studying coronal features and their time - varying properties may allow us to investigate the relations between events ( flares , coronal mass ejections ( cmes ) , jets , bright points , etc . ) and overall solar activity ( teske and thomas , 1969 ; tan _ et al ._ , 2010 ; kilcik _ et al ._ , 2011 ) .the solar corona is characterized by a three - part structure consisting of active regions ( ar ) , the quiet sun ( qs ) , and coronal holes ( ch ) . when the sun is observed in soft x - rays and extreme ultraviolet ( euv ) wavelengths ,very bright areas cover a small fraction of the full disk .these areas are called active regions .numerous dynamic events such as flares , jets , and plasma heating occur within these areas , resulting in the release of enormous quantities of energy ( aschwanden , 2006 ; priest , 2014 ) .in addition to ars , dark areas mostly emerge near the northern and southern polar zones of the sun with plasma of lower density and an open configuration of magnetic field lines .these are termed coronal holes . because the plasma within these areas is most of the time depleted , chs appear much darker than the other parts of the sun ( ar and qs ) . here, we mainly focus on segmentations of ars and chs , and qs would be attainable as the complement of the identified regions .  the increasing amount of solar data necessitates automated methods for the detection of solar features .this need has dramatically increased in recent years . to address this need ,many types of automatic segmentations and identifications of solar features have been proposed for observations obtained with solar instruments ( _ e.g. _ , _ solar dynamics observatory _ , _ transition region and coronal explorer _ , and _ big bear solar observatory _ ) .a sample of methods based on region - growing techniques employed for extracting ars in magnetogram images includes boundary - extraction , which distinguishes between consecutive images to remove both quiet - sun and transient magnetic features .it also uses local intensity thresholding , median filtering and morphological operations at 195  and magnetograms .another method was proposed by kestener _( 2010 ) on the basis of the wavelet transform modulus maxima method . in new efforts for visual segmentation , pattern - recognition methods ( both supervised and unsupervised techniques )were developed to increase accuracy and stability .some supervised techniques were established based on bayesian classifiers and fuzzy rules . on the other hand ,the unsupervised techniques provide a focus for characterizing coronal regions consisting of a method that is based on optimization and accurate definition of clusterings in the regions of interest .another significant and perhaps the most efficient classification method to derive distinguishing properties of coronal features is the spatial possibilistic clustering algorithm ( spoca ) , which has been developed mainly to determine the boundaries of ars and chs .this algorithm uses the fuzzy c - means algorithm ( fcm ) , and a corrected version of the possibilistic c - means algorithm ( pcm ) to integrate neighboring intensity values . for detailed information about coronal image processing and visualization and comparison between both the segmentations and overall detection performances , we refer to ireland and young ( 2009 ) , revathy _( 2005 ) , and verbeeck _ et al . _( 2013 ) , respectively .  in image - processing categories ,the segmentation algorithms are generally expanded into three groups : a first group of algorithms employs region - based methods , edge - based methods are approaches used for determining boundaries , and hybrid approaches are methods that allow us to use the two algorithms mentioned above .  our proposed region - based segmentation algorithm is designed based on the bayesian framework .to do this , a posterior model is designated to labels , and in this way , we need to obtain both prior and likelihood models rooted in bayes rule .the potts model is considered as the prior model of labels .in addition , a markov - gauss model ( mgm ) and non - local means ( nl - means ) are combined to achieve a likelihood model .the idea of using these new models , which was employed in the field of noise - reduction models for the first time ( gilboa and osher , 2007 ) , is an efficient method among other recent standard developments ( dong _ et al ._ , 2011 ; werlberger _et al . _ , 2010 ; gilboa and osher , 2007 ; takeda _ et al . _ , 2009 ) .the gibbs estimator is employed to estimate labels and hyperparameters .the procedure of cellular learning automata is added to the process to sample labels .  here , we checked our code on solar data obtained from euv full - disk images ( 171  and 193 ) recorded by sdo / aia to extract ars and chs .the article is organized as follows : we describe the data in section [ data ] , explain the segmentation method applied on euv images in the bayesian framework in section [ segmentation ] , and present the results and a discussions in section [ res ] . finally , the results are interpreted in section [ conc ] .the modern space telescope _ solar dynamics observatory _ ( sdo ) was launched on february 11 , 2010 to study the solar atmosphere in many wavelengths with high temporal cadence and high spatial resolution .the sdo with the instrument _ atmospheric imaging assembly _ ( aia : lemen _ et al . _ , 2012 ) provides full - disk imaging of the sun in several uv and euv bands with a pixel size of about 0.6 . here ,sdo / aia 171  and 193  data sets are employed to test our segmentation code . as the evolution of ars and explosive events are best observed at 171  ( innes and teriaca , 2013 ; schmieder _ et al ._ , 2013 ) and chs are well - detectable features at 193  , our code is prepared to detect ars and chs at 171 and 193 , respectively .the normalization of image intensities and modification of limb brightening are applied on the dataset as preprocessing steps .  