ChapterPDF Available

A LoRaWAN Wireless Sensor Network for Data Center Temperature Monitoring


Abstract and Figures

High-performance computing installations, which are at the basis of web and cloud servers as well as supercomputers, are constrained by two main conflicting requirements: IT power consumption generated by the computing nodes and the heat that must be removed to avoid thermal hazards. In the worst cases, up to 60% of the energy consumed in a data center is used for cooling, often related to an over-designed cooling system. We propose a low-cost and battery-supplied wireless sensor network (WSN) for fine-grained, flexible and long-term data center temperature monitoring. The WSN has been operational collecting more than six million data points, with no losses, for six months without battery recharges. Our work reaches a 300× better energy efficiency than the previously reported WSNs for similar scenarios and on a 7× wider area. The data collected by the network can be used to optimize cooling effort while avoiding dangerous hot spots.
Content may be subject to copyright.
A LoRaWAN Wireless Sensor Network for Data Center
Temperature Monitoring
Tommaso Polonelli1, Davide Brunelli2, Andrea Bartolini1, Luca Benini1,3
1 University of Bologna, Italy
{tommaso.polonelli2, andrea.bartolini, luca.benini}
2 University of Trento, Italy -
3 ETH Zurich, Switzerland -
Abstract. High-performance computing installations, which are at the basis of
web and cloud servers as well as supercomputers, are constrained by two main
conflicting requirements: IT power consumption generated by the computing
nodes and the heat that must be removed to avoid thermal hazards. In the worst
cases, up to 60% of the energy consumed in a data center is used for cooling,
often related to an over-designed cooling system. We propose a low-cost and
battery-supplied wireless sensor network (WSN) for fine-grained, flexible and
long-term data center temperature monitoring. The WSN has been operational
collecting more than six million data points, with no losses, for six months
without battery recharges. Our work reaches a 300x better energy efficiency
than the previously reported WSNs for similar scenarios and on a 7x wider area.
The data collected by the network can be used to optimize cooling effort while
avoiding dangerous hot spots.
1 Introduction
Due to the extraordinary fast growth of IT (Information Technology) industry, the
worldwide data centers energy consumption has attracted global attention because of
their impact on pollution and climate change. In past years the overall trend in
hardware design field continue to decrease the power efficiency of both processor and
memory, but a parallel trend at data centers is that the heat density of computing
systems has increased at a faster rate. Nowadays, into the data center hosting facilities,
the IT devices count only for 30% to 60% [1] of the overall electric bill; indeed the
rest of the energy is lost by the environmental control systems such as Computer Air
Conditioning (CRAC) units, water chillers, humidifiers or during the power
conversion process.
Real-time data about the temperature inside a data center building [2], together with
historical information collected, are useful not only for diagnosis problems, to predict
possible thermal issues, but also for improving the data center power efficiency [3].
Tier0 Supercomputing centers [4] are designed for peak computational performance
and thus are characterized by the highest power/computational densities between data
centers. Cooling efficiency in this domain limits the maximum achievable
performance and thus Tier0’s QoS. As an example, the former most powerful
supercomputer worldwide Tianhe-2 occupies 720m2, consumes 17.8 MW for 33.2
Petaflops. However, the power consumption increases to 24 MW also considering the
cooling infrastructure [5]. This cost can be reduced by adopting predictive control
approaches [6][7], which combine the supercomputer’s IT power consumption,
external and room temperatures for optimal control of cooling effort [8]. Traditional
commercial solutions for temperature monitoring use wired sensors, but due to the
high installation costs, systems utilize only a few measurements points [3]. On the
other hand, wireless sensor networks (WSN) are ideal for scattered sensing systems;
mobile nodes may be placed freely in critical areas to measure temperature or power
consumption [9][10]. Unfortunately, the data center environment is characterized by a
typical condition that is generally adverse for wireless communication. The primary
material in data center facilities is metal, in addition to switches, racks, cables, and
other obstacles including cooling fans, power distribution system and cable rails that
generate intense electromagnetic noise.
