A Statistical Priority-based Scheduling Metric for M2M Communications in LTE Networks

Transcription

1 1 A Statstcal Prorty-based Schedulng Metrc for M2M Communcatons n LTE Networks Ahmed Elhamy Mostafa, Student Member, IEEE and Yasser Gadallah, Senor Member, IEEE Abstract Resource allocaton, or schedulng, s one of the man challenges that face supportng Machne-to-Machne (M2M) communcatons on Long Term Evoluton (LTE) networks. M2M traffc has unque characterstcs. It generally conssts of a large number of small data packets, wth specfc deadlnes, generated by a potentally massve number of devces contendng over the scarce rado resources. In ths paper, we ntroduce a novel M2M schedulng metrc that we term the statstcal prorty. Statstcal prorty s a term that ndcates the unqueness of the nformaton carred by certan data packets sent by Machne-type Communcatons Devces (MTCDs). If an MTCD data unt s sgnfcantly dssmlar to the prevously sent data, t s consdered to carry non-redundant nformaton. Consequently, t would be assgned hgher statstcal prorty and ths MTCD should then be gven hgher prorty n the schedulng process. Usng ths proposed metrc n schedulng, the scarce rado resources would be used for transmttng statstcally mportant nformaton rather than repettve data, whch s a common stuaton n M2M communcatons. Smulaton results show that our proposed statstcal prorty-based scheduler outperforms the other baselne schedulers n terms of havng the least number of deadlne msses (less than 4%) for crtcal data packets. In addton, our scheduler outperforms the other baselne schedulers n non-redundant data transmsson as t acheves a success rato of at least 70%. Index Terms Internet of Thngs; LTE; Machne-to-Machne Communcatons; Statstcal Prorty; Uplnk Schedulng T I. INTRODUCTION he Internet of Thngs (IoT) s consdered the network of the foreseen future [1]. It s the network through whch all objects (thngs) wth communcaton capabltes are connected to acheve certan goals wth mnmal human nterventon. Machne-to-Machne (M2M) communcatons [2] s one of the man IoT enablng technques. The devces that are nvolved n M2M communcatons, or Machne Type Communcatons (MTC), are usually called Machne Type Communcatons Devces (MTCDs). Unlke Human-to- Human (H2H) communcatons, M2M communcatons are generally characterzed wth massve access, combned wth small data payloads. MTCDs' data can be generated by event trggerng or n the form of perodc reports. M2M communcatons are used n a wde varety of applcatons ncludng envronmental montorng (e.g. temperature, pressure, etc.), survellance (e.g. securty cameras), alarm systems (e.g. fre alarms), statstcal survey and countng systems (e.g. people and vehcle countng), ntellgent transportaton systems, healthcare, farmng and ndustral producton lnes. M2M communcatons are expected to domnate traffc n cellular networks n 5 th generaton (5G) tme frame and beyond [3]. The number of MTCDs s expected to reach 3.2 bllon by year 2020 [3]. Long Term Evoluton (LTE) s now seen as the best technology to support MTC due to ts Internet compatblty, hgh capacty, flexblty n rado resources management and scalablty. One of the man challenges n adoptng LTE for M2M communcatons s the problem of rado resource management or schedulng. Exstng H2H LTE uplnk schedulng algorthms [4] that focus manly on throughput maxmzaton and preservng the contguty of rado resources that are assgned to a certan devce are not effcent to use wth MTC. Ths s due to the fact that M2M communcatons have dfferent characterstcs when compared to H2H communcatons. MTC traffc conssts of manly small bursty payloads that exst mostly n the uplnk drecton (.e. from a devce to the servng base staton). MTCDs are also used n a wde varety of applcatons. Each applcaton has ts own requrements that may nclude specfc Qualty of Servce (QoS) level, energy consumpton mnmzaton, or data transmsson deadlnes. A deadlne n ths context s the tme by whch data must be transmtted to avod unwanted consequences e.g. n the case of emergency alerts. Snce rado resources are lmted for massve M2M communcatons, the schedulng algorthm should manly consder the mportance of the nformaton carred by the data traffc of the dfferent MTCDs. The data reported by many montorng devces are repettve n many stuatons. Ths means that they do not necessarly carry hgh-value nformaton all the tme. For the data to be consdered of hghvalue (.e. unque data), the nformaton should dffer sgnfcantly from the latest communcated data by an MTCD. Ths means that value smlarty s low or, n other words, the dfference between current and prevous data unt values exceeds a mnmum threshold. In addton, data are also consdered to have hgh-value nformaton f they show a constant ncreasng or decreasng trend wth prevous data,.e. hgh trend smlarty. Ths consstent trend may ndcate a problem e.g. the consstent ncrease of temperature nsde an ndustral system, whch may ndcate a possble fre or exploson and hence requres emergency procedures to be ntated. Furthermore, the data sent by an MTCD are consdered valuable f they fall outsde an expected range of

2 2 values (.e. upper and lower thresholds) snce ths may ndcate abnormal condtons. The mportance of data also ncreases f data have small tme-autocorrelaton (.e. less correlaton wth prevous data). An M2M schedulng algorthm should gve data wth hgh-value nformaton hgher prorty compared to redundant data. In order to address ths problem, there s a need to fnd a method to defne and quantfy the mportance of nformaton carred n the data sent by MTCDs. In ths paper, we propose a novel schedulng metrc that quantfes data mportance usng the aforementoned statstcal attrbutes of data such as value smlarty, trend smlarty and tme-autocorrelaton. We term ths metrc the Statstcal Prorty. We use the statstcal prorty value as a schedulng metrc and prove ts effectveness for M2M communcatons when rado resources are lmted (.e. rado resources are not suffcent to send all data). We have two man contrbutons n ths paper: We propose a novel schedulng metrc statstcal prorty to quantfy the mportance of nformaton carred n the data to be sent by MTCDs. Ths metrc s calculated based on statstcal attrbutes of the data such as value smlarty, trend smlarty and tmeautocorrelaton. We use the statstcal prorty metrc as the bass of a schedulng algorthm to allocate the scarce rado resources to MTCDs based on the mportance of nformaton carred n ther data. The rest of ths paper s organzed as follows. In Secton