to study the physical properties of ars and chs ( _ e.g. _ , filling factors and brightness fluctuations ) ,our automatic recognition code was applied to an sdo 171 and 193  dataset recorded during 2011 , from 1 january to 31 december , taken at 13:00 ut at a cadence of one image per day .the following statistical concepts are briefly discussed . the segmentation model based on bayes rulewas employed to identify pixel labels . in the markovian process, the intensity of each pixel ( each state ) only depends on the four nearest neighbors ( the previous state ) , named the mixture of markov gauss model ( mgm ) . in the present work, the spatial dependence of pixels was considered nonlocally ( the higher order of neighborhoods ) markovian . to do this , the pixels were weighted based on nonlocal means ( nl - means ) .next , an image was defined as a lattice - like system , wherein intensity and other properties of pixels such as orientation of edges were the same as states of atoms . by assigning an energy function to the image , gibbs distributions were then computable . to model the image , a markov random field ( mrf ) was used , which is a set of random variables consisting of a markov property dissected by an undirected graph .we can explain images by an assembly of nodes corresponding to pixels .the potts model represents models for interacting spins ( equivalent to pixels ) on a crystalline lattice ( equivalent to the image ) by a simple hamiltonian .lastly , cellular learning automata ( cla ) , a mixture of cellular automata ( ca ) algorithm and learning automata ( la ) method , were employed to estimate certain pixel labels .let be the intensity of pixels in the segmentation modeling of an image .it is assumed that the image is the union of disjoint homogeneous regions ( _ e.g. , _ ars , chs , and qs ) .each region can be shown with labels represented by , in which each pixel is related to a label , and r represents the set of all pixels ( union of regions ) .the selection of labels for regions concerning the whole of the possible states , which is related to the intensity of pixels , can be interpreted as an inference bayesian problem .using bayes rule , the posterior probability distribution of labels of pixels is given as where , is the posterior distribution of labels .the unknown hyperparameters , and , are related to the image model ( or likelihood model ) and the labels model ( or prior model ) , respectively .the model has the role of the likelihood distribution in equation ( [ pospr ] ) .the image intensity within the i - th region only depends on the corresponding label . forboth the independent and identically distributed form , the image model is given as where is the set of pixels within the -th region .the parameters and are the mean and the variance of the pixel values in the k - th region , respectively . starting with the pioneering work of ayasso and djafari ( 2010 ) ,the mgm is defined by the parameter is defined as follows :  [ lmu ] _ k(*r * ) m_k , & if , + _ * r*_*r * x_*r * , & if ,  where is the set of the four nearest neighbors of pixel . the parameter is representative of contours , wherein is the delta dirac function . for pixels located within contours , the value of equals 1 ; otherwise , it is set to zero .each pixel is considered a markov random variable , where the central pixel is the new state of markovian process , and neighboring pixels are the previous state of markov .equation ( [ lmu ] ) shows that the mean value of adjacency connectivity is not used to avoid edge smoothing within the contours .  in the present model ,the nl - means are used based on nonlocal information of weighted pixels .these weights are computed using the similarity criterion defined by gestalt - grouping principles . in the nonlocal method ,all image pixels can be considered as neighboring pixels .the nonlocal mean of an image is given by where is the normalization factor and is the weighting factor between the central pixel and the neighboring pixels gained by criteria including brightness similarity and geometric proximity .  by using the mean of nonlocal weights instead of the local mean in equation ( [ lmu ] ) ,the image model is modified as  [ wmu ] _k(*r * ) m_k , & if , + _ * r*_*r * _ * r*,*r*x_*r * , & if .  thus , the image intensity model is represented as where is the constant that controls the influence of the likelihood model on the output . by increasing , the value of likelihood probability decreases , and _vice versa_.   we employed an mrf framework to obtain different prior spatial models to model the labels .mrf consists of a set of random variables such that every one of the random variables arises from a discrete set  .each random variable and its neighborhoods is considered a clique .the probability distribution of is included in the gibbs distribution as follows : the energy function and the probability value correspond to different configurations of  .the random variables are considered as labels corresponding with regions . using the potts model , the energy functionis defined as  where is a coefficient that controls the rate of smoothness ( the amount of connections created among labels ) . using equations ( [ pf ] ) and ( [ uz ] ) ,the probability function of the potts model can be expressed as if the smoothness in segmentation increases , the probability value shows the growth in its distribution . choosing a greater value for causes loss of segmentation details , and the selection of lower values may lead to increased noise .for nonlocal information , the probability can be expressed as    the labels of pixels and hyperparameters can be obtained by solving the following equation where represents the probability model of the set of unknown parameters . using gibbs sampling , and are obtained in the iteration loop as follows  according to this sampler , the -th sample of is obtained from equation ( [ zmcmc ] ) .then , we use it in equation ( [ tmcmc ] ) to sample , and _ vice versa_. this iterative way must be continued until is related to the precision of segmentation . for more details about estimating all the unknown parameters we refer to humblot and djafari ( 2006 ) , and murphy ( 2007 ) .the cellular learning automata method , a mathematical model for complex dynamical systems consisting of numerous simple elements , was exploited to estimate labels .each cell sends an output ( action ) to its surroundings and then receives an input response .the probability of choosing each action in the -th iteration is expressed by the action probability . in other words ,when the cell receives a desired response input , the action for automaton , which is denoted by , is rewarded ; for another possible input , it is penalized . in cla ,input is determined by the current output ( action ) of neighbors of each cell . here , pixels are equivalent to cells , and actions are comparable with labels . for the label of each pixel ,all probabilities of the number of actions applied to each pixel are calculated . in other words , the greater the value of action probability of each label , the higher the selection probability of the label .the label of each pixel in each iteration is attained by where the function indmax returns the index corresponding with the highest value that can be interpreted as the label of the pixel , and is a random number ranging from 0 to 1 assigned to -th selection probability .the potts model can be regarded as a ca that is able to specify the posterior probability of labels for each cell based on two criteria : markov dependence ( image model ) and similarity between each cell and its neighbors ( labels model ) . on the basis of the mentioned assumptions, we must consider the desired input response ( label ) , obtained from the action increasing the posterior probability ; on the other hand , reduces the probability .  in -th iteration ,the probability related with the value of the label is reinforced .thus , the probability related to is reinforced in matrix ( a matrix in which the element shows the assignment probability of the -th label to the -th pixel ) . by computing , the final label , is given as proposed method was applied to solar data that have mainly been recorded at two wavelengths ( 171 and 193 ) .it is a well - established fact that ars can be identified better at 171 , and on the other hand , the preferred wavelength for ch recognition is 193 . to improve image segmentations , we here extracted the optimized values for parameters and specified in equations ( [ nlmgm ] ) and ( [ finalbeta ] ) , respectively .we determined regions in one computational loop with iterations with the condition given in equation ( [ epsilon ] ) . here, we decided that the parameter equals 30 pixels for images of size 1024 1024 pixels to obtain great precision .figure [ fig2 ] shows different numbers of iterations applied to 171  data to segment ars .it can be seen that ars are gradually recognized in higher - order iteration .certain performances of both ar and ch identification reach their final value in the 400-th and 1200-th iteration , respectively .  in the next step ,200 images recorded ( from 2010 to 2013 ) at two wavelengths were randomly selected .ars and chs were manually extracted from 171 and 193  images , respectively .the code with different values of and was applied to these data to distinguish regions .the two segmented images ( manual selection approach and proposed method ) were compared on a pixel - by - pixel basis , measuring the proportion of equally segmented pixels .for example , if a pixel is segmented as an ar in both results , it is assigned a score (= 1 ) ; if a pixel is segmented as non - ar in both results , it is assigned a score (= 1 ) ; if a pixel is segmented as ar in one result and as non - ar in the other result , it is assigned a score (= 0 ) .then , the summation of scores is divided by the number of pixels of the disk .figure [ fig3 ] demonstrates the mean value of the summations for 100 images at each wavelength for different values of and .since this segmentation method is the result of a random markovian process , some negligible changes appeared in the resulting segmented images in each running code .the optimal values for the set ( , ) are obtained to equal ( 0.13 , 1.81 ) and ( 0.97 , 0.31 ) for ars and chs , respectively .  to estimate the reliability of the algorithm and to compare it to the spoca results , we picked up full - disk sdo / aia images recorded on 25 june 2011 ( 13:00:36 ut ) and 28 july 2011 ( 13:00:37 ut ) taken at 171 ( figures [ fig4]a , and [ fig5]a ) and 193  ( figures [ fig4]b , and [ fig5]b ) .first , we manually extracted ars ( figures [ fig4]c , and [ fig5]c ) and chs ( figures [ fig4]d , and [ fig5]d ) . for comparison , the performance of the spoca results in the hek catalog represented at 171  ( figures [ fig4]e , and [ fig5]e ) and 193  ( figures [ fig4]f , and [ fig5]f ) are shown . then , our method was applied on these two wavelengths to extract ars ( figures [ fig4]g , and [ fig5]g ) and chs ( figures [ fig4]h , and [ fig5]h ) with the optimal values of ( , ) .  to exclude bright features appearing as bright points , very slender coronal loops resolving out of ars , etc ., the minimum sizes for ars and chs were considered to be 1400 and 3000 arcsec , respectively , close to the values in verbeeck _ et al . _the method clearly is able to separate regions of interest with high precision .figure [ fig6 ] presents more samples at 171  ( first row ) and 193  ( third row ) .the recognition of ars and chs are displayed with red ( second row ) and green contours ( last row ) , respectively .columns are representative of data recorded on 22 january 2011 ( left column ) , 24 february 2011 ( middle column ) , and 31 march 2011 ( right column ) .  to investigate the statistics of coronal regions , the code was applied to a dataset of 12 months ranging from 1 january to 31 december 2011 . at 171 and 193  an image for each day at each wavelength was selected .the filling factors of ar , ch , and qs are shown with mean values of 0.032 , 0.057 , and 0.911 , respectively ( figure [ fig7 ] ) .the fluctuations ( the solar rotation of about 27 days ) are compatible with corresponding results obtained by verbeeck _( 2014 ) for 2011 . the total number of ars and chs extracted by our code for 2011 equal 3580 and 3275 , respectively .the number of ars and chs on each day with the mean value of 9.8 and 8.9 , respectively , are shown in figure [ fig8 ] .the correlation ( pearson ) between these two time series is about zero .brightness fluctuations of ars , chs , and qs with their variances during 2011 are illustrated in figure [ fig9 ] .the mean brightness value of ars is 20 and five times as much as that calculated for chs and qs , respectively .the variance in the brightness of ar is about 200 times more than that computed for the brightness of qs , which is expected to be indicative of their activity .the size frequency distribution of ars ( figure [ fig10 ] , upper panel ) and chs ( figure [ fig10 ] , lower panel ) are displayed in log - log scale .we used a data - based method to determine the optimal bin number within histograms .this optimization method is based on the minimizing loss function between the histogram and an unknown density function by adopting the mean integrated squared error .we used the maximum likelihood estimator method to compute the power - law exponents of size frequency distributions .the power - law exponents , which is shown as slope of linear fit ( dashed line ) in figure [ fig10 ] , are equal to -1.597.025 and -1.508.028 for ars and chs , respectively , with 97 confidence levels in fitting .the scatter plots of ars ( figure [ fig11 ] , left panel ) and chs ( figure [ fig11 ] , right panel ) with mean values for each bin ( 01800 arcsec , 18003600 arcsec , _ etc ._ ) are presented .both small ars and chs have excessive scatter in brightness .  