heterogeneous communication architecture for the smart grid · pdf fileand communication...

y and large, the current power grid is defined as asystem made of electrical generators, transformers,transmission, and distribution lines used to deliverelectricity power to eventual users. Smart grid net-

work control and monitoring are very important features inorder to provide distributed generation and storage [1], quali-ty of service (QoS) [2, 3], and security [1, 4]. Nevertheless,most of these functions are currently only carried out at highvoltage. In recent years, international organizations, govern-ments, utilities, and standardization organizations havebecome increasingly aware of the need for grid modernization[5]. The future smart grid must be distinguished by its self-healing and automation features, taking into account that itshould support thousands of clients and energy providers.

Smart grids must be understood as complex networks ofintelligent electronic devices (IEDs), wired and wireless sen-sors, smart meters [6], distributed generators, and dispersedloads that require cooperation and coordination in order toplay their expected role [2, 3]. Needless to say, informationand communication technologies (ICTs), trust management,and technological integration also play an essential role in thisscenario [1].

In this article, a heterogeneous communication paradigmbased on the requirements of the smart grid network [7] isproposed in order to support smart grid applications. Thiscommunication paradigm achieves end-to-end integration ofheterogeneous technologies by using the ubiquitous sensornetwork (USN) architecture [8] and defining the interoper-ability with the next-generation network (NGN) as the smartgrid backbone [9]. The framework design must include adecentralized middleware that has to coordinate all the smartgrid functions [3] (Fig. 1).

This article is organized as follows. First, we introducesmart grid fundamental topics, and describe the communica-tions and QoS requirements of smart grids. Second, we dis-cuss our proposal of a smart grid communication architecturebased on International Telecommunication Union (ITU)USNs plus NGNs. Then we present the adaptation of thesmart grid communication architecture to the new communi-cation paradigm proposed in this article. Finally, we concludeand provide further work.

Components and CommunicationRequirementsThe change toward the so-called smart grid promises tochange the whole business model, and this concerns utilities,regulation entities, service providers, technology suppliers,and electricity consumers. The smart grid requires a broadarray of requirements that are different from those of othertypes of networks; for example, very high availability togetherwith low latency. This transformation toward an intelligentnetwork is possible by importing the philosophy, concepts, andtechnologies from the Internet context [1–5]. According to thedefinition in the Strategic Deployment Document (SDD) ofthe European SmartGrids Technology Platform, a smart gridis an electricity network that can intelligently integrate theactions of all users connected to it (generators, consumers,and those that do both) in order to efficiently deliver sustain-able, economic, and secure electricity supplies.

First and foremost, the main component of the smart gridis the sensor network, which consists of a system of distributedsensor nodes that interact among themselves and with the

IEEE Network • September/October 201130 0890-8044/11/$25.00 © 2011 IEEE

BB

Agustin Zaballos, Alex Vallejo, and Josep M. Selga, University Ramon Llull

AbstractThe smart grid concept provides a solution to the growing recognition that currentutility grids need an ICT deployment infrastructure based upgrade to allow millionsof potential market players to operate and to cope with distributed generation,wide-area situational awareness, demand response, electric storage, and efficientelectric transportation. Smart grid deployment is mainly about defining the neces-sary standards for ICT solutions. The design of the communication network associ-ated with the smart grid involves detailed analysis of i ts communicationrequirements, a proposal of the appropriate protocol architecture, the choice of themost suitable technologies for each case study, and a scheme for the resultant het-erogeneous network management system. Given the smart grid use cases, this arti-cle is focused on proposing a heterogeneous communication paradigm for smartgrids based on power line communications and wireless networks. The proposal isrelated to the framework of the ITU ubiquitous sensor network architecture using theITU next-generation network model. This architecture allows for better managementof the QoS in the smart grid and should facilitate interoperability with other tech-nologies.

Heterogeneous CommunicationArchitecture for the Smart Grid

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IEEE Network • September/October 2011 31

infrastructure in order to acquire, process, transfer and pro-vide information extracted from the physical world. Sensornodes could also have processing and routing capabilitiesusing either a wireless or a wired medium. The processing ofthe sensor information should allow the modification of theelectrical grid behavior through intelligent actuators.

Another important smart grid component is the smart meter[6], which is the bridge between user behavior and power con-sumption metering. Moreover, an enhanced distribution man-agement system (DMS) is required in order to analyze, controland provide enough useful information to the utility. Thesmart grid is also composed of legacy remote terminal units(RTUs) that can perform sensor network gateway functionsacting as intermediate points in the medium voltage network.The sensor network gateway is the bridge between the sensornetwork itself and the back-end system. Therefore, it provideswired/wireless interfaces to other sensor nodes as well aswired/wireless interfaces to existing ICT infrastructures.