II, the necessary background of M2M communcatons and M2M uplnk rado resource schedulng s dscussed. In Secton III, we ntroduce the concept of statstcal prorty for M2M uplnk schedulng over LTE. In Secton IV, we present our novel statstcal prorty metrc. The statstcal prorty-based schedulng algorthm s descrbed n Secton V and t s evaluated n Secton VI. Secton VII concludes the paper. II. BACKGROUND A. M2M Communcatons General Structure Several types of devces are nvolved n M2M communcatons, such as MTCDs, Machne Type Communcaton Gateways (MTCGs) and Machne Type Communcaton Servers (MTC Servers). The MTCD s the devce used to collect nformaton from the envronment (e.g. sensng, survellance and countng). The MTCD sends data to the base staton, whch s known as enb n LTE, ether drectly or va an MTCG. The MTCG acts as a cluster head for a group of MTCDs. The MTCG apples some forms of processng on data comng from MTCDs, e.g. combnng and flterng [5]-[6] to compress the amount of data to be sent to the enb. The MTC server s the end-target of the data sent by MTCDs. It receves data va the backhaul from the enb and makes t avalable for access by human or machne type users through some applcaton. M2M communcatons characterstcs dffer from those of H2H communcatons n several aspects such as the followng: Most of the M2M communcatons traffc occurs n the uplnk drecton,.e. from the MTCDs to the enb. MTC traffc s bursty and conssts mostly of low-rate small-sze packets. Many MTC applcatons have strct data transmsson deadlnes. Abdng by deadlnes s necessary to report an alarm for a dsaster, to mantan a certan data rate or a certan QoS and to send data before they become useless or obsolete. There are numerous types of MTCDs and they are used n a wde varety of applcatons. Hence, MTCDs vary wdely n terms of requrements of deadlnes and needed QoS. B. The M2M Schedulng Process Schedulng s the process carred out by the enb to allocate rado resources accordng to the requests of human user equpment (UEs) or MTCDs n downlnk or uplnk drecton. The mnmum rado resource unt that can be allocated to one UE/MTCD s called the Physcal Resource Block (PRB) [7]. The PRB s a resource grd that conssts of 12 subcarrers n one tme slot. The schedulng process can be dvded nto 2 stages: Tme Doman Packet Schedulng (TDPS): In ths stage, the enb selects a termnal (UE or MTCD) or a group of termnals to be assgned PRBs accordng to certan crtera (e.g. channel state, QoS, farness, etc.). Frequency Doman Packet Schedulng (FDPS): In ths stage, the enb selects the PRBs to be assgned to the termnal or group of termnals that have been selected n the TDPS stage. The enb allocates PRBs that the termnal can make the maxmum use of. For example, t may allocate the PRBs at whch the gven termnal has the best channel condtons. The desgn of uplnk schedulng technques for M2M communcatons should take nto consderaton that MTCDs n M2M communcatons may have strct deadlne requrements. In addton, schedulng should consder the fact that a massve number of MTCDs may contend for the lmted rado resources [8]. C. M2M Schedulng Algorthms Revew M2M schedulng algorthms desgn follows dfferent approaches. The frst approach s to use data transmsson deadlnes as the schedulng metrc [9]-[11]. The authors n [9] propose two schedulng technques for M2M communcatons that combne both channel state and MTCDs deadlnes as metrcs for schedulng decsons. The frst algorthm adapts a conventonal channel state-based algorthm to take nto consderaton MTCD deadlnes. The second algorthm gves MTCD deadlnes hgher prorty as compared to channel state n the schedulng decsons. The authors n [10] propose a new metrc called urgency that combnes deadlne requrements and the buffer sze (sze of the data to be sent) to allocate PRBs to MTCDs wth hgher urgency value. In [11], the authors propose an algorthm that alternates between channelstate and MTCD deadlnes nterchangeably n tme to balance

3 3 throughput maxmzaton and deadlne mssng rato mnmzaton objectves heurstcally. The second approach s to group the MTCDs nto QoS classes and allocate rado resources accordngly [12]-[17]. In [12], a groupng-based algorthm s proposed as an mprovement to the algorthm n [13]. The MTCDs are grouped n clusters that belong to a QoS class characterzed by the packet arrval rate and the sze of data to be downloaded or uploaded. When the product of the aforementoned two factors for a certan group s hgher than that of the other groups, ths group gets allocated rado resources more frequently. The authors n [14] defne three QoS Class Indcators (QCI) for M2M applcatons. In [15], a Class-Based Dynamc Prorty (CBDP) scheme s presented. Ths schedulng technque suggests that a hybrd scheduler for both M2M and H2H traffc s the most effcent. The authors desgn ther algorthm n such a way that enables t to prortze delay-senstve M2M traffc over delay-tolerant H2H traffc. In [16], the authors propose a mult-step scheduler that dvdes MTCDs nto groups where each group s assgned a porton of resources based on the requred QoS and the buffer status. The authors n [17] propose a hybrd schedulng algorthm for a heterogeneous network that deals wth both H2H and M2M communcatons. The traffc s dvded nto two queues and each queue s scheduled separately. The frst queue ncludes all H2H users (UEs) and delay-senstve MTCDs. Schedulng s based on a combnaton of metrcs that nclude buffer watng tme, proportonal farness and delay thresholds. The second queue ncludes all remanng (delay-tolerant) MTCDs that are scheduled usng a combnaton of channel-state-based and round-robn-based schedulers. The thrd approach s to desgn a schedulng algorthm that s talored for a specfc applcaton. The authors n [18] ntroduce the dea of predctve schedulng that allocates rado resources to MTCDs that are n vcnty of an MTCD that s currently sendng a Schedulng Request (SR). The algorthm s sutable for specfc applcatons such as cascaded alarm systems and Wreless Sensor Networks (WSNs). The fourth approach formulates the schedulng problem as an optmzaton problem wth dfferent objectves [19]-[21]. In [19], the authors propose an algorthm to solve an optmzaton problem whose objectve s allocate power and rado resources such that the mnmum lfetme of a group of MTCDs s maxmzed. The constrants of maxmum transmsson power and LTE transport block sze are taken nto consderaton. The authors n [20] propose an energyeffcent schedulng algorthm by mnmzng the transmsson tme of MTCDs whle consderng