to check the influence of noise on the segmentation of data taken on 6 february 2013 ( 12:08:30 ut ) with the optimized values of ( , ) , was used the zero - mean gaussian noise .the signal - to - noise ratio ( s ) can be obtained by where and are the standard deviations of the pixel intensity and noise , respectively . in a similar manner , s is formulated in decibels as to evaluate the segmentation error , we calculated the percentage of pixels that were differently classified in the case of artificial noise , when compared to the segmentation without noise in the same way described above for figure [ fig3 ] .thus , the segmentation error for ars ( figure [ fig12 ] , left panel ) and chs ( figure [ fig12 ] , right panel ) , which was applied to data recorded on 6 february 2013 in different values of s was calculated when zero - mean gaussian noise was added to the data . as expected , the influence of noise on the segmentation of ars is weaker than that of chs .we can see that in the lower values of s , the result of segmentation is unpredictable .the filling factors of ars ( figure [ fig13 ] , left panel ) and chs ( figure [ fig13 ] , right panel ) within 50 images , which were selected randomly from 20102012 , were extracted using both a manual selection approach and our automatic method for the optimized values of noted in figure [ fig3 ] . by comparing these two results on a pixel - by - pixel basis , we noted some false - positive detections .here , we presented a hybrid algorithm defined in a bayesian framework based on the mgm and potts model . then , cellular learning automata were employed to sample labels from their posterior probability .the proposed algorithm was applied to coronal full - disk sdo / aia data to subdivide areas into regions of interest . by applying this code to various types of solar data on different days, its flexibility and capability in separating regions were tested .  in figure [ fig11 ]we show that chs with sizes smaller than 5000 arcsec are more scattered in brightness , and for large sizes , fluctuations in brightness decrease . for small ch regions , the average brightness increases .this shows that the influence of the border pixels that are located next to the qs and especially in the vicinity of the ars becomes important . for large ch regions, the average brightness shows less scattering .most probably , the false - positive detection error of the code ( figure [ fig13 ] , right panel ) affects the average value of brightness for some ch regions .as expected , the segmentation of chs is more sensitive to noise than the segmentation of ars .therefore , it is necessary to take preprocessing steps before recognizing chs .this method can be improved to restore and segment simultaneously to retrieve coronal regions .it will also be better to revise our code to use two or three wavelengths to characterize ars and chs .  in the future, we will try to establish a reliable criterion for determining border pixels of ars and chs , based on mean and standard deviation .moreover , we intend to revise this algorithm to extract regions of interest from photospheric and chromospheric data. then , we aim to investigate the evolution and variations of different types of regions in a number of wavelengths in half a solar cycle .the authors acknowledge the spoca group : c. verbeeck , v. delouille , and b. mampaey for making spoca results publicly available and the hek team for providing spoca detections in the hek .the authors also thank the anonymous referee for meticulous comments and suggestions .altschuler , m.d . ,trotter , d.e . ,orrall , f.q . : 1972 , _ solar phys . _ * * 26 * * , 354 ( doi : 10.1007 ) .aschwanden , m.j . : 2006 , _ physics of the solar corona _ , 2nd edn . ,praxis publishing , springer ( doi : 10.1007 - 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Abstract: the solar corona is the origin of very dynamic events that are mostly produced in active regions ( ar ) and coronal holes ( ch ) . the exact location of these large - scale features can be determined by applying image - processing approaches to extreme - ultraviolet ( euv ) data .   we here investigate the problem of segmentation of solar euv images into ars , chs , and quiet - sun ( qs ) images in a firm bayesian way . on the basis of bayes rule , we need to obtain both prior and likelihood models . to find the prior model of an image , we used a potts model in non - local mode . to construct the likelihood model , we combined a mixture of a markov - gauss model and non - local means . after estimating labels and hyperparameters with the gibbs estimator , cellular learning automata were employed to determine the label of each pixel .   we applied the proposed method to a _ solar dynamics observatory/ atmospheric imaging assembly _ ( sdo / aia ) dataset recorded during 2011 and found that the mean value of the filling factor of ars is 0.032 and 0.057 for chs . the power - law exponents of the size distribution of ars and chs were obtained to be -1.597 and -1.508 , respectively , with the maximum likelihood estimator method . when we compare the filling factors of our method with a manual selection approach and the spoca algorithm , they are highly compatible .


