Planning ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Energy Hole

Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

scholarly journals Monitoring Passive Wireless Devices

2021 ◽  
Author(s):  
Zohar Naor
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Data Gathering ◽  
Geographic Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Devices ◽  
Iot Devices ◽  
Passive Rfid

Abstract This study suggests using a user-initiated detecting and data gathering from power-limited and even passive wireless devices, such as passive RFID tags, wireless sensor networks (WSNs), and Internet of Things (IoT) devices, that either power limitation or poor cellular coverage prevents them from communicating directly with wireless networks. While previous studies focused on sensors that continuously transmit their data, the focus of this study is on passive devices. The key idea is that instead of receiving the data transmitted by the sensor nodes, an external device (a reader), such as an unnamed aerial vehicle (UAV), or a smartphone is used to detect IoT devices and read the data stored in the sensor nodes, and then to deliver it to the cloud, in which it is stored and processed. While previous studies on UAV-aided data collection from WSNs focused on the UAV path planning, the focus of this study is on the rate at which the passive sensor nodes should be polled. That is, to find the minimal monitoring rate that still guarantees accurate and reliable data collection. The proposed scheme enables us to deploy wireless sensor networks over a large geographic area (e.g., for agricultural applications), in which the cellular coverage is very poor if any. Furthermore, the usage of initiated data collection can enable the deployment of passive WSNs. Thus, can significantly reduce both the operational cost, as well as the deployment cost, of the WSN.

scholarly journals SISTEM PENGELOLAAN KEARSIPAN Google MELALUI BIG DATA

2020 ◽  
Vol 15 (2) ◽  
pp. 21-20
Author(s):  
Aldha Shafrielda Sihab ◽  
Anugerah Pagiyan Nurfajar
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Secondary Data ◽  
Data Sources ◽  
The World ◽  
Library Studies ◽  
Secondary Data Sources ◽  
Learning Machine ◽  
Crucial Part ◽  
Big Data Technology

Big data is an newest trend that embraces the world of technology and business. It's a data collection so large and complex that it no longer allows it to be managed with traditional software tools. One of the world's companies moving in big data technology is Google.Inc. This company maintains and districts data for various purposes, so its presence is urgently needed. As the development of data in Google has been a crucial part of the digital age, resulting in several breakthroughs. First Google data can hold files effectively as well as easily be accessed by people. Both big data can easily call back specifically through Google's learning machine. The study was conducted using qualitative diskirtive methods with secondary data sources through library studies.

scholarly journals MCEEP-BDA: Multilevel Clustering Based -Energy Efficient Privacy-Preserving Big Data Aggregation in Large-Scale Wsn

2019 ◽  
Vol 9 (1) ◽  
pp. 6779-6785
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Large Scale ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Communication Overhead

In current scenario, the Big Data processing that includes data storage, aggregation, transmission and evaluation has attained more attraction from researchers, since there is an enormous data produced by the sensing nodes of large-scale Wireless Sensor Networks (WSNs). Concerning the energy efficiency and the privacy conservation needs of WSNs in big data aggregation and processing, this paper develops a novel model called Multilevel Clustering based- Energy Efficient Privacy-preserving Big Data Aggregation (MCEEP-BDA). Initially, based on the pre-defined structure of gradient topology, the sensor nodes are framed into clusters. Further, the sensed information collected from each sensor node is altered with respect to the privacy preserving model obtained from their corresponding sinks. The Energy model has been defined for determining the efficient energy consumption in the overall process of big data aggregation in WSN. Moreover, Cluster_head Rotation process has been incorporated for effectively reducing the communication overhead and computational cost. Additionally, algorithm has been framed for Least BDA Tree for aggregating the big sensor data through the selected cluster heads effectively. The simulation results show that the developed MCEEP-BDA model is more scalable and energy efficient. And, it shows that the Big Data Aggregation (BDA) has been performed here with reduced resource utilization and secure manner by the privacy preserving model, further satisfying the security concerns of the developing application-oriented needs.

An Energy-Efficient Model for Opportunistic Data Collection in IoV-Enabled SC Waste Management

2021 ◽  
pp. 1-19
Author(s):  
Gerald K. Ijemaru ◽  
Ericmoore T. Ngharamike ◽  
Emmanuel U. Oleka ◽  
Augustine O. Nwajana
Keyword(s):  
Data Collection ◽  
Waste Management ◽  
Energy Efficient ◽  
Vehicular Networks ◽  
Access Point ◽  
High Energy ◽  
Sensor Nodes ◽  
Management Scenarios ◽  
Mobile Data Collectors ◽  
High Energy Consumption

Recent advancements in technological research have seen the use of mobile data collectors (MDCs) or data MULEs for wireless sensor network (WSN) applications. In the context of smart city (SC) waste management scenarios, vehicular networks or the internet of Vehicles (IoV) can be exploited as MDCs or data MULEs for data collection and transmission purposes from the sparsely distributed smart sensors that are attached to the smart bins to an access point or sink node and further deployed for waste management operations. A major challenge with the traditional methods of data collection using static sink nodes is the high energy consumption of the sensor-nodes. The use of MDCs has been well studied and shown to be energy efficient. To the best of the authors' knowledge, this scheme has not been exploited for waste management operations in a SC. Compared to the centralized schemes, the data MULE scheme presents several advantages for data collection in WSN applications. This chapter proposes an energy-efficient model for opportunistic data collection in IoV-enabled SC waste management operations.

scholarly journals Open Sensor Manager for IIoT

2020 ◽  
Vol 9 (2) ◽  
pp. 30 ◽  
Author(s):  
Riku Ala-Laurinaho ◽  
Juuso Autiosalo ◽  
Kari Tammi
Keyword(s):  
Data Collection ◽  
Application Programming Interface ◽  
Sensor Nodes ◽  
Authentication Code ◽  
Representational State Transfer ◽  
Software Updating ◽  
Additional Data Collection ◽  
Iot Devices ◽  
Rest Api ◽  
User Friendly

Data collection in an industrial environment enables several benefits: processes and machinery can be monitored; the performance can be optimized; and the machinery can be proactively maintained. To collect data from machines or production lines, numerous sensors are required, which necessitates a management system. The management of constrained IoT devices such as sensor nodes is extensively studied. However, the previous studies focused only on the remote software updating or configuration of sensor nodes. This paper presents a holistic Open Sensor Manager (OSEMA), which addresses also generating software for different sensor models based on the configuration. In addition, it offers a user-friendly web interface, as well as a REST API (Representational State Transfer Application Programming Interface) for the management. The manager is built with the Django web framework, and sensor nodes rely on ESP32-based microcontrollers. OSEMA enables secure remote software updates of sensor nodes via encryption and hash-based message authentication code. The collected data can be transmitted using the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT). The use of OSEMA is demonstrated in an industrial domain with applications estimating the usage roughness of an overhead crane and tracking its location. OSEMA enables retrofitting different sensors to existing machinery and processes, allowing additional data collection.

scholarly journals A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2024
Author(s):  
Seyed Ahmad Soleymani ◽  
Shidrokh Goudarzi ◽  
Nazri Kama ◽  
Saiful Adli Ismail ◽  
Mazlan Ali ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Data Collection ◽  
Data Transmission ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Sampling ◽  
Efficient Data ◽  
Efficient Data Collection

Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions.

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