This paper proposes a WSN designed to monitor in a distributed fashion the
temperature evolution in a data center with the goal to improve cooling efficiency. We
use Semtech's LoRa (Long Range) [11] that is one of the most promising wide-area
IoT communication technologies [12][13] because of the proprietary spread spectrum
So far, indoor monitoring applications used mostly Zigbee and other mesh-oriented
protocols in the 2,4GHz bandwidth studied intensively during the past decade. In this
case, multi-hop communication is necessary for long distance transmission, or for
reliability in noisy or crowded environments. The usage of LoRa in indoor
environments introduces a return to the one-hop communication model, at the cost of
a reduced available bandwidth, but with the capability to cover up 34000m2 [14] in a
single communication with a similar transmission power. Moreover, for low traffic
intensity, it has been demonstrated in [15] that indoor LoRa communication is more
energy efficient than a multi-hop network that needs more than one router to cover the
same distance. That article shows that even the 802.11ah is significantly worse than
LoRa, regarding energy efficiency, when an application requires to exchange tiny data
Considering these recent results and the requirements for data-center environmental
monitoring, in this paper, we describe the first deployment of a distributed
temperature monitoring system in a data-center using the LoRa technology. The
contribution of the paper is twofold: (i) to explore the performance of the application
using LoRaWAN, especially regarding throughput, energy consumption and packet
conflicts; (ii) to investigate the merits of using LoRaWAN as an alternative for the
mesh-oriented communications used in this class of applications, so far.
The sensor nodes are designed for easy deployment within an existing
supercomputing center facility. Experiments are run in CINECA, the Tier0
supercomputing center for scientific research in Italy [4].
The article is organized as follows: Section 1.1 presents the related works. Section 2
describes the LoRa capabilities, while Section 3 discusses the hardware design.
Section 4 describes the WSN setup and the reached results. Eventually, Section V
concludes the paper with comments and final remarks.
1.1 Related Works
Thermal management of data centers has been studied in depth in the last few years
[6], ranging diverse strategies, with the goal to reduce the cooling infrastructure
power consumption, improving the overall efficiency. In this context, numerous
proposals have been presented: in [3] optimization applies to select a data center
cooling mode and liquid flow with the aim of minimizing the facilities overall power,
under the quality of service and thermal requirements. Optimization in [16] and [17]
aiming to select the rack fans speed, whereas in [18], the CRACs internal
temperatures are used as the reference to minimize the computational and thermal
power. In [19], the effort is put into selecting the active subfloor tiles opening and
blowers speed, for optimizing the Computer Room Air Handlers (CRAHs) cooling
system. In [14] an optimal control policy is presented for hybrid systems featuring
free, liquid and air cooling. The optimal control policy is based on a predictive model
of the cooling efficiency based on environmental, room and IT temperature
measurements. All model-predictive approaches implicitly assume the availability of
high-quality temperature data, both off-line for model identification and online for
driving control decision. In [20] a green cooling system is proposed; the management
model collects information from WSN that utilizes temperature sensors to control the
ventilation system and the air conditioning. The WSN is based on the ZigBee protocol
and includes the actuators.
This generates a highly sophisticated network and a complex deployment even
with 10 boards scattered in only 20m2. As a comparison, our deployment is much
cheaper because does not need any router, and at the same cost we could deploy more
than 30 nodes. In our test, we covered a 150m2 data center room transmitting data to a
gateway installed another area of the building at a 60m distance. Moreover, some of
the sensor nodes proposed in [21] require a wired power supply that severely restricts
the deployment and increases the installation cost.
Another recent WSN for environmental data monitoring is presented in [5] and
proposes Zigbee sensor nodes supplied by 2000mAh batteries. The deployment
consists of 10 nodes in a 30m2 area that represents half of their data room. Since the
network is configured as a very dense mesh to provide reliability in case of packet
loss, several boards are programmed as a router, and communications from the more
distant node need up to 4 hops. This has a tremendous impact on the power
consumption and on the lifetime of the installation. A sensor in [5] consumes 73mA
on average that is two orders of magnitude higher than our LoRa solution. With an
average current consumption of 194uA, our deployment can operate unattended for
more than 200 days using a 1000mAh battery, half of the size needed in [5].