one of the biggest technological challenges that characterizes major research and development work programmes , such as horizon 2020 , is to overcome the fragmentation of vertically - oriented closed systems , architectures and application areas so as to move towards open systems and platforms that can support multiple applications .the scope is to create an ecosystem composed of devices and services that satisfy crucial requirements such as interoperability , re - usability , scalability and reliability .these requirements are also looked for in the specific context of smart grid ( sg ) . among the key elements of their vision ,the smart grid s strategic research agenda of the european union and the national institute of standards and technology ( nist ) , identified the need to ensure a successful interfacing of grid equipment between vendors so as to ensure interoperability and integration of the most diverse applications ( e.g. distribution system state estimation and smart home enegy management ) .  as a matter of fact , so far only closed systems relying on a poorly interoperable approach have been used in power systems .thus , actions such as data collection , storage , access , analysis and visualization have been run independently by involved stakeholders , causing several drawbacks : managing resources and data in a closed manner limits new and innovative applications exploiting available resources and integration with cross - domain data ( e.g. weather data ) ; in a power system with new and updated components such as distributed energy resources ( ders ) , timely adaptation is compulsory for the correct functioning of the system ; with the massive amounts of data and information expected to flow with the implementation of sg technologies , data collection and analysis centers become points of criticality in case the necessary infrastructure is not timely scaled ; since the number of involved components and actors in the sg domain is growing exponentially , the need for a scalable and programmable environment allowing different views of the system is arising , which is currently not satisfied ; considering the uncertainty and variability of future power systems , applications need to dynamically adapt their computational power to changes which may occur ( e.g. in critical situations ) . in light of these considerations, we provide our contribution by proposing a cloud - based virtualization middleware for sg applications so that a digital counterpart of real entities is created allowing for representing components of various nature ( smart meters , phasor measurement units ( pmus ) , concentrators , circuit breakers , servers and many more ) into a programmable environment .this proposal can be seen as the first step towards : extending the functionalities , the features and the capabilities offered by entities and devices in various domains ; transforming them into services which share functionalities with different stakeholders and give different views depending on permission ; composing their capabilities independently from the physical component in order to allow more value - added application innovations . moreover , virtualizing physical resources allows for bringing services to the cloud .the use of cloud computing for sg applications allows for taking advantage of both elastic storage and computational capabilities as well as a cheaper infrastructure from both a capital expenditure and an operating expenditure perspective .furthermore , robustness to outages or failures in a region is provided with geographic replication .last but not least , in this paper communication with virtual entities and physical objects take place using a restful approach , which guarantees a simple yet highly - performing interfacing between system components .the remainder of the paper is organized as follows . in section [ sec : virt_sg ] , the key features of the virtualization framework and the functional blocks of the platform are presented .section [ sec : case_study ] presents a case study implemented in a cloud - based internet of things ( iot ) platform named lysis in order to show the advantages of the proposed architecture .section [ sec : res ] discusses the advantages brought by the proposed architecture .section [ sec : concl ] concludes the paper and discusses future work .in line with the reference smart grid architectural model ( sgam ) developed by cen , cenelec and etsi , and exploiting the in the iot field  , the virtualization platform is organized in three levels :  * a * virtualization layer * made of so called virtual objects ( vos ) that abstract real world objects ( rwos ) such as intelligent electronic devices ( ieds ) but also process related components needing some kind of interface to the digital world ( e.g. wind turbines ) , thus hiding the underlying technological heterogeneity of rwos ; * an * aggregation layer * made of so called micro engines ( mes ) that are cognitive mash - ups of semantically interoperable vos created with the aim to aggregate vos in order to accomplish a certain high - level task and give different views to the application level depending on applications permissions ; * a * service layer * , which has the role to organize the application requirements into services to be fulfilled thanks to mes .  the use of such three - layered solution  .however , its application to the sg domain has never been fully exploited . in the following ,we introduce different functional blocks composing the overall virtualization framework ( fig . [ architecture ] ) that satisfy the specific requirements of the smart grid .a vo is an abstract description that virtualizes one or more rwos , so that any application can access it or its functionalities in an interoperable way , without knowing about the means that are needed to physically reach and retrieve information from it .the scope is to help building mes , which comprise one or more vos , as well as some additional functionalities .thus vos help in rwo functionalities abstraction through a uniform way of representing data .for this reason , vos are reusable by different mes and are permanently and continuously active , monitoring and reacting to the needs coming from the aggregation level as well as to changes in the rwos .this helps the application developer to build aggregated functions very easily .for example , both a smart meter and a pmu are able to provide data about the power flowing on a given branch . by means of a virtualization layer ,these measures are decoupled from the physical object and presented as data described by metadata such as timestamp and quality of the measure .must also provide a northbound interface to the aggregation level , so that communications with the higher levels of the virtualization platform is put in place .herein , all the functional blocks composing the virtualization layer are presented and their role and responsibilities are extensively described .  ** hardware abstraction layer ( hal ) * is the interface between real world and vos which can either reside on the physical object as northbound interface , or in the cloud as southbound interface of the vo , depending on the rwo characteristics and capabilities .hal translates specific vendor functionalities of rwos into a platform - understandable language .this block is of utmost importance , since it is the only one interfacing with the real world and speaking the rwo specific language .moreover , it provides a standard communication with the virtualization level by means of rest apis ( which are better explained in the vo execution container part ) , and a standard representation using key - value pairs in json format . doing so , the virtualization platform becomes resilient to components evolutions , since rwo representation at the virtualization level is standard .* * vo execution environment * represents a virtual environment at the virtualization level , which is most likely owned by the same owner of the physical object , and made of three blocks : * * * vo execution manager * is responsible for the management of the vo .it monitors state , qos parameters as well as connectivity with the rwo and reacts to changes ( e.g. a change in the qos ) by updating important information about the vo status in the vo registry and alerting the me if needed . ** * vo access controller * deals with mes permissions to access the vo and manages access conflicts that may arise among mes ( e.g. giving priority to a given me dealing with safety critical operations over one which optimizes demand response ) . * * * vo execution container * manages actuation and data retrieval from in terms of which data need to be retrieved as well as how often they need to be retrieved . depending on the characteristics of rwos and on the needs ,vo can either receive periodic updates from the rwo automatically or request them .the kind of interfacing used depends on the specific rwo and on the hal .same considerations can be done for the actuation . ** vo management environment * is the part of the platform that manages registered vos .it is made of two components : * * * vo registry * keeps track of the description ( vo identifier , location , offered functionalities , etc . ) and status of all the running vos .therefore , each time the status of the vo changes , the registry is updated by the vo execution manager using the http post method . * * * vo request manager * is the block dealing with the phase in which requests are received to compose functionalities at the aggregation level .the vo request manager is responsible for searching available vos in the vo registry based on the requirements coming from the me request & composition manager ( see next subsection ) and the permissions which the requester has with regard to the available vos , which are granted with a key .here we suppose three kind of permissions : a vo can be freely accessible if public , accessible only by authorized parties with a _ friend key _ , accessible only by its owner with a _private key_. the vo request manager is also responsible for updating the vo access controller about new mes that are granted access to the vo .a me comprises multiple vos in order to achieve a specific functionality but it is more than just the aggregation of its parts . while vos provide a well - defined set of fine - grained functionalities that are abstracted from rwos and provided for reuse , the task of the aggregation level is to use these fine - grained functionalities and provide higher - level functionalities required by one or more services .moreover , different mes may use the same set of vos to provide different views to the service level which are compliant with stakeholders permissions and ensure proper privacy and confidentiality  .for example , if a number of smart meters pertaining to a certain zone are considered , a me can provide consumption data which are aggregated over households but fine - grained over time for the sake of operational purposes . on the other hand ,another me may provide consumption data aggregated over a certain period ( e.g. one month ) but fine - grained for each household thus providing the necessary information for billing purposes .therefore , the aim of mes is to effectively link the service level and virtualization level by providing at the same time the glue intelligence and separation needed .notice that while vos are entities that represent a fixed and always - on interface to the rwo , mes can be dynamically modified in order to satisfy service requirements . here, we define the functional blocks that can be identified at the aggregation level .  ** me execution environment * are made of three blocks : * * * me execution manager * is the counterpart of the execution manager at the vo level .it keeps track of the status of the me reacting to events that may change mes status thus needing a specific action .in particular , it monitors requirements dictated by service level and uses self - management functions to meet those requirements and react to changes ( e.g. asking the me request & composition manager to reconfigure the me by choosing an alternative set of vos when service requirements can not be met ) . * * * me access controller * deals with permissions to access the me similarly to what has been presented for the virtualization level .moreover , it has a key role when a me instance is jointly used by multiple services .in fact , different services may affect the same resources operating simultaneously .this block resolves undesired conflicts and system instabilities . * * * me execution container * is the execution environment of the me instances in which data coming from the virtualization level are received , parsed and stored or sent to the necessary destination .moreover , commands coming from the upper layers are passed to the appropriate vo through this block . in other words , it takes care of actuation and data retrieval from vos , which data need to be retrieved and how often .me can either receive periodic updates from the vo automatically or request them with a get http method . in the latter case, the get method will specify all the parameters necessary for security as well as query related parameters ( e.g. for billing