ther data transmsson deadlnes. In [21], power consumpton mnmzaton s the objectve, and mnmal data rate requrements are added to deadlne constrants. The novel feature n our proposed metrc s that we consder the nature of the real-tme data sent by MTCDs. We ntroduce a metrc that helps the enb allocate rado resources to the MTCDs that need to send more mportant data n real-tme nstead of just dependng on fxed prorty assgnments. D. Data Compresson Data compresson s the process of reducng the sze of a data stream reported by a devce. Ths s done for many purposes such as savng the devce's power and reducng traffc wthn the network. The man method for data compresson s to lmt data transmssons by refranng from sendng data values that are hghly smlar to prevously sent data ponts usng dfferent methods. In [5], the sensor node does not send the measured data value except f t dffers by at least a certan threshold from the last sent data value (.e. low value smlarty or low temporal correlaton). Furthermore, the gateway node (e.g. MTCG) collects the data sent by sensor nodes and excludes those readngs that do not dffer sgnfcantly from the readngs of the other sensors wthn the vcnty of ths sensor (.e. low value smlarty or low spatal correlaton). In [6], a gateway node collects measured data values from sensors and sets a dstrbuton of data values based on normal dstrbuton or T-dstrbuton wth a certan range. Ths range s broadcasted to all sensor nodes so that every sensor node does not transmt ts measured value except f t s out of the broadcasted range. As we wll dscuss later n the paper, we do not rely on applcaton-based compresson technques or group-based decsons to reduce transmtted data. Ths s due to the fact that mposng such rules on applcaton developers would be restrctve and mpractcal. In addton, n many cases, network nodes act ndvdually and autonomously wth regard to data transmsson decsons, whch renders group-based decsons napplcable. III. THE ATTRIBUTES OF STATISTICAL PRIORITY In ths secton, we present possble statstcal attrbutes that can be used to quantfy the mportance of nformaton of the data to be sent by MTCDs. Ths offers an opportunty for allocatng the scarce rado resources on the bass of data unqueness. Hence, the MTCDs wth unque data are gven hgher prorty that we term the Statstcal Prorty (SP). We classfy M2M data accordng to [15] nto three classes, namely, envronmental montorng data, vdeo data and alarm data. The envronmental montorng class represents perodc data wth low rate, relaxed deadlnes (.e. the data transmsson deadlne s relatvely long compared to that of other MTCDs) and redundant data. The vdeo survellance class s characterzed by large payloads wth many smlar vdeo frames due to montorng a stable stuaton most of the tme. It s worth notng that we focus on vdeo applcatons wthn the context of MTC only (.e. H2H vdeo applcatons lke streamng and vdeo conferencng are not consdered). The alarm class represents the event-drven data wth hgh mportance and very strct deadlne requrements n the order of mllseconds. Whle there s a wde varety of MTC data, we only consder the aforementoned classes as sample applcatons that represent dfferent traffc characterstcs. We therefore defne the possble statstcal attrbutes that can be used to determne the unqueness of the data of each of these classes n the followng.

4 4 A. Envronmental Montorng Data Envronmental montorng data are produced by sensors that montor a gven phenomenon such as temperature, humdty, pressure, lght ntensty and gas concentraton n a gas leakage detecton system. These data may be collected for recordng and archvng purposes. For example, t could be requred to keep records of the daly temperature changes of a cty throughout the year to support weather forecast and meteorologcal studes. In addton, data may be collected to ensure the safety/stablty of ndustral processes and montor the contents of warehouses for theft detecton. The envronmental montorng data are generally characterzed by small packet szes and low data rates. There are several statstcal parameters that can help defne the value of nformaton reported by MTCDs performng envronmental montorng such as threshold, value smlarty and trend smlarty. Threshold MTCD data that exceed a certan upper/lower threshold may need an acton as a response. These data ponts also become even more mportant when the dfference between them and the threshold ncreases. If an MTCD reports a data value, x(t), at a dscrete tme nstant t where we have an upper threshold of nterest Th upper and a lower threshold of nterest Th lower, x(t) s sad to have hghly valuable nformaton f: x( t) Th upper or x( t) Thlower. (1) Value Smlarty Let the data pont reported by an MTCD at a gven nstance x(t) be dfferent from the last reported data pont x p by x(t) - x p. If the value of x(t) - x p exceeds a certan threshold Δ, ths means that the reported data pont s not redundant and ts nformaton has a unque value. For example, let us assume a temperature sensor that reports the data ponts of values (27.1, 27.1, and 27.4 degrees Celsus). If the frst data pont s reported, the second data pont s redundant snce t s the same lke the prevous one (especally when the tme dfference between them s small). The thrd data pont s not consdered redundant but t has less valuable nformaton when compared to the fourth data pont. Hence, settng Δ to a value of 0.1 to defne the data ponts of hgh-value nformaton s reasonable. The data pont becomes more mportant when the change level threshold s exceeded by a hgher dfference snce t presents a hgher change n the measurement of the montored phenomenon. Hence, x(t) s sad to have unque nformaton of hgh value f: x ( t) x. (2) p Trend Smlarty When a sequence of data ponts reported by an MTCD mantans a constant trend (ncreasng or decreasng) for a seres of ponts, ths may be more valuable to report than ponts oscllatng around an average value wth small varatons. For example, consder an MTCD that reports humdty data ponts of (40%, 41%, 39% and 40%). Ths can be seen as mnor fluctuatons around an average value. On the other hand, f t reports data ponts of humdty of (39%, 40%, 41% and 42%), t s easly concluded that there s an ncreasng trend n humdty data. Ths may be of more nterest to know and report than the modestly fluctuatng case. If an MTCD reports a data pont x(t) at nstant t, x(t) s sad to have a hgh nformaton value f [22]: ( x ( t) x( t 1)) ( x( t 1) x( t 2)) 0. (3) B. Vdeo Data Vdeo