Chhayarith Heng Uy
email: chhayarith.heng-uy@univ-grenoble-alpes.fr

Carolynn Bernier
email: carolynn.bernier@cea.fr

Sylvie Charbonnier
email: sylvie.charbonnier@univ-grenoble-alpes.fr

Design of a Low Complexity Interference Detector for LPWA Networks

Keywords: Internet of Things (IoT), Low Power Wide Area (LPWA), Interference Detection, Classification, Channel State Information, Channel Analysis Signal, Interference Presence Indicator, Ultra-Low Power Transceivers

Many Internet of Things applications are deployed over shared ISM (Industrial, Scientific, Medical) radiofrequency spectrum bands. With the recent development of Low Power Wide Area (LPWA) wireless networks, the probability of interference and frame collisions has significantly increased. In this context, real-time interference monitoring is essential to provide precious information for network planning, base-station installation site selection, congested area detection, etc. This work presents a novel, low complexity interference detector for mobile LPWA nodes that is designed using a set of experimental data acquisitions. A first classifier is used to detect the presence of interference and shows a detection accuracy of 94%. If interference is detected, a second classifier is used to classify the interference's relative strength into ten classes with a correct classification rate up to 97%. The detector also provides an estimation of the interference's duration with an average relative error of 2% for medium to strong interference levels.

I. INTRODUCTION

The massive deployment of Internet of Things (IoT) applications requires an efficient use of the scarce radiofrequency (RF) spectrum. Current IoT wireless communication protocols are deployed either over cellular networks, using e.g. the NB-IoT protocol, or in shared ISM (Industrial, Scientific, Medical) frequency bands. While cellular systems offer spectrum availability guarantees, spectrum sharing in ISM bands is only enforced through coexistence rules. Historically, ISM bands were used for short-range communication systems with inherently low mutual disturbance probability. The recent development of Low Power Wide Area (LPWA) wireless networks deployed over these same ISM bands has significantly increased the probability of interference for two reasons: First, the order of magnitude greater range increases the probability of co-located networks. This is true for both same-type and for differenttype co-located LPWA communication systems (e.g. networks such as LoRa, Sigfox, IEEE 802.15.4g/k, MYOTY, and so on, that are all deployed in the same ISM bands). Second, the increase in range implies that extremely low payload bitrates must be used. Thus, the resulting very long frame on-air times increase the probability of collisions [START_REF] Rauh | Lpwan occupancy model parameter identification for license exempt sub-ghz frequency bands[END_REF]. For example, recent research has shown that LoRa networks based on an ALOHA medium access protocol quickly become interference limited when node density increases, thus necessitating new interference-related performance metrics [START_REF] Georgiou | Low Power Wide Area Network Analysis: Can LoRa Scale?[END_REF].

The present work focuses on the design of a low complexity, real-time interference detector for LPWA transceivers. Our objective is that, every time the transceiver correctly receives a frame, the detector simultaneously outputs information on the presence or absence of interference, and, if applicable, information concerning the interference's relative strength and duration. This interference-related information gathered by the wireless node can be used in many ways. An obvious application is network planning and selection of base station installation sites. Indeed, since the information is gathered by the nodes themselves, the network installer will dispose of very precise information concerning congested areas and be able to optimize the base station placement accordingly. Alternatively, interference-related information can be used by adaptive transmission protocols that aim to optimize the physical layer signalling rate in view of either improving throughput or saving energy when favourable RF propagation conditions have been detected. Detailed per-frame interference-related information can be exploited to fine-tune channel adaptation strategies.

This paper focuses on the design of an information-rich interference detector for ultra-low power transceivers typically employed in LPWA IoT applications. To comply with the extremely tight energy budget of these applications, our detection mechanism avoids the transmission of dedicated frames or symbols and avoids burdening the wireless node's processing capacity with algorithms that require high computational complexity. In addition, to ease the development of new MAC or network-level adaptive communication protocols, the information delivered by the detector is clear, simple to use, precise, and reflects the instantaneous propagation conditions.