2 LoRa wireless modulation capabilities
LoRa™ is a wireless modulation for long-range low-power low-data-rate
applications developed by Semtech. LoRa is supported by an alliance (LoRa Alliance)
that has defined LoRaWAN, standardizing the higher-layer protocols on top of the
physical radio to regulate secure communication for IoT applications and wide area
networks. The network consists of end devices and gateways. Based on the
LoRaWAN specifications three classes of end devices are defined: Class A, Class B,
and Class C.
LoRa modulation is both bandwidth and frequency scalable. Moreover, due to the
high Bandwidth Time Product (BT), a LoRa signal is very resistant to both in-band
and out-of-band interference mechanisms. Since the symbol period can be longer than
the typical short-duration of a noisy spike, it provides immunity to pulsed interference
mechanisms. With spread spectrum, the wireless communication issues caused by the
presence of interference is reduced by the process gain that is inherent to the
modulation. These interfering signals are spread beyond the desired information
bandwidth and can be easily removed by filtering at the receiver side.
Another factor that must be considered is the wireless link budget that defines the
maximum communication range for given transmission power. The link budget delta,
from a comparison between an FSK transceiver with a sensitivity of -122 dBm at
1.2Kbps with LoRa, at a fixed transmission output power, is more than four times,
such as deeply studied in [7].
As well known, in a wireless path the propagation loss increases with the distance
between nodes; this means that, for narrowband systems, additional nodes could be
needed to generate a mesh network topology (with increased network complexity and
redundancy) or to operate as additional repeaters for star networks. Unfortunately, the
installation cost associated with installing a repeater increases the overall price of the
WSN, in term of hardware components and software development. LoRa can
minimize this cost, using a simple star network, by taking advantage of the property
that signals with a different spreading factor or sequence will appear as noise at the
3 Network Hardware Design
The sensor node is designed to be low-power and versatile with internal temperature
and humidity sensor in parallel with a thorough assessment of the components cost.
Moreover, multiple sensors and devices can be connected to their expansion
connectors, useful for future works. The gateway used for tests is a commercial
product [22] easily customizable in hardware and software. It can be configured both
as gateway alone or gateway and server.
3.1 Sensor Node
The end-node is based on the STM32L4 MCU from ST Microelectronics; this
component includes both low-power and high computational resources, while the
RFM96 SoM transceiver [23] manages the LoRa Physical layer. The high sensitivity
in reception (-148 dBm) combined with the integrated +20 dBm power amplifier
makes it optimal for applications requiring range or robustness in communications.
Figure 1 - Sensor node schematic.
The power consumption of each element was measured. The obtained current in
sleep mode is 4 uA @ 3V with the Real Time Clock (RTC); instead, the STM32L4
provides a low consumption in RUN mode @48 MHz about 8.25 mA, the analog
circuits (2 mA) and the RFM96 (76 mA in TX at 10 dBm) are the most expensive
parts. The sensor node firmware is based on the I-CUBE-LRWAN libraries package,
which is configured to be compliant with LoRaWAN Class A. Each sensor node is
programmed to transmit a packet to the gateway every 30 seconds. Every packet
includes a temperature and humidity sample, furthermore, to monitor and manage the
WSN, node status information is acquired, such as the battery voltage and channel
conflicts. With this configuration and with a battery of 1000mAh, the sensor node
lifespan is seven months. Notice that the solution presented in [21] can operate only a
couple of days, using the same energy stored in the battery.