purposes , data about household consumption on a given interval ) . in the former case ,data is received automatically in a periodic or triggered - based manner , and the content of the updates as well as its frequency can be modified .in particular , the vo will send an http post method containing all the as well as commands such as ` start periodic updates ' , ` stop periodic updates ' , ` change information of the periodic updates ' .actuation commands take place in a similar manner . ** vo management environment * is the part of the platform at the aggregation level which is made of two components : * * * me registry * contains information for each me such as me identifier , vos connected to the me and many more .thus the focus of the me registry is to keep track of all mes . * * * me request & composition manager * handles new requests coming from a given service manager , verifies the key granting access permission and performs a search in the me registry in order to find the needed me .then , it communicates to the service manager how to reach the considered me and updates access changes in the me access controller .this block is also responsible for vo composition changes requested by the me execution manager .the service layer consists of functions ( i.e. services ) that enable sg use cases .a service represents a logical entity which performs a dedicated application task ( e.g. visualization of household consumption with a dedicated application ) .* * service request manager * is responsible for providing requests to the aggregation level in the boot process of a new service .* * service execution manager * is responsible for managing and supervising the execution of the service .in this section , the implementation of the proposed architecture is described for the case study of two smart homes , from which data about the consumption , the generation of der and realtime weather is provided ( fig .[ casestudy ] ) . for the simulations , we rely on the open umass smart * home data set of which is provided in the form of csv files . in particular , home b and c of the data set have been considered . for our simulations ,these files have been uploaded in 5 different raspberry pi 2 model b development boards , which can nevertheless be used in a real scenario , considering their capability to implement the needed hal for objects like microturbines and pv panels , as well as the interface to sensors such as temperature and humidity sensors .this represents our real world level . concerning the implementation of the virtual entities in the cloud, google app engines have been used . provide a complete platform as a service ( paas ) in which the various components have been deployed .let us now describe the role of each real and virtual entity , its accessibility ( private , friendship - based , public ) and its ownership .the next section will leverage on this discussion to highlight the properties claimed in the introduction .three kinds of rwos are considered here .weather units are supposed to be owned by resident of the home in which the unit is installed .therefore , also the corresponding vos have the same owner .these rwos provide the following measurements : timestamp , outside temperature , outside humidity , wind speed , wind direction , wind gust , heat index .in addition , the weather unit in home b also provides inside temperature and humidity .this data is sent to the corresponding vo every minute .[ fig : lysis ] shows how the registered vos appear in the platform .concerning sm and der , currently there is a lot of discussion about whether the customer , the utility or a third party should own them . here , for the sake of generality we consider both of them to be owned by a third party actor .therefore , also in this case the vos representing these physical objects have the same owner of the physical object .every minute , sms send information about consumption in kwh and the corresponding timestamp .der is composed of two wind turbines and 3 photovoltaic panels plus a battery . therefore , each minute information about power generation and about the battery level are sent to the corresponding vo .let us now analyze the aggregation and service level .home b info _ is a me which is owned by dwellers and gathers data about consumption and from the weather unit .access to the sm is guaranteed by the friend key provided by the metering operator , while weather home b is accessed with the private key .in fact , while we suppose weather units to be publicly accessible , the private key allow to receive privacy - critical information such as inside temperature and humidity , which are not provided with the public access .these information are provided in a home info visualization service which can be used by dwellers or dwellers friends which possess a friend key .the _ gen&load _ and _ 1 month cons _mes provide respectively aggregated generation and load information on a 1-minute basis and information about monthly consumption for each customer .these are provided by the third party managing sms and ders thus guaranteeing the needed level of privacy .services such as distribution automation of distribution system operators and billing of energy retailers can access this information with a friend key and use the gathered information for their own purposes .finally , public information gathered from the weather unit is aggregated in a _ weather _ me which , for example , could be owned by the municipality in order to provide open data for local weather forecast services or for distribution automation services which exploit weather conditions .  to demonstrate the adaptability property of the cloud ,the umass smart * microgrid data set of has been used , which comprises sm consumption of 400 homes in the same period . here , we assume all 400 sms sending an http post to the virtual entity in the cloud , at the beginning of a new minute .[ fig : microgrid ] shows how the performance changes with the number of instances .instances are of the virtual entity which can be created in order to satisfy high loads .this process is not visible to the senders , which utilize the same address. the number of instances can be statically or dynamically set .if dynamically set , parameters such as the minimum and maximum number of instances and triggers for scaling up and down the number of instances must be specified . from fig .