Advanced metering infrastructure (AMI) consists of smartmeters, data management, communication network and appli-cations. AMI is one of the three main anchors of smart gridsalong with distributed energy resource (DER) and advanceddistributed automation (ADA). Last but not least, a geo-graphic information system (GIS) and a consumer informa-tion system (CIS) usually contribute with tools and importantprocesses. All the information recollected and processed byDMS must be reported to a supervisory control and dataacquisition system (SCADA).

Smart grid networks will manage real-time information andwill collect information from established IEDs for control andautomation purposes. This kind of data network is not exemptfrom the growing needs of QoS [2, 3]. Smart grids need tocommunicate many different types of devices, with differentneeds for QoS over different physical media. IEDs can havevery different QoS necessities depending on the function car-ried out. For example, real-time communications are requiredin the case of fault detection, service restoration or qualitymonitoring; periodic communications are used in Automatic

Meter Reading systems (AMR); bulk data transfers are usefulto read logs and energy quality information [2, 3, 6].

The IEDs involved in these processes can be situated in dif-ferent locations due to the pursued decentralized architecture.For example, electrical substation elements are connected tothe substation’s Ethernet network; sensors can be installedalong electrical cables communicated through wireless sensorstandards, for example based on IEEE 802.11s. Communica-tions from the control center to energy meters and betweensubstations can be carried out via a high variety of technolo-gies such as narrowband power line communications (NB-PLC), Universal Mobile Telecommunications System(UMTS), general packet radio service (GPRS), broadbandPLC (BPL), or WiMAX.

Today, different standard communication protocols at vari-ous voltage levels and for different kinds of equipment areused. The medium and low voltage communication assets arecharacterized by economically limited ICT infrastructures.Therefore, standardized, open information models and com-munication services for all data exchanges are needed in thiscase. Due to these circumstances, smart grids will be support-ed by a highly heterogeneous data network with strict QoSconstraints. One of the most important specifications requiredfor smart grids is that which refers to necessary communica-tions. A framework for management of end-to-end QoS for allcommunications in the grid will be a must in the future. Infact, a suitable communication infrastructure enables the elec-tric system to increase its efficiency to a much greater extentthan automation without communication capacities could everdo [7].

In this article, a communication paradigm based on IP isproposed for the smart grid, since it is the most widely usedprotocol for communications. Furthermore, several promisingstandards have recently appeared for smart grids that basetheir communications on IP. An appropriate starting point forfurther standards development would be the harmonization ofIEC 61850 standards as they address communications forDER and ADA.

Figure 1. Heterogeneous network integration.

PHY layer

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IEEE Network • September/October 201132

Smart Grid as an USNNowadays, nearly each new application that is added to thepower grid involves the deployment of a new ICT infrastruc-ture. As a consequence, there is a fragmented map of applica-tions and communications, which makes it difficult to improvethe quality of supply and smart grid management in general.This fact usually results in the redundancy of infrastructureand functions in the grid. In this sense, one of the main objec-tives of smart grid development is to deploy a unique integrat-ed system through which all the applications can takeadvantage of the same strengths.

Although a similar approach is being discussed in ITUTelecommunication Standardization Sector (ITU-T) Question25/16, “Framework of USN applications and services forsmart metering (F.USN-SM)” for AMI, to the best of ourknowledge, our approach is the first proposal of a holistic net-work architecture based on USNs with the aim of integratingall the communications requested by smart grid applications

in a single system. There are many applications that can usethe USN, where information and knowledge are developed byusing context awareness. They can be classified as [8]:• Detection: for example, detection of temperatures exceeding

a particular threshold, intruders, and brush fires• Tracking: for example, the tracking of items in supply chain

management, plug-in electrical vehicles (PEVs) in intelli-gent transport systems, and workers in dangerous workenvironments such as offshore platforms

• Monitoring: for example, monitoring of inhospitable envi-ronments such as volcanoes and the structural health ofbuildingsFigure 2 shows our proposal of the schematic model of the

smart grid USN. In fact, it is the adaptation of the ITU’s USNmodel [8] to the smart grid context, using specific applicationsof this domain as USN work at ITU remains fairly generic atthis stage. In the first level, there are different sensor net-works, which transmit and collect information regarding thesurrounding environment. This information is collected by the

Figure 2. Schematic layers of a USN architecture applied to the smart grid.