3.2 LoRaWAN Gateway and Server
MultiConnect® ConduitTM is a highly configurable, manageable, and scalable
communications gateway for industrial IoT applications. Network connectivity
choices to any preferred data management platform include carrier approved 4G-LTE,
3G, 2G and Ethernet. A diverse range of accessory cards provide the local wired or
wireless field asset connectivity and plug directly into the rear of the Conduit
gateway. The LoRaWAN radio front-end includes a Semtech SX1301 and two
SX1257 that demodulate the packets received simultaneously on all channels.
4 Network Setup
Figure 2 - Sensor node deployment. (a) a hallway positioning is proposed, (b) the
sensor node is under the data center floor, (c) GALILEO (CINECA) data center map
A final network experimental deployment was carried out with 20 sensor nodes,
arranged in key points of data center’s room. This test aimed to verify in a realistic
operating condition the LoRa capabilities in a noisy wireless environment. We
positioned all the devices in hallways between racks, close to the CRAC output and
under the floor, besides, some sensor was placed into full metallic air conditioning
pipes and structure. Figure 2 shows two pictures of the WSN deployment.
4.1 Network results
We tested the sensor node for six months in CINECA data center, without recharging
the batteries for the entire trial duration. During this time, we acquired more than six
million measurements. Taking as reference a month of operation, with 1,073,771
points acquired, the LoRa radio conflicts were in average the 0.55%, that allows high
reliability in term of packet communication and data analysis, a prerequisite for
automatic cooling systems. By employing automatic retransmission of the collided
packets, no discontinuity of collected data was detected, and only one sensor node,
during the entire trial period was rebooted due to firmware issues.
All the collected data are acquired by InfuxDB time-series database, which is
managed by a Node-Red application. Moreover, to provide a ready-to-use cooling
monitoring system we used Grafana (Figure 3), a platform for data analytics and
monitoring. This tool is used by the data center's operators to dynamically adjust the
room temperature accordingly with IT's heat generation.
Figure 3 - Grafana dashboard, temperatures acquired in CINECA data center.
With an average current consumption of 194uA, due to the unique characteristics of
LoRa modulation, our WSN is capable to operate with a single hop communication
under challenging environments, allowing both communication reliability and low
power. In comparison with other solutions (Table 1), we propose a reduction of the
average number of hop (ANH) up to 4 times and an improvement of 90x the battery
lifetime respect ZigBee protocol and 1.5x with others LoRaWAN WSN.
Table 1 - WSN in data center environments
Max Range
This Paper
6 months
48 h
4 months
5 Conclusion
This paper demonstrates that a LoRa wireless sensor nodes can be an effective and
low-cost tool for temperature and humidity sensing in a data center. The proposed
nodes and LoRaWAN WSN can provide reliable data for data center’s room and
environmental monitoring, and to train and feed numerical models for optimal cooling
control. LoRaWAN network offers the advantage of easy deployment throughout the
data center facilities because there is no need of wiring for power and communication.
This network also offers freedom in deployment, as the sensor module can be placed
in locations where wired sensors would be unfeasible for technical or safety reasons
and are not constrained to keep specific distance between nodes and routers like other
mesh-oriented protocols.
This work was partially supported by a collaboration grant with CINECA. A special
thanks for the support to Michele Toni, Massimo Alessio Mauri, Emanuele Sacco is
also acknowledged.
[1] C.L. Belady. “In the data center, power and cooling costs more than the equipment it
supports” ElectronicsCooling magazine, 3(1), February 2007.
[2] M. Rossi, L. Rizzon, M. Fait, R. Passerone and D. Brunelli, "Energy Neutral Wireless
Sensing for Server Farms Monitoring," in IEEE Journal on Emerging and Selected Topics
in Circuits and Systems, vol. 4, no. 3, pp. 324-334, Sept. 2014.
[3] J. Kim, M. Ruggiero, and D. Atienza, “Free cooling-aware dynamic power management
for green datacenters,” Proc. of IEEE HPCS, pp. 140146, 2012.
[4] Top500 list, Available online:
[5] J. Dongarra. “Visit to the national university for defense technology changsha”. China,
University of Tennessee, 2013.
