Reducing Energy Consumption by Data Aggregation in M2M Networks

Hydro-, Aeolian and Solar energy show significant promise in helping to reduce pollutants and greenhouse gases, which is a primary focus in today's sustainable development culture. To enjoy the power of them, the human society needs solutions to transform the energies mentioned above into electric energy, at specific reliability, efficiency and sustainability parameters. Maintaining this kind of parameters implies, among other things, the careful and permanent monitoring of equipment. The monitoring process implies remote monitoring, as we are talking about preserving natural resources. The equipment is mostly situated in the middle of nature, covering large areas mostly outside of populated locations. In their great majority installations for wind and solar energy have been designed and manufactured relatively recently, which makes them contain systems of tele-monitoring that are designed and included by default, as a part of great importance to the entire investment. The situation is different in the case of hydroelectric plants, which have a tradition of over 130 years and have been and are still built to this day. Renewable energy sources are being increasingly used and need to be constantly monitored for optimizing the power grid. Unfortunately, such micro power plants are located in difficult to access remote locations where often only satellite or sparse GSM radio signals are available. In this paper we study the way how to process big data gathered by a & George Suciu decentralized cloud system, based on general systems and remote telemetry units (RTUs), for tele-monitoring of renewable energy objectives. Also, we analyze a proposed cloud M2M system, where each RTU communicates by radio with a telemetry data gateway connected to a cloud computing infrastructure equipped with appropriate software that delivers processed data. Furthermore, we present how we use a search based application built on Exalead CloudView to search for weak signals in big data. In particular, given the telemetry application, we propose to leverage trivial and non-trivial connections between different sensor signals and data from other online environmental wireless sensor networks, in order to find patterns that are likely to provide innovative solutions to existing problems. The aggregation of such weak signals will provide evidence of connections between renewable energies and environmental related issues faster and better than trivial mining of sensor data. As a consequence, the software has a significant potential for matching environmental applications and challenges that are related in non-obvious ways. Finally, we present the measurement results for a hydro-energy case study and discuss the applicability for other renewable sources such as solar or wind energy.

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“…Furthermore, by using low power sensors and data aggregation the energy consumption of the M2M network can be optimized [29].…”

Section: Big Datamentioning

confidence: 99%

Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System

Suciu

Vulpe

Marțian

et al. 2015

Wireless Pers Commun

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“…For instance, authors in [28] evaluated the performance of data aggregation in terms of energy efficiency, thus to increase the lifetime of capillary M2M networks. However, the authors neglected the emerging mobile M2M applications in their work.…”

Section: Literature Reviewmentioning

confidence: 99%

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

Marwat

Mehmood

Görg

et al. 2016

J Wireless Com Network

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Machine-to-machine (M2M) communication is becoming an increasingly essential part of mobile traffic and thus also a major focus of the latest 4G and upcoming 5G mobile networks. M2M communication offers various ubiquitous services and is one of the main enablers of the Internet-of-things (IoTs) vision. Nevertheless, the concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability as well as decreasing costs of future mobile networks. Resultantly, M2M traffic poses drastic challenges to mobile networks, particularly due to the expected large number of devices sending small-sized data. Moreover, mobile M2M traffic is anticipated to degrade the performance of traditional cellular traffic due to inefficient utilization of the scarce radio spectrum. This paper presents a novel data aggregation and multiplexing scheme for mobile M2M traffic and thus focuses on the latest 3GPP (3 rd Generation Partnership Project) tong-term-evolution-advanced (LTE-A) networks. 3GPP standardized layer 3 inband Relay Nodes (RNs) are used to aggregate uplink M2M traffic by sharing the Physical Resource Blocks (PRBs) among several devices. The proposed scheme is validated through extensive system level simulations in an LTE-A based implementation for the Riverbed Modeler simulator. Our simulation results show that besides coverage extensions, RNs serve approximately 40 % more M2M devices with the proposed data multiplexing scheme compared to the conventional without multiplexing approach. Moreover, in this paper an analytical model is developed to compute the multiplexing transition probabilities. In the end, the simulation and analytical results of multiplexing transition probabilities are compared in order to analyze the multiplexing scheme.

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“…Tsai et al [40] simulated effect of data aggregation to energy consumption of M2M systems.Problems are also different from our work because these works do not mention development of aggregation logics.…”

Section: Related Workmentioning

confidence: 99%

Rule-Based Sensor Data Aggregation System for M2M Gateways

Nakamura

Moriguchi

Irie

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYTo reduce the server load and communication costs of machine-to-machine (M2M) systems, sensor data are aggregated in M2M gateways. Aggregation logic is typically programmed in the C language and embedded into the firmware. However, developing aggregation programs is difficult for M2M service providers because it requires gatewayspecific knowledge and consideration of resource issues, especially RAM usage. In addition, modification of aggregation logic requires the application of firmware updates, which are risky. We propose a rule-based sensor data aggregation system, called the complex sensor data aggregator (CSDA), for M2M gateways. The functions comprising the data aggregation process are subdivided into the categories of filtering, statistical calculation, and concatenation. The proposed CSDA supports this aggregation process in three steps: the input, periodic data processing, and output steps. The behaviors of these steps are configured by an XML-based rule. The rule is stored in the data area of flash ROM and is updatable through the Internet without the need for a firmware update. In addition, in order to keep within the memory limit specified by the M2M gateway's manufacturer, the number of threads and the size of the working memory are static after startup, and the size of the working memory can be adjusted by configuring the sampling setting of a buffer for sensor data input. The proposed system is evaluated in an M2M gateway experimental environment. Results show that developing CSDA configurations is much easier than using C because the configuration decreases by 10%. In addition, the performance evaluation demonstrates the proposed system's ability to operate on M2M gateways. key words: M2M gateway, sensor data aggregation, in memory processing, IoT (the Internet of Things)

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Reducing Energy Consumption by Data Aggregation in M2M Networks

Cited by 24 publications

References 11 publications

Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System

Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

Rule-Based Sensor Data Aggregation System for M2M Gateways

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