USNaccess

DMS AMIDistributedgeneration

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Supervisionand vigilance

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ZABALLOS LAYOUT 9/12/11 12:39 PM 2


access network, which can facilitate communication with acontrol center or external entities. Then it is transported via adata network. This ICT infrastructure can be considered anew kind of access network from the point of view of theNGN model.

Before reaching the applications, large volumes of data arecollected and processed in the software called USN middleware.Finally, information reaches the application platform. The USNapplication level is a technology platform to enable the effectiveuse of a USN in a particular industrial sector or application(e.g., real-time control and automation functions) [7].

Paradigm for Smart Grid CommunicationArchitectureSmart Grid’s USN Access Network LevelAlong with the previously stated aspects, it is clear that theICT infrastructure is crucial in the smart grid; notwithstand-ing, there is no single technology that can solve all the needsby itself. In this section, the candidate technologies for thesmart grid communication network are presented, as well asthe network management model based on USNs.

Due to the nature of the power grid, PLC is apparently themost suitable technology for the communication network,especially when much of the smart grid infrastructure isunderground or in enclosed places that are not readily acces-sible. However, as a technological option it presents somedrawbacks, both technically and economically. For example, inNorth America, PLC-based AMI systems are generally notpreferred because there are only one to three customers pertransformer, so most PLC technologies for communicationsare deemed too costly, compared to Europe, where there arearound 100–300 customers per transformer [6, 7]. Thus, ourproposal employs a combination of PLC and wireless tech-nologies.

Access USN Baseline Technology — PLC is a suitable candi-date technology for the USN sensor network, USN access net-work, and even for NGN [7]. This technology uses the powergrid for transmitting data. It can be divided into BPL and NB-

PLC. NB-PLC is being used for electric company communica-tions, meter reading [6], and home automation. NB-PLC usu-ally uses frequencies up to 150 kHz in Europe and 450 kHz inthe United States, and delivers bit rates from 2 to 128 kb/s.On the other hand, BPL gives the opportunity to communi-cate at higher bit rates and can be used in in-home LANs andaccess networks [7]. Common nominal bandwidth values ofBPL are from 10 to 300 Mb/s, although new systems are offer-ing higher bandwidths.

The characteristics of the PLC medium make it especiallydifficult to ensure a given QoS. Some of the problems thatPLC technology has to overcome are: unpredictable frequencyand time dependence of impedance, attenuation and transmis-sion characteristics, impulse and background noise and theirwide variability, limited bandwidth, and harmonic interfer-ence. The variability of the channel is especially troublesomefor QoS because it can suddenly bring the bandwidth down.

At the moment, there are several ongoing standardizationprocesses for PLC. The IEEE standardization process by theP1901 working group is aimed at standardizing both in-homeand access networks for seamless interaction with smart gridapplications. On the other hand, Study Group 15 of the ITU’sStandardization Sector is working in G.hn and G.hnem speci-fications. These standards will comprise home network andaccess network aspects.

Besides PLC communication protocol, several access tech-nologies must be integrated into the resulting smart gridarchitecture. Each utility has its own communication policy,either subcontracting an Internet service provider (ISP) for itscommunications necessities or deploying a private network.Some easy options to integrate are briefly outlined in the fol-lowing: • WIMAX (Worldwide Interoperability for Microwave

Access): IEEE 802.16 is a standard technology for wirelesswideband access. Among its advantages, the ease of instal-lation is by far the most important aspect. WIMAX sup-ports either point-to-multipoint or mesh topologies. Inmesh topologies, it is not necessary that all the nodes areconnected to the central node. In this way, active nodesperiodically announce MSH-NCFG messages (mesh net-work configuration), which contain information about the

Figure 3. Communications network proposed.

CPE PLC(customer premises

equipment)

PLC repeaterEMR

CPE PLC

HAN mesh networkPLC in-homeIEEE 802.15.4g

HAN mesh networkPLC in-homeIEEE 802.15.4g

EMR(electricity meters room)

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base station identifier and the channel in use.• IEEE 802.11s: A draft from IEEE 802.11 for mesh networks

that defines how wireless devices can be connected to cre-ate ad hoc networks. The implementation should be overthe physical layer in the IEEE 802.11 a/b/g/n. A combina-tion of IEEE 802.11n and IEEE 802.11s could be also afeasible solution for USNs.