To address this challenge, our strategy is to exploit channelrelated information contained within the received signal itself. To this end, a channel analysis signal (CAS) is defined in the digital base-band (DBB) processing part of the wireless receiver. The CAS is then processed to calculate two features per frame and which are used as inputs to classifiers. For each correctly received frame, the proposed detector is able to detect the presence or absence of interference, and if required provide an indication of the relative strength and duration of the interference. While the work described below is based on the LECIM FSK physical layer of the IEEE 802.15.4-2015 standard, the approach can easily be generalized to other LPWA communication standards.

The contributions of this paper are:

• A low complexity single-frame interference presence indicator (IPI) for LPWA transceivers based on a monofeature classifier and with 94% accuracy; • A 10-level interference relative-strength classifier with an accuracy up to 97% built upon two minimal complexity features; • An interference duration estimator;

• An experimentation-based classifier design methodology. This paper is structured as follows: relative work is discussed in Section II, followed by a presentation of the experimental platform and data collection in Section III. The design methodology for our interference detector and interference strength estimator is proposed in Sections IV and V, respectively. Finally, experimental results are discussed in section VI.

II. RELATED WORK Interference detection mechanisms have been developed

and employed in the field of wireless communications for many different applications and in many differing contexts. In particular, blind interference detection techniques have been extensively investigated for cognitive radios (CR) where transceivers must compete for spectrum use while avoiding the interference caused by other communication systems sharing the same frequency band. For example, a pilot-aided interference detection method was proposed in [START_REF] Li | Robust joint interference detection and decoding for OFDM-based cognitive radio systems with unknown interference[END_REF] to allow robust orthogonal frequency division multiplexing (OFDM) signal detection in the presence of in-band interference. A pilotfree interference estimation technique was proposed in [START_REF] Zhao | A frequency-domain estimation scheme for single-tone interference in ofdm systems[END_REF] for similar OFDM-based cognitive radios. More generally, many different spectrum sensing techniques have been developed in the cognitive radio context, for example using hidden Markov modeling [START_REF] Coulson | Spectrum sensing using hidden markov modeling[END_REF].

Interference detection techniques have also been proposed for ISM radio communications where many different propa-gation schemes share the same spectrum. For example, for the 2.4 GHz ISM band, an approach to detect and minimize the impact of interference caused by Bluetooth packets to IEEE 802.11g OFDM-based transmissions is proposed in [START_REF] Vogeler | Blind bluetooth interference detection and suppression for ofdm transmission in the ism band[END_REF]. The authors exploit the OFDM demodulation algorithm to extract useful information from the symbol magnitude for each subcarrier obtained after the demodulation FFT. In [START_REF] Jamieson | Ppr: Partial packet recovery for wireless networks[END_REF] and [START_REF] Vutukuru | Cross-layer wireless bit rate adaptation[END_REF], the authors propose a PHY-independent interference detection mechanism based on the detection of abrupt changes in a BER estimate calculated for each received symbol. While very efficient, this approach requires that a large number of per-symbol confidence data, gathered within the DBB, be transferred to the upper-layer protocol layers for processing. Alternatively, the authors in [START_REF] Barac | Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments[END_REF] propose a very simple interference detection mechanism based on the analysis of the distribution of coded bit errors within a frame. Unfortunately, this approach implies that frame data be necessarily encoded and limits interference detection to frames received at low SNR, since bit errors must absolutely be present.

Finally, in [START_REF] Wu | Chip Error Pattern Analysis in IEEE 802.15.4[END_REF], demodulated chip error patterns of 2.4 GHz IEEE 802.15.4 transmissions are analyzed to infer the wireless link conditions, both to uncover the reasons for frame loss and also to determine whether interference is present, even when no frame is dropped. The extensive experiments performed by the authors focus on the transitional region of a wireless link which is characterized by highly volatile link conditions and thus in which received signal power is barely above the receiver's sensitivity. While a channel classification heuristic is proposed by the authors, little effort is committed to the explanation of observed error patterns with respect to the channel conditions. Compared to the above related work, our work focuses on LPWA transmission schemes which, with respect to OFDM signalling schemes, are generally of lower complexity. Consequently, we focus on low complexity interference detection mechanisms.