[ fig : microgrid ] it can be seen that the bigger the number of instances , the better the performance of the virtual entity in satisfying http requests . in our particular case studythis is important , since data for operational purposes must be updated each minute and using an instance means to delay data acquisition by almost seconds . finally , we have also simulated the case in which instances are dynamically scaled in order to reduce costs in a pay per use paas .in particular , instances were allowed to scale from a minimum of zero ( idle ) to a maximum of 10 .results show that at boot ( 1st minute ) the performance is initially bad but rapidly improves as soon as the instances are created . on the other hand , in all the following minutes ( )the performance is comparable to the one for the same static number of instances .in this section , the properties which result from the deployment of the considered virtualization middleware in the considered case study are analyzed .   in the proposed case study ,weather data is of big interest for : the owner of the house , who would like to know the weather at her dwelling location ; the distribution operator , which will combine this information with energy generation and consumption for distribution optimization ; the municipality , which can exploit various sensors across the city in order to provide open data .therefore , various actors can benefit from the same physical objects with just one of them needing to install and maintain sensors .the two weather stations in home b and c have different properties , since only the first one can provide also data about indoor temperature and humidity .nevertheless , these differences are not perceived on the aggregation level , since the particular properties of each rwo are decoupled at the virtualization level . butintegration also means putting together data from different domains such as weather and energy consumption , which boosts the creation of new services such as distribution automation .different services have really diverse and sometimes variable computational needs .leveraging on cloud services such as google paas allows to overcome these problems in an affordable way , since resources can be easily scaled and paid per use .the use of keys to access data allows for reusing data coming from the same rwo without compromising privacy . in our case study, the sm can be used both for network operational management and for billing purposes . as a matter of fact , by using access keys and mes , it is ensured that actors will gather data with no more than the granularity ( in terms of time or in terms of sms data aggregation ) which has been granted to them .in this paper an internet of things platform which is cloud - based and relies on a restful interfacing has been presented . this middleware allows to break vertical domain silos of individual applications , thus making sg an integral part of the internet of things .presented architecture satisfies compulsory requirements for smart grid deployment such as reusability , integration , adaptability , security and privacy .the core rationale of this paper is to present the concept and functional blocks behind the idea of a virtualization platform with restful interfacing .the scenario presented completes the core idea by giving a practical case study in which it can be used .details about apis have been voluntarily omitted for the sake of brevity . in a extended version of this paper, we would like to present more application scenarios and give extensive information about how to create vos starting from devices with rest capabilities and about apis which are used to access vos we created .the goal is to realize a set of vos that can be used and exploited by anyone interested , in order to advance research .ec , secure , clean and efficient energy draft work programme " , dec . 2014 .[ online ] .available : http://ec.europa.eu/research/horizon2020/pdf/work-programmes/secure_clean_and_efficient_energy_draft_work_programme.pdf .icore , architecture reference model " , 2013 .[ online ] .available : http://www.iot-icore.eu/attachments/article/89/20130228 icore d2.3 final v2 reformatted.pdf[http://www.iot-icore.eu/attachments/article/89/20130228 icore d2.3 final v2 reformatted.pdf ] .j. gubbi , r. buyya , s. marusic , m. palaniswami , internet of things ( iot ) : a vision , architectural elements , and future directions " , future generation computer systems , vol .29 , n. 7 , pp .1645 - 1660 , sep . 2013 .z. erkin , j. troncoso - pastoriza , r. lagendijk , and f. perez - gonzalez , privacy - preserving data aggregation in smart metering systems : an overview , signal processing magazine , ieee , vol .2 , pp . 7586 , march 2013 .e. mckenna , i. richardson , and m. thomson , smart meter data : balancing consumer privacy concerns with legitimate applications , energy policy , vol .807 814 , 2012 , modeling transport ( energy ) demand and policies .s. barker , a. mishra , d. irwin , e. cecchet , p. shenoy , and j. albrecht , smart * : an open data set and tools for enabling research in sustainable homes . 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Abstract: currently , one of the hottest topics in the internet of things ( iot ) research domain regards the issue to overcome the heterogeneity of proprietary technologies and systems so as to enable the integration of applications and devices developed for different environments in a single interoperable framework . towards this objective , virtualization is widely used to foster integration and creation of new services and applications . similar benefits are expected by its application in the smart grid arena . the use of a virtualization middleware is expected to enrich the capabilities and opportunities related to the use of smart grid devices . in this paper , we propose key features for an interoperable , re - usable , elastic and secure smart grid architecture which is cloud - based and relies on rest apis . to highlight the benefits , a practical case study is presented , where outlined functionalities are implemented in a cloud - based iot platform . the advantages brought by the proposed middleware in terms of interoperability , re - usability , privacy and elasticity of the underlying infrastructure are discussed .   internet of things , smart grid , virtualization , cyber - power system , cloud computing , restful .