• IEEE 802.22: It uses the existing gaps in the TV frequencyspectrum between 54 and 862 MHz. The development ofthis standard is based on the use of cognitive radio tech-niques in order to give broadband access in areas with lowpopulation and which are difficult to reach.

USN Sensor Network Technology — Most wireless traditionalsystems use point-to-point or point-to-multipoint technologies.Mesh networks are an alternative to these topologies. Thereare several reasons to think that a mesh network is appropri-ate for the smart grid’s sensor network [7]. Firstly, it is easy toadd new nodes in the network thanks to the self-configurationand self-organization capabilities. Furthermore, a mesh net-work is a robust network as there will almost always be analternative path to the destination. This links with the strin-gent requirements regarding reliability.

Given the large scenario in which the smart grid is going tobe deployed, different technologies will be needed in order tocover all the area. Some technologies based on IEEE 802.15.4are presented as wireless communication candidate technolo-gies that work within mesh networks.• IEEE 802.15.4: It defines the medium access control (MAC)

and physical (PHY) layers in low-rate personal area net-works (LR-PANs). In 2008, the Smart Utility Networks(SUN) Task Group 4g (TG4g) was created within the802.15 group. The role of TG4g is to define new physical

layers to provide a global standard that facilitates very largescale process control applications such as the utility smartgrid.

• IEEE 802.15.5: This is the WPAN mesh standard approvedin March 2009. This working group was established in orderto define a mesh architecture in PAN networks based onIEEE 802.15.4. There are different proposals regardingrouting in LR-WPAN networks. Nevertheless, these algo-rithms are not fully optimal.In the upper layers, there may be communication protocols

such as Zigbee or 6LoWPAN. The 6 Low-power Wireless Per-sonal Area Network (6LoWPAN) is a work group belongingto the Internet Engineering Task Force (IETF), which worksover the methods that allow to use IPv6 protocol over thebase of IEEE 802.15.4 in 30 kbytes of sensor memory.Although 6LoWPAN can work with different topologies, itnormally works with mesh networks. On the other hand, Zig-bee specifies a bundle of high level communication protocolsto be used in low consumption digital radio. It is also basedon the IEEE 802.15.4 standard. Although ZigBee is notdesigned to work over IP, the ZigBee Alliance, through theZigBee Smart Energy group, announced the ZigBee IP proto-col in order to fulfill the needs of the power market.

Conclusions for the Smart Grid USN — Figure 3 shows ourproposal for the USN access and sensor communication net-works.

Regarding metropolitan/wide area networks, wireless wide-band technology has been proposed for low populated areasdue to its easy deployment. In this way, WIMAX will workfrom the core to the high/medium voltage substations andPLC from these substations up to the homes.

With regard to the home area network (HAN), some suit-

Figure 4. Extended NGN architecture with OSE.

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able technologies, such as 6LoWPAN, IEEE 802.15.5, andZigBee, have been studied. ZigBee is the most currentlyextended and mature technology, and the one that has pre-sented the most smart grid related applications until now.Finally, the combination of PLC and ZigBee/IEEE 802.15.4gprovides a new concept of home and substation automationwith outside interaction. It has to be said that in some casesnot all these elements will be present in the network, but allof them must be integrated into the policy base management.

Smart Grid’s USN NGN LevelFor this purpose, ITU-T Recommendation Y.211 defines ageneric end-to-end architecture for the QoS resource controlin NGNs. The aim of this architecture is to provide QoS man-agement of new end-to-end services and multimedia commu-nications through diverse NGNs, even though enhancementsof the functionalities to support advanced services are stillbeing discussed. It is also important to mention the Open Ser-vice Environment (OSE) capabilities of ITU’s NGN model [9]because OSE capabilities allow the creation of enhanced and

flexible services based on the use of standard interfaces, aswell as the reuse, portability, and accessibility of services (Fig.4).

According to ITU, an NGN is a packet-based network inwhich service-related functions are independent of the under-lying transport-related technologies. It supports generalizedmobility, which will provide users with consistent and ubiqui-tous provision of services. The ITU architecture for the QoSresource control in NGNs has been developed summarizingthe local efforts of different agents in their respective fields:the Third Generation Partnership Project (3GPP), DSLForum, WiMAX Forum, and European TelecommunicationsStandards Institute — Telecom and Internet Converged Ser-vices and Protocols for Advanced Networks’ (ETSI-TISPAN’s)generic access network architecture.