III. EXPERIMENTAL PLATFORM AND DATA COLLECTION

Since commercial off the shelf (COTS) devices do not allow access to DBB algorithms, a software defined radio is necessary for experimentation. The aim of the experiments is to gather realistic data for channel analysis, IPI choice, and data-set collection for classifier training and validation. After the presentation of the SDR-based experimental platform, a brief description of the IEEE 802.15.4-2015 LECIM FSK PHY standard is given, and the labelled data-set collection procedure is described.

A. SDR-Based Experimental Platform

The SDR platform is composed of three universal software radio peripherals (USRP) located in a 5 m × 4 m room. The three USRP are programmed to support the LECIM (Low Energy Critical Infrastructure Monitoring) FSK PHY of the IEEE 802.15.42015 standard [START_REF]IEEE Standard for Low Rate Wireless Networks-IEEE Std[END_REF]. The first USRP is used as transmitter (T x), the second as interference source (Ix) and the last as receiver (Rx). The USRP are N210 models [START_REF]USRP Hardware Driver and USRP Manual-USRP2 and N2x0 Series[END_REF] [START_REF] Tada | High deployability of ieee 802.15.4k dsss systems in interference dominated bands[END_REF]. This physical layer employs the GMSK modulation at 37.5 kchip/s with optional direct-sequence modulation spreading factors of 2, 4, 8 or 16. The frames generated for the experiments are composed of a 4-byte preamble, 3byte start of frame delimiter (SFD), 2-byte header, 1250byte payload and 2-byte cyclic redundancy check (CRC) code. The 2-byte CRC is based on the generator polynomial G(x) = x 16 +x 15 +x 2 +1. Convolutional 1/2 rate forward error code with constraint length K = 7, optional in the standard, is used in all of our experiments along with a spreading factor of 4. Including coding and frame synchronization overheads, the resulting frames have an on-air time of 2.15 seconds.

A digital baseband receiver able to detect FSK-modulated frames was designed and is presented in Fig. 1. Indeed, with A[k] defined as the complex output of the channel filter (FIR2), it can be shown that x

[k] = ℑ(A[k] × A * [k -1]
) is a good approximation of the instantaneous frequency. Synchronization and SFD detection is handled by blocks in blue which perform a correlation with the known preamble sequence. These blocks calculate the optimal decimation moment thus allowing the sample stream to be decimated by a factor 8. Demodulation is performed by yellow blocks while green blocks compute the channel analysis signal (CAS) as well as the features used by our interference detector (DDSL and ADA) which are described below.

C. Labelled Data Collection

The T x power is chosen such that the received signal is approximately 15 dB above the Rx sensitivity. This relatively high SNR level is typical of real-world deployments since it allows for a comfortable fading margin. Labelled datasets are produced by two experiments. In the first one, 300 frames are acquired in an undisturbed RF environment and labelled accordingly. In the second experiment, an interference environment is generated using Ix as an interference source. The interference is an IEEE 802.15.4-2015 LECIM FSK PHY signal emitted in the same frequency band as that of T x. The emission duration of Ix is randomly selected in the range [30; 1700] ms. To account for variable interference levels, ten interference environment data-sets are collected, each obtained using a different baseband signal amplitude 'a' applied to the samples transmitted by Ix. Values of a are selected from 0.1 to 1 with a step of 0.1 in order to cover a 20 dB dynamic range for received interference power. Thus, values of a close to 0.1 and 1 respectively correspond to low and high levels of interference. The Ix USRP gain is configured such that, when a = 1, frames received by Rx contain bit errors which are corrected by the forward error correction decoding process. In this configuration, the measured signal to cochannel interference ratio (CIR) is approximately 4 dB, which is consistent with the expected sensitivity of an FSK-based spread-spectrum receiver. In all of these experiments, only CRC-valid frames were analyzed and collected for feeding the classifier training and evaluation steps. For each value of a, a set of 100 frames was acquired to which was assigned the label 'disturbed' as well as the label 'l', with l = 10 × a. Thus a total of 1000 frames was collected in this second experiment.