data represent one of the common data types n M2M networks. Ther sources are manly cameras that are used n many applcatons e.g. survellance, people countng and object countng. These cameras have hgher data rates (for seamless transton between consecutve frames, cameras operates at a mnmum of 30 frames/second) and harder delay tolerance requrements when compared to envronmental montorng data. Throughout ths paper, vdeos are represented n RGB format, where any pxel at a gven poston (x, y) and tme nstant (t) has an 8-bt represented level (Maxmum Level = 255) for each of the R, G and B components. However, the statstcal attrbutes we use for vdeo data (correlaton) can be appled to other vdeo formats lke YCbCr, wthout loss of generalty. Vdeo encodng, compresson and specal frames are out of the scope of ths paper. The vdeo data are consdered to have hgh-value nformaton when the frames carry sgnfcant changes when compared to prevous frames. For example, a survellance camera recordng a stable status wth no changes should be gven lower prorty n data transmsson when rado resources are scarce. 2-D Frame Correlaton The best statstc to measure the value of nformaton carred by a gven vdeo frame s the correlaton wth prevous frames. A frame that s very hghly correlated (correlaton 1) wth the last transmtted frame has less mportance and value f compared to frames carryng new events and many changes and hence less correlated wth last reported frame. For example, a survellance camera recordng at late nght or early mornng wll barely carry useful nformaton to tell snce the frames are almost constant (except at the tme of threats). Consder an MTCD that reports a vdeo frame at nstant t represented by the vector F(x, y, t) = [F R(x, y, t) F G(x, y, t) F B(x, y, t)], where x and y are the coordnates of the pxel n the frame, whle F R, F G and F B represent the level of RGB components at ths pxel. The 2-D correlaton between the current frame and a prevous frame at nstant t o can be defned by the vector C as follows: C C C Corr(F (x, y, t),f (x, y, t ) ). (4) C R G B C. Alarm Data Corr(F (x, y, t),f (x, R G Corr(F (x, y, t),f (x, B R G B y, t y, t Alarm data are the data that report the occurrence of abnormal condtons, n general. They are manly eventtrggered. The alarms may be used as alerts for fres or nonauthorzed buldng entry. They can be modeled as a sequence of very small payload data packets wth tghtly strct deadlne requrements. The data transmtted by alarm MTCDs are always crucal. Hence, Alarm MTCDs should be gven the o o o ) ) ) )

5 5 hghest prorty durng the rado resource allocaton process IV. STATISTICAL PRIORITY METRIC Statstcal Prorty (SP) s a quantfcaton of the value of nformaton entaled n a data unt reported by an MTCD based on the statstcal attrbutes that we dscussed n the prevous secton. In ths secton, we propose a bounded output functon whose output takes a value between 0 and SP max (a postve real value) to calculate the SP value. The man goals of the SP value evaluaton functon can be stated as follows: Assgnng hgher prorty to data packets carryng nonredundant nformaton of hgh value or mportance n a manner that s adaptve to the varous M2M applcatons. Guaranteeng a mnmum rate of data transmsson by an MTCD. The goal s to ensure that every MTCD reports at least one comprehensve unt of nformaton every defned perod T to prevent MTCD resource starvaton. A. Statstcal Prorty as a Functon of Statstcal Attrbutes Consder the array Y = (y 1(t),, y (t),, y n(t)) where y (t) s a set of the aforementoned statstcal attrbutes. SP can be calculated as a weghted sum of functons f(y (t)) based on the value of statstcal attrbutes y (t) at a dscrete tme nstant t. The value of the functon f(y (t)) ndcates whether a certan statstcal attrbute y (t) s detected at nstant t. Hence, the functon f(y (t)) can be modeled as a sgmodal (logstcal or s- curve) functon that s output bounded between 0 and 1: f ( y 1 ( t) ) sgmod ( y ( t), b, c ), (5) b ( y ( t) c ) 1 e where c represents the nflecton pont of the functon such that f (y (t) = c ) s 0.5 and b controls how steep the functon moves from 0 to 1. A large magntude of b results n a steep transton from 0 to 1 smlar to the step functon. On the other hand, a small magntude of b makes ths transton smooth. If b < 0, the sgmod functon s reflected around the vertcal axs. Fg. 1 shows dfferent realzatons of the sgmod functon at c = 0. We consder 4 cases of statstcal attrbutes: Case 1: f x(t) > Th upper, x(t) should be consdered to have unque and mportant data and should therefore have a hgher SP value. Then, the attrbute of nterest y (t) can be expressed as: y ( t) x( t). (6) Th upper When x(t) exceeds a threshold (.e. hgh-value nformaton), y (t) becomes postve and f(y (t)) should approach 1. When the threshold has not been exceeded, y (t) takes a negatve value and f(y (t)) should approach 0. Hence, f(y (t)) could be represented as sgmod(y (t),100,0) (where b can be any large number for steep transton). A smlar case that can be consdered s when x(t) < Th lower. In ths case, we can set b of the sgmod functon to be a negatve value /1+exp(-b*y) y Fg. 1. The Sgmod Functon Case 2: f the data are consdered valuable and unque when the dfference between x(t) and last reported measurement x p by the MTCD exceeds a certan threshold. Consequently, y (t) can be represented as: y ( t) x( t) x. (7) Hence, f(y (t)) could be represented as sgmod(y (t),b,0) where b can be selected accordng to the desred level of sgnfcance of the value of the dfferences. Case 3: If a data pont s shown to follow a trend, t s more valuable to send and should have hgher SP value. Then, the functon of nterest y (t) can be represented as: 2d1 y ( t) ( x( t j) x( t j 1)), d 1,2,..., (8) j0 where d represents the depth of the trend functon (when d = 1, we consder the trend n the latest three data ponts, when d = 2, we consder the trend n the latest fve data ponts). If the measurements follow an ncreasng or decreasng trend, the value of y (t) wll be postve (for an odd number of measurements ncludng the current measurement at nstant t) and f(y (t)) should approach 1. Otherwse, y (t) wll be negatve and f(y (t)) should approach 0. Hence, f(y (t)) could be represented as sgmod(y (t),100,0) (where b can be any large number for a steep transton). On the other hand, a small value of b can be used to assgn hgher prorty to trends wth bgger dfferences between successve data values. Case 4: 2-D Tme autocorrelaton between the current frame at tme t and a prevous frame at tme t o at the three components of the vdeo frame (e.g. RGB components or any other representaton) of a gven survellance camera MTCD s calculated to form C as n (4). Then, the mnmum correlaton C mn s obtaned as follows: p C mn( C, C, C ). (9) mn Therefore, the attrbute of nterest y can be represented as: R G mn C Th B b = 10 b = -10 b = 1 b = -1 y ( t) C, (10) where C Th s the maxmum correlaton value that represents an effectve change between two vdeo frames. In ths case, a negatve value of y (t) ndcates that the current frame at nstant t s a unque frame of hgh SP value and t carres new nformaton (sgnfcant changes) as nferred from the fact that t s not hghly correlated wth last transmtted frame at nstant