The QoS control management is achieved by the central-ized management of QoS through policy-based network man-agement (PBNM) with protocols such as Common OpenPolicy Service for Provisioning (COPS-PR), from theResource and Admission Control Functions entity (RACF)

Figure 5. Middleware interaction.

Open API

USN middleware

Sensor networkdirectory service

Substation monitoringHome and building monitoring

Distributedgeneration monitoring

Securitymanager

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Integrated conrtolcentre

Substation control andmaintenanceapplication

Customermonitoringapplication

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Sensing datamining processor

Eventprocessor



(Fig. 4). The RACF carries out the resource control of thetransport subsystem within access and core networks. RACFuses reference points to manage the negotiated QoS throughthe session signaling and the flow control at a network level.

Our NGN proposal for the smart grid’s USN is based onthe use of ITU NGN architectures for the high-level manage-ment of the smart grid’s data network, including the accep-tance of traffic streams and QoS management [7]. Since thearchitecture has to work over a heterogeneous network, whichconsists of wireless and PLC nodes, the communicationdescriptors (e.g., QoS parameters or security constraints) mustbe mapped between these technologies in order to obtain suit-able end-to-end communications. This mapping scheme mustbe carried out by using, for example, our proposed communi-cation broker incorporated into the USN middleware [7].

The main advantage of the communication broker architec-

ture for communication control is that the requester nodesimply needs to specify the parameters for the communicationbroker. Internetworking among different network technolo-gies is crucial, in both smart grids and NGNs, to support suit-able end-to-end communications. The communication brokerfunction is aware of those mappings and decides whether thenetwork has sufficient resources when a new request is gener-ated, and it can also reconfigure the intertechnology parame-ter mappings on demand.

Smart Grid’s USN Middleware LevelBy definition, USN’s NGN only performs data transport.However, due to the fact that ITU’s NGN model has beenchosen by the authors, it also performs additional functions.In our case, there are many common functions between theNGN and the middleware, particularly concerning NGN’s

Figure 6. Use of COSMOS messages in the AMR use case.

Periodicreadings arestopped

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AMR application Middleware Sensornetwork

ReqConnCtrl

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OSE capabilities. In fact, the middleware is a group of logicalfunctions and business intelligence that can be implementedthrough the OSE. If the OSE does not include the appropri-ate function, it has to be independently implemented at themiddleware level.

Figure 5 shows an example of the process carried out whena smart grid application wants to reach the sensor networkthrough the middleware in order to collect data. It is basedon the scheme proposed in Fig. 2. It shows the architecturewith the applications on the top, the proposed middlewaremodel in the middle, and sensor networks at the bottom.According to the illustration, it appears that there are threeactive applications which work independently but which arecontrolled by a control center. If each application has directaccess to the sensor network, each application developershould know the details of each sensor network and theirinterfaces. However, when using the USN middleware, eachapplication developer only needs to know how to use theopen application programming interface (API). All the appli-cations always have to communicate with the middleware andthe middleware has to exchange information appropriatelywith sensor network.

In this article, the reference chosen for the middleware isETRIS’s Common System for Middleware of Sensor Net-works (COSMOS) [10], which is in its ITU standardizationprocess. Figure 6 shows an example of a use case using COS-MOS interface between the middleware and the sensor net-work. The communication between the applications and themiddleware is shown in a conceptual way, as it is carried outthrough the open API. The case shown in Fig. 6 is related toAMR. First, the connection and authentication process mustconclude. Next, the AMR application configures periodicmeter reading through the ContinuousCmd command. In thisway, the sensor network will periodically send the energy con-sumption value to the middleware. In this process, the appli-cation can interrupt the programming introduced by issuingthe CmdActionReq command.

ConclusionsThis article provides a general but complete view of the cur-rent ICT status for smart grids. In essence, the smart grid isa totally automated energy transport network, which is ableto guarantee bidirectional power and information flowsamong generation plants, final users, and applications interalia. It goes without saying that the development of thesmart grid will mean a drastic change in power use andadministration. On one hand, customers will become activeactors in energy management and will be able to controltheir consumption. Moreover, they will have new applica-tions, both inside and outside their homes, that will providethem with a higher quality of life. On the other hand, utili-ties will be able to control demand peaks and manage thegrid efficiently, from generation to distribution. For thesereasons, their communication infrastructure must beimproved.