[6] R. Rhomadon, M. Ali, A. M. Mahdzir, and Y. A. Abakr, “Energy efficiency and
renewable energy integration in data centers. Strategies and modeling review," Renewable
and Sustainable Energy Reviews, vol. 42, pp. 429445, 2015.
[7] Park, Seonghyun, and Janghoo Seo. "Analysis of air-side economizers in terms of cooling-
energy performance in a data center considering exhaust air recirculation." Energies 11.2,
[8] C. Conficoni, A. Bartolini, A. Tilli, C. Cavazzoni and L. Benini, "HPC Cooling: A
Flexible Modeling Tool for Effective Design and Management," in IEEE Transactions on
Sustainable Computing.
[9] D. Porcarelli, D. Brunelli, L.Benini, “Clamp-and-Forget: A self-sustainable non-invasive
wireless sensor node for smart metering applications,” Microelectronics Journal, Volume
45, Issue 12, 2014, Pages 1671-1678
[10] D. Balsamo, D. Porcarelli, L. Benini and B. Davide, "A new non-invasive voltage
measurement method for wireless analysis of electrical parameters and power quality,"
SENSORS, 2013 IEEE, Baltimore, MD, 2013, pp. 1-4.
[11] LoRa™ Modulation Basics, AN1200 v22, , LoRa Alliance, Inc. 2400 Camino Ramon,
Suite 375 San Ramon, CA 94583 (2015),” LoRa Alliance, Tech.
[12] D. Brunelli; E. Bedeschi; M. Ferrari; F. Tinti; A. Barbaresi; L. Benini “Long-range Radio
for Underground Sensors in Geothermal Energy Systems, in: Applications in Electronics
Pervading Industry, Environment and Society.” Lecture Notes in Electrical Engineering,
vol 429. Springer, Cham, Springer (2016)
[13] D. Sartori and D. Brunelli, "A smart sensor for precision agriculture powered by microbial
fuel cells," 2016 IEEE Sensors Applications Symposium (SAS), Catania, 2016, pp. 1-6.
[14] J. Haxhibeqiri, A. Karaagac, F. Van den Abeele, W. Joseph, I. Moerman and J. Hoebeke,
"LoRa indoor coverage and performance in an industrial environment: Case study," 2017
22nd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA), Limassol, 2017, pp. 1-8.
[15] É. Morin, M. Maman, R. Guizzetti and A. Duda, "Comparison of the Device Lifetime in
Wireless Networks for the Internet of Things," in IEEE Access, vol. 5, pp. 7097-7114,
[16] R. Das, J. O. Kephart, J. Lenchner, and H. Hamann, "Utility function-driven energy-
efficient cooling in data centers,” Proc. Of ICAC, pp. 15261544, 2010.
[17] A. Banerjee, T. Mukherjee, G. Varsamopoulos, and S. K. Gupta, “Energy-optimal
dynamic thermal management: Computation and cooling power co-optimization,” IEEE
Trans. Ind. Informat., vol. 6(3), pp. 340351, 2010.
[18] L. Parolini, B. Sinopoli, B. H. Krogh, and Z. Wang, "A cyber-physical systems approach
to data center modeling and control for energy efficiency,” Proc. of the IEEE, vol. 100(1),
pp. 255268, 2012.
[19] R. Zhou, Z. Wang, C. E. Bash, A. McReynolds, C. Hoover, R. Shih, N. Kumari, and R. K.
Sharma, “A holistic and optimal approach for data center cooling management,” Proc. of
IEEE Amer. Ctrl. Conf., pp. 13461351, 2011.
[20] Liu, Qiang, et al. "Green data center with IoT sensing and cloud-assisted smart
temperature control system." Computer Networks 101 (2016): 104-112.
[21] Rodriguez, Michael G., et al. "Wireless sensor network for data-center environmental
monitoring." Sensing Technology (ICST), 2011 Fifth Int. Conference on. IEEE, 2011.