6 6 t o. Hence, f(y (t)) could be represented as sgmod(y (t),- 1000,(1-C Th)/2) (where b s a very large negatve number snce practcally a frame that s correlated by up to ±0.995 carres dfferent nformaton and c s a md-pont between C Th and the maxmum correlaton of 1). Choosng the type of statstcal feature that ndcates the value of nformaton entaled n data s dependent on the applcaton. Moreover, multple statstcal features may be useful wth dfferent levels of mportance. That s, each functon f(y (t)) s gven a weght a from the vector A = (a 1,, a,, a n). These weghts take values such that: n 1 0 a SP max (11) It s worth notng that the condton n (11) apples only to non-mutually exclusve statstcs (.e. mutually exclusve statstcal attrbutes that can never happen together such as exceedng upper and lower thresholds do not need to satsfy ths condton). Ths condton makes the SP value at nstant t bounded snce t s modeled as a weghted sum of the sgmodal functons f(y (t)) of the dfferent statstcal attrbutes as follows: n SP ( A, Y, t) a f ( y ( t)). (12) 1 B. Guaranteeng Mnmum Rate of Data Transmsson The SP value should ensure no starvaton occurs by allowng each MTCD to send at least one unt of data every perod of tme, T, regardless of the data mportance. For example, a sensor may be confgured to provde a measurement every 0.1T. Of these measurements, at least one measurement should be reported every T seconds (.e. at least one measurement out of 10 measurements should be assgned a hgh SP value). Consder a perodc functon f o(t) whose output ranges between 0 and 1 and has a perod T. f o(t) could be wrtten, for example, as a snusodal functon wth an offset as follows: 2t f o ( t) 0.5(cos( ) 1) (13) T Ths perodc functon has a weght a o that wll be used n the SP calculaton, as we wll llustrate shortly, where: 0 a o SP max (14) Furthermore, to lmt the guaranteed number of data transmssons to one data transmsson per perod T, we ntroduce the varable f r(t,t) where: 0,1 f r ( t, T) (15) Ths varable s set to 0 f any data packet was sent durng the current nterval T and t remans 1 otherwse. f r(t,t) s rentalzed to take the value of 1 at the start of every new cycle of length T. Hence, SP value can expressed based on the perodc functon f o(t) and varable f r(t,t) as follows: SP( A, Y, t, T) a f ( t) f ( t, T) (16) o o r When the MTCD has non-redundant data, t can send more than one data transmsson dependng on the SP value, as wll be clarfed n the followng sub-secton. C. Statstcal Prorty Evaluaton Functon The goals of SP that are satsfed by the SP value expressons n (12) and (16) can be combned by selectng the maxmum of the two functons as follows, n SP( A, Y, t, T ) max( ao fo ( t) f r ( t, T ), a f ( y ( t))). (17) 1 In ths case, the MTCDs wth unque data that have mportant nformaton based on the statstcal attrbutes wll cause the second functon to nfluence the SP to assume a hgh value. As a result, the data become of hgh prorty durng the rado resource allocaton process. On the other hand, the MTCDs wth repettve data wll be guaranteed to send one data unt every perod T as a result of the effect of the frst perodc functon. A specal case exsts for alarms. The data from alarms are always consdered mportant and urgent. Hence, SP s drectly set to SP max for alarm MTCDs. Ths mathematcal model for evaluatng the SP value s very flexble whch makes t sutable for dfferent M2M applcatons, dfferent statstcal attrbutes of nterest (e.g. mean crossng rate) and perodc data transmsson. For example, f an MTCD needs to send dentcal lve sgnals perodcally, then a o can be set to be SP max to guarantee one packet s transmtted every perod T. D. Statstcal Prorty Reportng n LTE In LTE, there are three physcal uplnk channels. The Physcal Uplnk Shared Channel (PUSCH) s used to carry uplnk data. The Physcal Random Access Channel (PRACH) s used for random access requests. Fnally, the Physcal Uplnk Control Channel (PUCCH) s used to send uplnk control nformaton from UEs/MTCDs to the enb such as Channel Qualty Indcator (CQI), Precodng Matrx Indcator (PMI), Rank Indcator (RI), Schedulng Request (SR) and Buffer Status Report (BSR) [7]. We propose to report the SP va a Statstcal Prorty Report (SPR) that could be sent by the MTCD through the PUCCH. It can follow smlar structure to that of the BSR. Ths means that there should be an SPR value for every rado bearer (logcal channel). The SPR value can be reported usng 6 bts (64 levels) lke the BSR [7]. Although, ths approach mposes more control traffc on the PUCCH, t wll help reduce the amount of redundant data traffc to be sent over LTE M2M networks. V. STATISTICAL PRIORITY-BASED SCHEDULING ALGORITHM The schedulng algorthm s based on the statstcal prorty metrc that we dscussed n Secton IV. Algorthm I s desgned such that t dedcates a set of rado resources for servng M2M communcatons traffc. Due to the scarcty of rado resources n general, the PRBs are allocated frst to the MTCDs that have the hghest SP score,.e. they are allowed to send data that are more unque and carry nformaton of hgher SP value. Consder a set of N PRBs and a set of M actve MTCDs (actve MTCDs are the ones that request data