IV. INTERFERENCE DETECTOR DESIGN METHODOLOGY

In this section, the proposed channel analysis signal (CAS) and interference presence indicator (IPI) are presented. Then, the methodology for designing the interference detector and interference duration estimator is detailed.

A. Channel Analysis Signal (CAS)

In [START_REF] Jamieson | Ppr: Partial packet recovery for wireless networks[END_REF], [START_REF] Wu | Chip Error Pattern Analysis in IEEE 802.15.4[END_REF] and [START_REF] Vutukuru | Cross-layer wireless bit rate adaptation[END_REF], the receiver's digital baseband (DBB) is instrumented to compute channel analysis signals (CAS) for channel diagnostics. Similarly, the CAS proposed in this work is extracted directly from the receiver's DBB. In our case, the CAS corresponds to the (averaged) absolute value of the input signal of the soft de-spreading block, x[k]. The CAS is thus proportional to the probability that the received symbol is correct. More formally, the CAS is defined as: In order to smooth the signal |x[k]| and to highlight the interference footprint, a moving average (MA) over a computation window of win samples is used. The choice of win must be compatible with the shortest detectable interference duration. For the given 37.5 kHz decimated sample rate, a value of win = 1000, corresponding to T win = 26.6 ms, is selected. Finally, we define CAS = [CAS 1 , ..., CAS N ] as the channel analysis signal, where N is the total number of samples in the frame. Fig. 2 shows an example of the CAS calculated for a frame received in the presence of interference. In this figure, we can easily recognize the temporal footprint of the disturbance corresponding to the presence of a strong interference. The computation of the CAS signal only requires a modulus operation and a moving average filter allowing this approach to be easily generalized to all FSK-based IoT standards (e.g. Sigfox, Bluetooth). Other signalling schemes can similarly be addressed by reusing the soft demodulator input signal of the corresponding DBB receiver.

CAS k = 1 win win-1 j=0 |x[k -j]|. (1)

B. Interference Presence Indicator (IPI)

Our aim is to find a single-value, low complexity IPI that is able to accurately extract the interference footprint information from the CAS. To this end, we propose to use the DDSL which measures the Difference between the averaged Disturbance and Signal Levels. The DDSL summarizes into one numerical value the strength footprint of the interference (disturbance caused by the emission of Ix used as interferer). Let µ CAS be the mean value of the CAS over the frame. U CAS k is the set of CAS values such that CAS k > µ CAS and, correspondingly, I CAS k is the set of CAS values such that CAS k ≤ µ CAS (cf. Fig. 2). DDSL is computed as (2) with M and P the number of samples respectively contained in the sets U CAS k and I CAS k , with M + P = N .

DDSL = 1 M M k=1 U CAS k - 1 P P k=1 I CAS k (2)

C. Mono-Feature Classifier Based Detector

The DDSL is used to classify each frame into two classes: 'undisturbed' and 'disturbed', with a detection threshold, λ. The 'disturbed' class is selected when DDSL is higher than λ. The value of λ is learned using a training data set from an empirical ROC curve. The ROC curve displays the true positive rate as a function of the false positive rate for all possible values of λ. A true positive is defined as a frame with interference correctly classified as 'disturbed'. A false positive is defined as an undisturbed frame wrongly classified as 'disturbed'. The performance of an ideal detector is determined by a 100% TP and a 0% FP, corresponding to the point {FPR(λ), TPR(λ)}={0,1}. The optimum value of λ is found by choosing the point on the ROC curve with the minimum distance from the ideal point (0,1). The minimizing equation is described by [START_REF] Li | Robust joint interference detection and decoding for OFDM-based cognitive radio systems with unknown interference[END_REF].

λ opt = argmin λ F P R(λ) 2 + (1 -T P R(λ)) 2 1/2
(3)

D. Interference Duration Estimator

Once a frame is detected as 'disturbed', the duration of the interference can be calculated from the CAS signal. The estimated interference duration τ is equal to the number of samples contained in the I CAS k set, P , divided by the decimated sample rate (F s,d ). τ is computed by [START_REF] Zhao | A frequency-domain estimation scheme for single-tone interference in ofdm systems[END_REF].