[22] MultiConnect® Conduit™, programmable gateway with Linux. Available online:
[23] 868/915Mhz RF Transceiver Module. Available online:
... IoT devices built with wireless technologies like Bluetooth, wireless sensor networks (WSN), ZigBee, Wi-Fi, and Radio-frequency identification (RFID) [1][2][3][4] are widely employed to transmit sensor data from the end nodes to gateways and later sent to the application server for further use by authorized users. Several temperature and humidity monitoring systems have been developed using these wireless technologies and found applications in greenhouses [5], server rooms [6], data centers [7], and so on. In greenhouses, for instance, any wrong combination of temperature and humidity will result in an abnormal spread of diseases to the crops, thereby reducing the yield and profit of the farmers [8]. ...
Full-text available
Temperature and humidity monitoring is an integral aspect of human lives and has several applications ranging from greenhouses, laboratories, food industries, server rooms, data centers, and so on. However, the primary technologies that drive these systems suffer from numerous drawbacks such as deployment cost, coverage, and power consumption. This paper aims to employ new and affordable technology, namely low power wide area networks, to implement a temperature and humidity monitoring system (THMS) due to their low cost, low power consumption, and long range in data transmission. A LoRa-based THMS using a Raspberry Pi 3 B+ gateway with a single channel packet forwarder and an Arduino UNO end device with a DHT11 sensor was designed and implemented. After registering the gateway and the end device on The Things Network (TTN), temperature and humidity values of 22.0℃ and 33.0% were recorded by the Arduino serial monitor and the TTN application server. The above implementation clearly shows that sensor values can be effectively transmitted using LoRa and LoRaWAN over long distances with minimal power consumption.
... Bandwidth refers to the capacity of the network connection for sending and receiving a vast amount of medical data in a given time [42], [44]. ...
In today’s modern era, with the rapid growth of edge-assisted solutions especially Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. Connected healthcare systems refer to health systems in which all the stakeholders are connected to each other. These systems employ novel technologies such as IoT, edge computing, and Artificial Intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, emerging connected healthcare systems encounter many restrictions and require new policies. Intelligent and edge computing technologies integration including IoT are a promising solution for most of the limitations arising from using cloud computing in connected healthcare applications. In fact, edge computing and AI are essential factors of smart IoT applications. Moving the computation and processing closer to the data sources and end-users, edge computing can reduce latency, bandwidth usage, and energy consumption. Notwithstanding the importance of edge-assisted solutions for connected healthcare systems, there is not any systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to comprehensively examine the state-of-the-art research in this area. We have reviewed the qualified existing works and divided them into two main taxonomies, including patient-centric and process-centric techniques, and their benefits and issues are also provided and discussed thoroughly. Furthermore, essential factors such as available datasets and parameters like accuracy, mobility, and data rate are comprehensively described and examined. In fact, in this paper, we bridge the gap between edge computing and connected healthcare solutions by comprehensively discussing relevant vital issues and highlighting the future trends.
... To realistically define an energy characterization of our sensor node, we develop a model based on measurements from a real LoRaWAN system and previous works [5], [13] assuming a fixed time interval between every transmission. Each Datarate (DR) used in this experimental evaluation, from 0 to 5, generates several configurations that impact the LoRa modulation. ...
Full-text available
Low power and long-range communications are crucial features of the Internet of Things (IoT) paradigm that is becoming essential even for industrial applications. Today, the most promising long-range communication technologies are LoRaWAN and Narrow Band IoT (NB-IoT), which are driving a large IoT ecosystem. In this paper, we evaluate the performance of LoRaWAN and NB-IoT with accurate in-field measurements using the same application context for a fair comparison in terms of energy efficiency, lifetime, quality of service, and coverage. The NB-IoT energy transmission is scarcely dependent on the payload length. Thus applications that can tolerate buffering and caching techniques on the node are favored. On the other hand, LoRaWAN consumes 10x lower energy compared to NB-IoT for occasional and latency-sensitive communications, for which it enables much end-device lifetime. Finally, this paper provides design guidelines for future industrial applications with stringent requirements of long-range and low power wireless connectivity.