7 7 transmsson and have not mssed the deadlne yet). Every MTCD m (m = 1,, M) has a deadlne D m, an SP value of SP m and an SNR value at the n th PRB of S m,n. ALGORITHM I. STATISTICAL PRIORITY-BASED SCHEDULING ALGORITHM Statstcal Prorty-based Schedulng Algorthm Step 1: Sort SP m values n a descendng order to select an actve MTCD m that carres data wth maxmum statstcal prorty score (.e. most valuable nformaton) Step 2: Assgn PRB n (at whch the selected MTCD m has maxmum SNR S m,n) to MTCD m Step 3: Allocate PRBs on the rght and the left of PRB n to MTCD m and keep expandng n both drectons tll any of the followng condtons apples [23]: - MTCD m acqures enough PRBs to send ts data. - Expandng n any drecton s blocked by PRBs allocated to other MTCDs. Step 4: Consder MTCD m as served and remove the allocated PRBs from the set of avalable PRBs. Then repeat Step 1 tll all PRBs are allocated, or the needs of all MTCDs have been fulflled In summary, ths algorthm starts by selectng the MTCD wth hgher SP value n Step 1. In Step 2, t chooses the PRB n at whch ths MTCD has the best SNR. The MTCD keeps acqurng PRBs to the left and the rght of the PRB n tll t ether gets suffcent PRBs to send ts data or t hts PRBs already allocated to other MTCDs as shown n Step 3. In Step 4, the allocated PRBs become no longer avalable and the MTCD s consdered served before contnung to allocate the remanng PRBs. The followng ponts are worth notng: SNR could be replaced by any channel-state metrc lke SINR or CQI Any MTCD that msses the deadlne returns to the dle state (except when there are stll data packets n ts buffer) tll t needs to transmt data once agan. VI. EXPERIMENTAL EVALUATION A. Smulaton Setup We compare the statstcal prorty-based schedulng wth delay-based schedulng n realstc M2M deployments where MTCDs have dfferent profles (.e. dfferent delay tolerances, data szes, data types and traffc structure). In addton, we use an SNR-based scheduler n the comparson knowng that t does not address deadlne prortes. In ths comparson, we ntroduce novel performance evaluaton metrcs that focus on measurng the crtcal data that were successfully transmtted rather than measurng the mere throughput that does not consder the level of mportance of the transmtted data packets. We consder a sngle base staton servng MTCDs usng 3 or 5 MHz dedcated bandwdth to M2M communcatons n an AWGN channel. The smulaton parameters are shown n Table I. Four groups of MTCD types, namely, emergency alarms, cameras and two groups of envronmental montorng (temperature and humdty sensors) are used to represent dfferent types of M2M traffc. Table II shows the detals of delay tolerance, packet sze and percentage of MTCDs that belong to the four groups. Further detals about the traffc generaton of each MTCD group are gven n Table III. Table IV descrbes the performance evaluaton metrcs used to compare our proposed schedulng scheme wth the other schemes. In ths table, we ntroduce the metrc Crtcal Packets Success Rate that shows how successful an algorthm s n allowng every MTCD to send suffcent data to represent the whole set of data gven the scarce rado resources. TABLE I. SIMULATION PARAMETERS FOR SP-BASED SCHEDULING EVALUATION Parameter Value Number of MTCDs (M) Number of Base Statons 1 Average SNR Range Unform (4dB,10dB) Number of Subframes Number of Runs 10 Independent Runs Bandwdth (MHz) 3 5 Number of PRBs (N) Channel Model Addtve Whte Gaussan Nose (AWGN) Deadlne-based Scheduler Schedulng Algorthms SNR-based (Channel-state) Scheduler SP-based Scheduler TABLE II. MTCD CONFIGURATION Applcaton Delay Tolerance (ms) Packet Sze % (Bytes) Emergency Alarms Constant (10) 32 10% Survellance Camera Unform (125,250) % Regular Montorng (Temperature) Unform (800,900) % Regular Montorng (Humdty) Unform (800,900) % TABLE III. MTCD TRAFFIC DESCRIPTION Applcaton Packet Content Traffc Descrpton Emergency Alarms Alert 5 packets wthn 200ms Survellance Camera 30 frames per second Compressed 400 frames per MTCD Low-qualty Random start tme Vdeo Frame Frames extracted from [24] Regular Montorng (Temperature, Humdty) TABLE IV. Evaluaton Metrc Overall deadlnemssng rato Alarm deadlnemssng rato Crtcal packets success rate (Sensors) Crtcal packets success rate (Cameras) Data Pont 1 data pont per second 200 data ponts per MTCD Data ponts extracted from [25] PERFORMANCE EVALUATION METRICS DESCRIPTION Descrpton The rato between packets that mssed deadlne to the overall number of packets for all MTCD types The rato between alarm packets that mssed deadlne to the overall number of alarm packets The rato between successfully sent packets for regular montorng MTCDs to the number of ts crtcal packets. The number of crtcal packets s the number of packets that are suffcent to clam that lnearly nterpolated summarzed data represent the full data set for every regular montorng MTCD. The rato between successfully sent packets for survellance camera MTCDs to the number of crtcal packets for every survellance camera MTCD.

8 8 TABLE V. STATISTICAL ATTRIBUTE THRESHOLDS FOR MTCDS Statstcal Attrbute Temperature Humdty Sensor Sensor Camera Upper Threshold (Th upper) 28 C 48% N/A Lower Threshold (Th lower) 27 C 42% N/A Dfference Threshold (Δ) 0.1 C 0.1% N/A Trend Yes Yes N/A 2D Correlaton (C Th) N/A N/A In addton, SP parameters are selected to boost the mportance of the data ponts or the frames that have hghvalue nformaton. The hgh-value nformaton for sensors data ponts, as descrbed n Secton III, s found n statstcal attrbutes lke exceedng a threshold, followng a trend, experencng a bg change wth respect to last reported data. In case of vdeo frames, hgh-value nformaton exsts n a frame f t s farly uncorrelated wth last sent vdeo frame. For alarms, all packets are of maxmum mportance (SP = SP max) and are therefore prortzed over all other types of traffc. SP max equals 10 n smulatons. Table V shows the thresholds used to calculate the dfferent statstcal attrbutes y (t) at an nstant t such as the upper and lower thresholds of nterest, the mnmum dfference threshold Δ and mnmum correlaton threshold for sensors and cameras, respectvely. Table VI lsts the values of parameters b and c for the sgmod functons f(y (t)) assocated wth the dfferent statstcal attrbutes y (t). Fnally, Table VII presents the values of the weghts a o and a gven to each statstcal feature functon and perodc data reportng to calculate the SP value usng (17). Overall, eght scenaros (two values of channel bandwdth four values of the number of MTCDs) are smulated accordng to the aforementoned parameters to represent dfferent traffc loads. The evaluaton metrcs stated n Table IV are measured for the eght smulaton scenaros. Each smulaton scenaro s performed usng each of the tested schedulers for ten ndependent runs. The fnal results represent the average of these runs wth 95% confdence nterval analyss. It s worth notng that, as ndcated n Table IV, each of the performance evaluaton metrcs focuses on a certan type of MTCD traffc (.e. sensor data, vdeo and alarms). TABLE