τ = P F s,d (4)
Alternatively, an estimation of the relative interference duration, defined as P/N , could also be calculated. Depending on how the interference detector data is exploited (e.g. network planning or adaptive protocol design), one or the other information might be more useful.

V. INTERFERENCE STRENGTH ESTIMATOR

In this section, each frame previously classified as 'disturbed' is further classified into one of ten classes, C = 1, 2, ..., 10, corresponding to the previously defined labels l = 1, 2, ..., 10 related to the interference strength.

A. Average of the Disturbance Area

While only DDSL is needed for the interference detector, the 10-level classification requires the use of an additional feature, the average of the distance area (ADA), to perform at its best. ADA gathers additional details concerning the temporal footprint of the interference. ADA is defined as the area between the I CAS k and the µ CAS curves (cf. Fig. 2) divided by the number of times the average, µ CAS , is intersected by the CAS k signal. The computation of ADA is described by [START_REF] Vogeler | Blind bluetooth interference detection and suppression for ofdm transmission in the ism band[END_REF]. The calculation of ADA requires only a few mathematical operations since 1 P P k=1 I CAS k and µ CAS have been previously computed. Also, the comparisons used previously to separate the I CAS k and U CAS k sets can be simultaneously used to compute the denominator of [START_REF] Vogeler | Blind bluetooth interference detection and suppression for ofdm transmission in the ism band[END_REF].

S k = sgn(CAS k -µ CAS ) (5)
ADA = 2P (µ CAS -1 P P k=1 I CAS k ) N k=2 | S k -S k-1 | (6)

B. 10-Level Interference Classification

The ten-level interference classifier uses ten multidimensional centroids. Each frame i is represented by a set of 2 features f i = [DDSL i , ADA i ]. In the feature space, each class C is represented by its centroid and dispersion. The centroid is defined as µ C = [ DDSL µ C , ADA µ C ] corresponding to the mean values of DDSL and ADA for each class. The dispersion of each class is represented by

V C = [ DDSL σ 2 C , ADA σ 2 C ]
, the variance values of DDSL and ADA. The values µ C and V C are learned for each class using its corresponding learning data set. For a test frame f i , the Mahalanobis squared distance between f i and the centroid of class C, D i,C , is calculated for each class using [START_REF] Jamieson | Ppr: Partial packet recovery for wireless networks[END_REF]. The covariance matrix of each class is considered to be diagonal.

D i,C = (f i -µ C )(diag(V C )) -1 (f i -µ C ) T (7)
C i = argmin C {D i,C } (8)
The class assigned to f i , C i where C ∈ N [1,10] , is the class whose D i,C is the smallest of the ten computed. Note that, for each class, the computation of ( 7) requires only 2 subtractions, multiplications and divisions and one sum.

C. Classifier Performance

To evaluate the performance of this classifier, a correct classification rate can be computed for different requirement levels. The γ-correct classification rate, R ±γ , is given by ( 9), counting as correct any frame classified into the γ adjacent classes. In (9), [...] are the Iverson brackets, [B] is defined to be 1 if B is true, and 0 if it is false and N T is the number of frames contained within the test set.

R ±γ = 1 N T NT i=1 [| C i -l i |≤ γ] (9)
VI. RESULTS

A. Disturbed versus Undisturbed Classifier

A learning set made of 150 'undisturbed' frames and 150 'disturbed' (i.e., 'interference') frames randomly chosen among the 1000 samples labelled as 'interference' (cf. section III-C) is created. This set is used to build the ROC curve displayed in Fig. 3. Using (3), the optimum operating point (FPR=0.04, TPR=0.82) is found with λ opt = 3.4×10 -3 . Fig. 4 shows clearly that it is possible to use DDSL to discriminate 'disturbed' frames from 'undisturbed' ones, with λ opt as a good boundary. The presence of some error points in the DDSL distribution is explained by the fact that the ROC curve never reaches the ideal point (0,1) meaning that the classifier is not flawless. Next, classifier performance is evaluated from a test set consisting of 300 labelled frames (150 frames per class) different from those contained in the learning set. The detector satisfies a correct detection rate of 94%.