At the moment, the definition of smart grids is a keyobjective for many countries and many entities are focusingon it, such as the SmartGrids and IntelliGrid platforms,NIST, EPRI, and the IEEE. Standards of communicationprotocols, information representation models, modulesinterfaces, and processes are crucial if smart grids are tosucceed. A communication paradigm architecture is present-ed in this article after its communication needs and require-ments have been analyzed. When studying ITU’s USNconcept and NGN, it is obvious that the need for interactionbetween different levels and the middleware should be met

in order to unify network management. USN middlewarehas been extended for security, mobility, and QoS parame-ters negotiation and configuration in order to cooperate, ina dovetailed manner, with NGN management tools likeNGN OSE.

IP plus IEEE 802.15.4g-based sensor networks are pro-posed as the USN bottom level using a mesh configurationover heterogeneous technologies. PLC technology will play animportant role in smart grid essential communications but hasto be complemented by wireless communication protocols.The resulting communication architecture must be able tointegrate whichever technology may be considered relevant byany smart grid actor. Internetworking between different net-work technologies is also very important. Heterogeneous net-work management is an active research area that must evolvein order to be applied in smart grids.

In conclusion, several trends and technology designs havebeen clearly presented in this study of the problem. The mostimportant consideration is that ITU USN/NGN have beensuccessfully adapted and applied to smart grid communicationarchitecture. This work allows us to create a unified ICTframework capable of comprehensibly supporting the strin-gent communication requirements of smart grids.

AcknowledgmentWe would like to thank EU Seventh Framework Programproject INTEGRIS (ICT-Energy-2009, number 247938) andLa Salle (URL) for their support, especially L. Kinnear forthe linguistic reviews of the article.

References[1] A. Zaballos et al., “Survey and Performance Comparison of AMR over PLC

Standards,” IEEE Trans. Power Delivery, vol. 24, no. 2, 2009, pp. 604–13.[2] EPRI, D. Von Dollen, “Report to NIST on the Smart Grid Interoperability Stan-

dards Roadmap,” 2009.[3] V. Pothamsetty and S. Malik, “Smart Grid Leveraging Intelligent Communica-

tions to Transform the Power Infrastructure,” Cisco rep., 2009.[4] Y. Kim et al., “A Secure Decentralized Data-Centric Information Infrastructure

for Smart Grid,” IEEE Commun. Mag., vol. 48, no. 11, 2010, pp. 58–65.[5] A. R. Metke and R. L. Ekl, “Security Technology for Smart Grid Networks,”

IEEE Trans. Smart Grids, vol. 1, 2010, pp. 99–107.[6] T. M. Chen, “Smart Grids, Smart Cities Need Better Networks,” Editor’s

Note, IEEE Network, vol. 24, no. 2, 2010, pp. 2–3.[7] INTEGRIS FP7 Project “INTelligent Electrical Grid Sensor Communications”

ICT-Energy-2009 call (number 247938); http://fp7integris.eu.[8] ITU-T, “Ubiquitous Sensor Networks (USN),” ITU-T Technology Watch Briefing

Report Series, no. 4, 2008.[9] ITU-T Rec. Y.2234, “Open Service Environment Capabilities for NGN,”

2008.[10] J. Wook Lee et al., “COSMOS: A Middleware for Integrated Data Process-

ing over Heterogeneous Sensor Networks,” ETRI J., vol. 30, no. 5, 2008, pp.696–706.

BiographiesAGUSTIN ZABALLOS ([email protected]) received his M.S. degree in electron-ics engineering from University Ramon Llull (URL), Barcelona, Spain, in 2000,where he is currently pursuing a Ph.D. degree in computer science. He has beenan assistant professor in the Department of Computer Science at URL since 1999and project manager of the R&D Networking Area since 2002. His research isfocused on real-time routing protocols in smart grids and sensor networks.

ALEX VALLEJO ([email protected]) received his M.S. degree in electronicsengineering from URL in 2001, and his Ph.D. degree in telecommunicationsengineering from URL in 2010. Currently, he is the manager director at MalpasIT and a part-time professor at URL. His research interests include the manage-ment of communication networks, smart grids, next-generation networks, andintelligent systems applied to networking systems.

JOSEP M. SELGA ([email protected]) received his M.S. degree from the Poly-technic University of Madrid, Spain, in 1971 and his Ph.D. degree in telecom-munications engineering from the Polytechnic University of Catalonia, Barcelona,Spain, in 1985. Currently, he is a professor at URL. He has been manager ofTelecommunications and Control Systems of the power utilities ENHER andENDESA, and President of the Technical/Regulatory Working Group of the PLCForum. His main research interest is on computer networking and the smart grid.


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