VI. Statstcal Attrbute SIGMOID FUNCTION PARAMETERS OF STATISTICAL ATTRIBUTES Temperature Sensor Humdty Sensor Camera b c b c b c Upper Threshold N/A N/A Lower Threshold N/A N/A Dfference Threshold N/A N/A Trend N/A N/A 2D Correlaton N/A N/A N/A N/A B. Overall Deadlne-Mssng Rato Overall deadlne-mssng rato results, whch are shown n Fg. 2 and Fg. 3, show that non-deadlne-based schedulers such as SNR-based and SP-based schedulers have less deadlne msses than the deadlne-based scheduler when rado resources are nsuffcent for satsfyng all data transmsson requests. Ths s due to the fact that the deadlne-based scheduler tres to meet all densely successve deadlnes for a large number of MTCDs whch results n mssng many of these deadlnes. On the other hand, the strategy of the nondeadlne-based schedulers helps reduce the rato of deadlne msses for dfferent denstes of MTCDs as shown n Fg. 2 and Fg. 3. We see from the fgures that SNR-based scheduler outperforms our proposed SP-based scheduler due to the fact that the SNR-based scheduler allocates PRBs to the MTCDs wth the best channel condtons so that they would utlze the scarce rado resources effcently (.e. wthout packet drops or the need for addtonal PRBs per packet). However, the dsadvantages of SNR-based schedulng wll be revealed through the other performance evaluaton metrcs. C. Alarm Deadlne-Mssng Rato Usng ths performance evaluaton metrc, we focus on alarm MTCDs due the hgh mportance of ther data and the strct deadlne requrement they have. A perfect scheduler should be able to allow the transmsson of all alarm packets (or mss as few as possble f alarm redundancy s assumed). The alarm deadlne-mssng rato results n Fg. 4 and Fg. 5 show that the SP-based scheduler outperforms the deadlnebased scheduler wth an alarm deadlne-mssng rato that does not exceed 4% for all the smulaton experments. On the other hand, the SNR-based scheduler deals wth alarm MTCDs and other MTCDs on equal bass despte the fact that alarm packets need to be gven hgher prorty due to the hgh-value nformaton they carry about abnormal condtons or threats. Hence, t performs sgnfcantly worse than the other schedulers from the perspectve of ths metrc. The reason behnd the superor performance of the statstcal prortybased scheduler s that t prortzes alarm MTCDs based on data mportance or SP score n whch alarm MTCDs always have hgher prorty over other MTCDs. On the other hand, the deadlne-based scheduler prortzes alarm MTCDs based on deadlne where other non-alarm MTCDs may have closer deadlnes. TABLE VII. WEIGHTS OF STATISTICAL FEATURE FUNCTIONS Statstcal Attrbute Temperature Humdty Sensor Sensor Camera Alarm Upper Threshold (a 1) N/A Lower Threshold (a 2) N/A Dfference Threshold (a 3) N/A Trend (a 4) N/A 2D Correlaton (a 5) N/A Perodc Prorty (a 0) N/A Default N/A N/A N/A 10 Fg. 2. Overall Deadlne-mssng Rato (BW = 3MHz)

9 9 calculated. Wth lmted rado resources, t s not effcent to strve to send as much data as possble. Rather, the focus should be on ensurng the success of sendng crtcal data packets. Crtcal _ Packets _ Success _ Rate # Sent _ Packets mn( # Crtcal _ Packets,1) 100% (18) Fg. 3. Overall Deadlne-mssng Rato (BW = 5MHz) Fg. 4. Alarm Deadlne-mssng Rato (BW = 3MHz) Fg. 5. Alarm Deadlne-mssng Rato (BW = 5MHz) D. Crtcal Packets Success Rate (Sensors) When we evaluate the performance of the scheduler for regular montorng MTCDs (e.g. temperature and humdty sensors), more focus should be gven to data of hgh-value nformaton. Regular montorng devces usually send repettve data values and the same data features could be preserved even wth sendng a reduced number of data values selected on the bass of ther mportance calculated by statstcal prorty. Hence, to evaluate the success of a gven scheduler wth respect to regular montorng MTCDs, we measure the success rate of sendng the crtcal packets n terms of the rato of the actual sent data packets to the number of crtcal packets that must be sent to fully represent the whole data stream. Ths success rato s measured for every regular montorng MTCD as n (18) and the average s The results of ths performance evaluaton metrc are shown n Fg. 6 and Fg. 7. It s clear that the statstcal prorty-based scheduler outperforms the other schedulers especally as the number of the MTCDs ncreases. The success rate of the proposed technque s almost 100% for the system bandwdth of 5MHz at any traffc load. When the system bandwdth s reduced to 3 MHz,.e. less number of PRBs, SPbased scheduler suffers the least losses and keeps the success rate above 90% when number of MTCDs s 300 or less. Although SNR-based scheduler enables transmttng more data packets than SP-based scheduler, t does not take the mportance of data collected by MTCDs nto consderaton. Hence, some MTCDs (usually wth hgh SNR) succeed to send more data packets (crtcal and non-crtcal) regardless of redundancy n data. On the other hand, MTCDs wth worse channel condtons may not be able to transmt crtcal packets wth nformaton of hgh value snce the prorty of rado resource allocaton s gven to MTCDs wth better channel condtons. An evaluaton metrc lke crtcal packet success rate refutes the concluson that the SNR-based scheduler outperforms SP-based scheduler due to success n sendng more data packets. Furthermore, the SP-based scheduler s shown to be farer than baselne schedulers by allowng a larger number of MTCDs, on average, to be closer to transmttng 100% of ther crtcal data packets (whch s equvalent to the hgher success rate for crtcal packets). E. Crtcal Packets Success Rate (Cameras) The crtcal success rate evaluaton metrc can also be used for survellance camera MTCDs. Sendng the crtcal frames s suffcent to represent vdeo nformaton. The success rate for survellance cameras MTCDs s less than that of regular montorng MTCDs due to the larger packet sze, denser traffc (.e. more packets per second) and strcter deadlnes. However, vsual nspecton has shown that a 70-80% success rate s suffcent to represent the nformaton n a vdeo wthout much negatve mpact as notced by the human eye. In ths smulaton experment, the success rato s measured for every survellance camera MTCD usng (18) and the average s then calculated. The SP-based scheduler s found to outperform the other schedulers, as shown n Fg. 8 and Fg. 9. The success rate does not drop below 70% except for most severe cases of rado resource lmtatons (.e. M 300 MTCDs under a system bandwdth of 3 MHz or M 400 MTCDs under a system bandwdth of 5 MHz).