B. Interference Duration Estimator

To validate and compute the interference duration estimator performance, an additional 400 frames are collected, this time with controlled interference duration. In order to generate correct validation data-set, frames transmitted by Ix are set to have on-air times of 0.4 s separated by 0.4 s pauses while T x transmits 2 second frames separated by 2 second pauses. In this way, we are certain that each acquired frame is impacted by at least 0.8 seconds of interference. The extraction of the I CAS k set is slightly modified to include only samples corresponding to a complete interference signal. To emulate various levels of interference, randomly generated values of a between 0.2 and 1 are applied to Ix. The interference duration estimator accuracy is evaluated computing the root mean square error (RMSE) and the average relative error (δα r ) between the estimated duration value τ and the preset value τ . Fig. 5 shows the evolution of the RMSE and the average relative error δα r for different values of the interference's complex signal amplitude a (cf. section III-C). The proposed interference duration estimator is very accurate for a > 0.5, corresponding to medium to strong interference with a RMSE < 0.044 s and a δα r < 2%. Even if the precision of the estimator is less accurate for lower-level interference, the estimator can still provide valuable information that can be used by an adaptive protocol that attempts to improve communication link by modifying frame length.

C. Interference Strength Classifier

The interference strength classifier is trained using a training set of 500 samples (50 samples per class). Next, the classifier performance is evaluated using a validation set consisting of the remaining 500 samples. The performance is summarized by the confusion matrix displayed in Fig. 6. Each row of the matrix shows the classifier output (in percentage) for the labelled set identified on the vertical scale. Thus, the gray-scale number located at (i,j) is the percentage of samples classified as belonging to class i while belonging to set j. Correct are presented in the diagonal while classification errors are represented by results outside of the diagonal. As expected, Fig. 6 shows a greater number of classification errors for classes 1, 2, 3 and 5 while, for the rest of the classes, the classification results are distributed in the diagonal area reflecting a good classification performance. Three γ-correct classification rates are computed: R ±0 = 42%, R ±1 = 74% and R ±2 = 97%. This last value means that the classifier practically never assigns the frame into a class further than 2 classes away from the correct one. This shows its ability to accurately evaluate the level of the interference strength. Most importantly, the classifier never confuses a low interference frame with a strong interference frame.

VII. CONCLUSION

In this paper, we present an information-rich interference detector able to detect the presence of a simultaneous transmission in a wireless channel while estimating its duration and relative strength. Since the detector provides this data for each correctly received frame, it is a valuable tool for monitoring the RF propagation conditions in real time. The low complexity detector is built upon a channel analysis signal (CAS) extracted directly from the receiver DBB, two novel feature extractors (DDSL and ADA) and two classifiers. Low computational complexity is an important requirement for all elements of the detector. The first classifier is able to detect the presence of interference with 94% accuracy. The second is able to discriminate between low, medium and strong interference levels with 97% accuracy. A reliable estimation of the interference duration, with an average 2% relative error when the level of interference is medium to strong is also proposed. The approach presented in this work can be easily generalized to other LPWAN standards. Future work will include experimentation with other types of interference sources (e.g. LoRa, Sigfox) to validate the classifier performance in generalized scenarios.

Fig. 1 .

 1 Fig. 1. IEEE 802.15.4-2015 LECIM FSK PHY digital baseband (DBB) receiver algorithm with CAS, DDSL and ADA computation.

Fig. 2 .

 2 Fig. 2. Channel Analysis Signal (CAS) with an 'undisturbed' part ( U CAS) in blue and an 'interference' part ( I CAS) in red. µ CAS is the mean value of the entire CAS.

Fig. 3 .Fig. 4 .

 34 Fig.3. ROC curve for classifying 'disturbed' versus 'undisturbed' frames with λ as the varying threshold parameter using the DDSL feature.

Fig. 5 .

 5 Fig. 5. Evolution of the average relative error δαr and the RMSE of the duration estimation as a function of the interference's complex signal amplitude 'a'.

Fig. 6 .