... To realistically define an energy characterization of our sensor node, we develop a model based on measurements from a real LoRaWAN system and previous works [5], [13] assuming a fixed time interval between every transmission. Each Datarate (DR) used in this experimental evaluation, from 0 to 5, generates several configurations that impact the LoRa modulation. ...
... R.2 High bandwidth: Bandwidth is the capability of wireless or wired network communication link to send a high amount of data from one point to another in a given amount of time over a network [41]. Biomedical sensors can send a limited amount of information due to restricted bandwidth in current 3G and 4G network, especially in real time monitoring applications [42]. ...
Full-text available
Healthcare is undergoing a rapid transformation from traditional hospital and specialist focused approach to a distributed patient-centric approach. Advances in several technologies fuel this rapid transformation of healthcare vertical. Among various technologies, communication technologies have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses the existing 4G network and other communication technologies for smart healthcare applications and are continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart healthcare market expands the number of applications connecting to the network will generate data that will vary in size and formats. This will place complex demands on the network in terms of bandwidth, data rate and latency, among other factors. As this smart healthcare market matures, the connectivity needs for a large number of devices and machines with sensor-based applications in hospitals will necessitate the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical Machine Type Communication. Existing communication technologies are unable to fulfill the complex and dynamic need that is put on the communication networks by the diverse smart healthcare applications. Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density and high energy efficiency. The future smart healthcare networks are expected to be a combination of 5G and IoT devices which are expected to increase cellular coverage, network performance and address security related concerns. This paper provides a state-of-the-art review of 5G and IoT enabled smart healthcare, Taxonomy, research trends, challenges and future research directions.
Full-text available
Complex computing platforms such as High Performance Computers and Data Centers are critical systems from the energy sustainability viewpoint, due to their high computational power and demanding thermal stability specifications. In this context, cooling is a crucial component to operate such systems efficiently. Adavanced solutions, based on liquid and hybrid topologies are available today, but they come with a twofold challenge. On one hand, as widely recognized in the literature, the cooling devices need to be operated in a coordinated and energy-efficient fashion. In addition, after design and deployment, the cooling system has to be dynamically managed to efficiently adapt to workload, and environmental conditions. On the other hand, at design time, the cooling hardware architecture has to be selected in order to fit in the best way the needs of the computing facility, also depending on the environmental conditions characterizing its location.
Full-text available
Demand is soaring for data centers with advanced data-processing capabilities. In data centers with high-temperature information technology (IT) equipment, enormous cooling systems are operated year-round. To date, studies have aimed to improve the cooling efficiency of server-room units, but cooling-energy-consumption analysis considering the recirculation of exhaust air (EA) has not been researched to a sufficient degree. This study analyzed the cooling-energy saving effects considering the EA-recirculation and supply-air (SA)-temperature conditions when direct and indirect air-side economizers were applied to a data center in Korea. Thirteen case studies were conducted. The results showed that when the EA-recirculation ratio in the direct air-side economizer was 15%, its annual cooling-energy consumption increased by approximately 6.1% compared to the case with no recirculation. The indirect air-side economizer also exhibited an approximately 9% increase in cooling-energy consumption. On the other hand, when the SA temperature changed to 22 °C, the annual cooling-energy consumption of the direct and indirect air-side economizers decreased by approximately 67% and 55%, respectively, compared to a central chilled-water system. This indicates the importance of developing measures to prevent EA recirculation and of securing a wind path for the improvement of air-conditioning efficiency in data centers at the design stage.