10 10 Fg. 6. Crtcal Packets Success Rate (Regular Montorng MTCDs) (BW = 3MHz) Fg. 7. Crtcal Packets Success Rate (Regular Montorng MTCDs) (BW = 5MHz) VII. CONCLUSION In ths paper, we ntroduced the concept of statstcal prorty-based schedulng for massve M2M deployments n LTE networks. Statstcal prorty s calculated by evaluatng specfc statstcal attrbutes of the data, dependng on the data type, that could be used to ndcate the mportance of a certan data value or vdeo frame. The mportance of a data value of a montorng sensor s determned by testng the ponts aganst upper and lower thresholds, checkng for magntude smlarty wth prevous data ponts and/or checkng f a seres of data ponts follow an ncreasng or decreasng trend. On the other hand, the mportance of a vdeo frame s determned through ts correlaton wth the prevous frames and as the correlaton decreases, the vdeo frame s deemed to carry more changes as compared to the prevous ones. Alarm data are gven default hghest statstcal prorty value. Therefore, a stream of data values or vdeo frames could be represented suffcently wth a smaller subset of the stream. Ths selected subset of the hghvalue nformaton s based on hgh statstcal prorty scores. We then mplemented a scheduler that utlzes ths metrc to allocate rado resources for M2M traffc over LTE-based networks that have multple types of MTCDs. We compared the new technque aganst channel-based and deadlne-based schedulng technques. Smulaton experments showed that the statstcal prorty-based scheduler has the least deadlnemssng rato of data packets generated by alarm MTCDs (less than 4%), whch have the hghest mportance. In addton, the statstcal prorty-based scheduler makes the best use of the scarce rado resources by allocatng them to the MTCDs carryng data wth the hghest nformatve value. The SPbased outperforms SNR-based and deadlne-based schedulers. It acheves a success rate that s always greater than 70% for envronmental montorng MTCDs. Ths rate s suffcent to represent the whole stream of data. The same superorty has also been acheved n case of survellance cameras. Fg. 8. Crtcal Packets Success Rate (Survellance Cameras MTCDs) (BW = 3MHz) Fg. 9. Crtcal Packets Success Rate (Survellance Cameras MTCDs) (BW = 5MHz) REFERENCES [1] J A. Gluhak, S. Krco, M. Nat, D. Pfsterer, N. Mtton and T. Razafndralambo, A Survey on Facltes for Expermental Internet of Thngs Research, IEEE Commun. Mag., vol. 49, no. 11, pp , Nov [2] F. Ghavm and H.-H. Chen, M2M Communcatons n 3GPP LTE/LTE-A Networks: Archtectures, Servce Requrements, Challenges, and Applcatons, IEEE Commun. Surveys Tuts., vol. 17, no. 2, May 2015 [3] Csco vsual networkng ndex: Global moble data traffc forecast update, , Feb [4] N. Abu-Al, A. M. Taha, M. Salah and H. Hassanen, Uplnk Schedulng n LTE and LTE-Advanced: Tutoral, Survey and Evaluaton Framework, IEEE Commun. Surveys Tuts., vol.16, no. 3, pp. 1-27, Sep [5] M.-H. L, C.-C. Ln, C.-C. Chuang, and R.-I. Chang, Error-Bounded Data Compresson usng Data, Temporal and Spatal Correlatons n Wreless Sensor Networks, IEEE Internatonal Conference on Multmeda Informaton Networkng and Securty (MINES), Nov [6] S. T. Hong and J. W. Chang, A New Data Flterng Scheme Based on Statstcal Data Analyss for Montorng Systems n Wreless Sensor Networks, IEEE 13th Internatonal Conference on Hgh Performance Computng and Communcatons (HPCC), Sep [7] 3GPP TS v , Physcal Layer Procedures (Release 10), Aprl 2011 [8] M. A. Mehaseb, Y. Gadallah, A. Elhamy, and H. Elhennawy, "Classfcaton of LTE Uplnk Schedulng Technques: An M2M

11 11 Perspectve," n IEEE Commun. Surveys & Tuts., vol. 18, no. 2, pp , November [9] A. Loumpas and A. Alexou, Uplnk Schedulng for Machne-to- Machne Communcatons n LTE-based Cellular Systems, IEEE GLOBECOM Workshops, Dec [10] N. Afrn, J. Brown and J. Y. Khan, Performance Analyss of an Enhanced Delay Senstve LTE Uplnk Scheduler for M2M Traffc, Australasan Telecommuncaton Networks and Applcatons Conference (ATNAC), Nov [11] A. Elhamy and Y. Gadallah, BAT: A Balanced Alternatng Technque for M2M Uplnk Schedulng over LTE, IEEE Vehcular Technology Conference (VTC-Sprng), May 2015 [12] Y. Hsu, K. Wang and Y. Tseng, Enhanced Cooperatve Access Class Barrng and Traffc Adaptve Rado Resource Management for M2M Communcatons over LTE-A, Asa-Pacfc Sgnal and Informaton Processng Assocaton Annual Summt and Conference (APSIPA), Oct [13] S.-Y. Len, K.-C. Chen and Y. Ln, Toward Ubqutous Massve Accesses n 3GPP Machne-to-Machne Communcatons, IEEE Commun. Mag., vol. 49, no. 4, pp , Apr [14] I. Abdallah and S. Venkatesan, A QoE Preservng M2M Aware Hybrd Scheduler for LTE Uplnk, Internatonal Conference on Selected Topcs n Moble and Wreless Networkng (MoWNeT), Aug [15] M. K. Gluka, N. Rajora, A. C. Kulkarn, V. Sathya and B. R. Tamma, Class Based Dynamc Prorty Schedulng for Uplnk to Support M2M Communcatons n LTE, IEEE World Forum on Internet of Thngs (WF-IoT), Mar [16] S. N. K. Marwat, C. Goerg, T. Weerawardane, A. Tmm-Gel and R. Perera, Impact of Machne-to Machne (M2M) Communcatons on Dsaster Management n Future Moble Networks, General Sr John Kotelawala Defence Unversty Annual Symposum, Sr Lanka, 2012 [17] S. Zhenq, Y. Hafeng, C. Xuefen and L. Hongxa, Research on Uplnk Schedulng Algorthm of Massve M2M and H2H Servces In LTE, IET Internatonal Conference on Informaton and Communcatons Technologes (IETICT 2013), Apr [18] J. Brown and J.Y. Khan, Predctve Resource Allocaton n the LTE Uplnk for Event Based M2M Applcatons, IEEE Internatonal Conference on Communcatons (ICC), Jun [19] A. Azar and G. Mao, "Lfetme-Aware Schedulng and Power Control for M2M Communcatons n LTE Networks," IEEE Vehcular Technology Conference (VTC-Sprng), Glasgow, UK, May 2015 [20] Y. B. Chen, S. R. Yang, J. N. Hwang, and M. Z. Wu, "An energyeffcent schedulng algorthm for real-tme machne-to-machne (M2M) data reportng," IEEE Global Communcatons Conference (GLOBECOM), Austn, TX, Dec [21] A. Ajaz and A. H. Aghvam, "On Rado Resource Allocaton n LTE Networks wth Machne-to-Machne Communcatons," IEEE Vehcular Technology Conference (VTC-Sprng), Dresden, Germany, Jun [22] S. Yang, Z. Gao, R. Huang and X. Qu, A Data Correlaton-based Vrtual Clusterng Algorthm for Wreless Sensor Network, Internatonal Conference on Moble Ad-hoc and Sensor Networks (MSN), Dec [23] L. R. de Temño, G. Berardnell, S. Frattas and P. Mogensen, Channel-Aware Schedulng Algorthms for SC-FDMA n LTE Uplnk, IEEE Internatonal Symposum on Personal, Indoor and Moble Rado Communcatons (PIMRC), Sep [24] [25] S. Suthaharan, M. Alzahran, S. Rajasegarar, C. Lecke and M. Palanswam, Labelled Data Collecton for Anomaly Detecton n Wreless Sensor Networks, Internatonal Conference on Intellgent Sensors, Sensor Networks and Informaton Processng (ISSNIP), Dec. 2010