Full-text available
This paper presents a comparison of the expected lifetime for IoT devices operating in several wireless networks: IEEE 802.15.4/e, Bluetooth Low Energy (BLE), IEEE 802.11 Power Saving Mode (PSM), IEEE 802.11ah, as well as in new emerging long-range technologies such as LoRa and SIGFOX. To compare all technologies on an equal basis, we have developed an analyzer that computes the energy consumption for a given protocol based on the power required in a given state (Sleep, Idle, Tx, Rx) and the duration of each state. We consider the case of an energy constrained node that uploads data to a sink, analyzing the physical (PHY) layer under Medium Access Control (MAC) constraints, and assuming IPv6 traffic whenever possible. The study takes into account the energy spent in retransmissions due to corrupted frames and collisions as well as the impact of imperfect clocks. The comparison shows that BLE offers the best lifetime for all traffic intensities in its capacity range. LoRa achieves long lifetimes behind 802.15.4 and BLE for ultra low traffic intensity; SIGFOX only matches LoRa for very small data sizes. Moreover, taking into account the energy consumption due to retransmissions of lost data packets only decreases the lifetimes without changing their relative ranking. We believe that these comparisons will give all users of IoT technologies indications about the technology that best fits their needs from the energy consumption point of view. Our analyzer will also help IoT network designers to select the right MAC parameters to optimize the energy consumption for a given application.
Conference Paper
The paper presents the design of a temperature monitoring system in a very harsh environment, such as Shallow Geothermal Systems (SGS), where the information of underground temperature is necessary to assess the thermal potential of the soil, for maximizing the efficiency of the SGS. The challenge is to get information at different depths (sometimes up to −100 m), to transmit data wirelessly in rural areas where conventional wireless connections (e.g. WiFi, GSM) are not guaranteed and energy availability poses severe limits. Our design exploits a recent new modulation protocol developed for long-range transmission, at the minimum energy cost, and a two-tier hardware architecture for measuring underground temperature. Aggressive duty cycling permits to achieve lifetime of several years. Experimental results demonstrate the utility of such a system during the design and the operational activity of a SGS.
The continuous growth in size, complexity and energy density of data centres due to the increasing demand for storage, networking and computation has become a worldwide energetic problem. The emergent awareness of the negative impact that the uncontrolled energy consumption has on natural environment, the predicted limitation of fossil fuels production in the upcoming decades and the growing associated costs have strongly influenced the energy systems engineering work in the last decades. Therefore, the implementation of well known and advanced energy efficiency measures to reduce data centres energy demand play an important role not only to a supportable growth but also to reduce its operational costs. The carbon footprint is greatly influenced by the energy sources used. Therefore, there have been recent efforts to exploit and reuse or combine green energy sources in data centres to lower brown energy consumption and CO2 emissions. This paper presents a comprehensible overview of the current data centre infrastructure and summarizes a number of currently available energy efficiency strategies and renewable energy integration into data centres and its characterization using numerical models. Moreover it would be necessary to develop dynamic models and metrics for properly understand and quantify the energy consumption and the benefits of applying the incoming energy efficiency strategies and renewable energy sources in the data centres. Thus, the researches or investors will be able to compare with reliability the different data centre designs and choose the best option depending on the renewable energy sources and capital available.
Energy harvesting techniques are consolidating as effective solutions to power electronic devices with embedded wireless capability. We present an energy harvesting system capable to sustain sensing and wireless communication using thermoelectric generators as energy scavengers and server's CPU as heat source. We target data center safety monitoring, where human presence should be avoided, and the maintenance must be reduced the most. We selected ARM-based CPUs to tune and to demonstrate the proposed solution since market forecasts envision this architecture as the core of future data centers. Our main goal is to achieve a completely sustainable monitoring system powered with heat dissipation of microprocessor. To this end we present the performance characterization of different thermal-electric harvesters. We discuss the relationship between the temperature and the CPU load percentage and clock frequency. We introduce a model to simulate the power characteristic of the harvester and a prototype has been realized to demonstrate the feasibility of the proposed approach. The resulting system achieves a minimum 5 min sampling frequency of environmental parameters such as temperature, humidity, light, supply voltage, and carbon-monoxide/volatile organic compounds gases using a MOX sensor mounted on a commercial wireless node with a power budget in the microwatt range.