 6 Fig. 6. Confusion matrix represented in gray scale computed from the test set classification.

  from Ettus Research with WBX [40 MHz -2200 MHz] daughter-board. All experiments employ a carrier within the 869.3-869.[START_REF] Zhao | A frequency-domain estimation scheme for single-tone interference in ofdm systems[END_REF] MHz ISM band which is not limited by duty cycle in France if transmissions are below 10 mW. Attenuators are inserted between each emitting USRP output SMA port and the half-wave dipole antenna which allows us to limit the transmission range to approximately two meters which is necessary for leading controlled experiments. Each USRP is an RF front-end which performs up/down frequency conversion, analog filtering and DA/AD conversion. The USRPs are driven by GnuRadio through a Gbit Ethernet connection to a PC. The transmission and reception DBB algorithms are implemented in MATLAB which provides/receives complex baseband data at a sample rate of 300 kHz to/from GnuRadio.

	B. IEEE 802.15.4-2015 LECIM FSK PHY
	Since FSK-based modulations are extremely common in
	ultra-low power communication systems, the experiments de-
	scribed in this work are based on the IEEE 802.15.4-2015
	LECIM FSK PHY which extends the range of conventional
	IEEE 802.15.4 physical layers through spread spectrum tech-
	niques

ACKNOWLEDGMENT

This work was supported by the LabEx PERSYVAL-Lab (ANR-11LABX-0025-01).


Ritu Raj Shrivastwa
email: ritu-raj.shrivastwa@grenoble-inp.fr

Ahmad Hably
email: ahmad.hably@grenoble-inp.fr

Kaouthar Melizi

Seddik Bacha

Understanding Microgrids and Their Future Trends

Keywords: Microgrids, distributed generation, Microgrid architecture, electricity markets, optimization

Depletion of natural resources to meet power demands has revolutionized the use of Renewable Energy Sources (RESs).The paradigm shift from the centralized to distributed control is witnessed due to the Microgrids. Different configurations using smart grids and Microgrids are expected to ensure grid stability and security.Eventually, electricity market is subjected to change due to the projected changes in the grid architecture. This paper is a review on the Microgrids, its elements and the controllability. This paper discusses the major issues in the Microgrids, the factors affecting the choice of the Microgrid type and also various generation sources and their combination for reliable power quality and control. Recent developments with future trends are also addressed in this paper.

I. INTRODUCTION

Changes in the global climatic conditions has raised a demand in the use of RESs irrespective of the changes in the control and efficiency of the existing utilities. Demand side installations of distributed generation (DG) sources marks a upcoming change to the electricity markets due to the increasing number of market players. Passing over an active role to the Distribution Network Operators (DNOs) changing the control from centralized to distributed, the market gives opportunities for new players like ancillary service (AS) providers to participate in the transactions. However, intermittency of the RESs pose a threat to stability of the grids. Also, penetration of power sources should be done at specific nodes to avoid voltage imbalances [START_REF] Quezada | Assessment of energy distribution losses for increasing penetration of distributed generation[END_REF]. Hedging of the investment in the electricity market is becoming easier with the introduction of the Microgrids enabling the players to engage in different markets.

Major classifications of Microgrids usually divides them as physical Microgrids (PM) and commercial Microgrids (CM). In PM all the sources and the loads are connected through a point of common coupling (PCC), where as the concept of PCC does not exist in CM which means that the loads and sources can lie on either side of the LV or MV lines. The PM are used for meeting the technical demands and the supply demand balance in the grid however, the term CM is used often to meet the financial aspects of the electricity market. Also, synchronous and non-synchronous sources being used in the Microgrids affect the stability drastically depending upon their share. This paper is organized as follows. In section II we discuss about the types of the Microgrids followed by section III and section IV where different sources and storages are discussed. Effect of the Microgrids on the power grids is discussed in section V. Section VI and VII talks about the economics and the optimization and control of Microgrids and finally the review is concluded with the future prospects in section VIII.

II. TYPES OF MICROGRIDS

The choice of Microgrids highly depends upon the criteria like stability, connectivity, total capacity, available sources and available infrastructure.

Connectivity with grid has a vital role to play in the stability and choice of current for operation in the Microgrid. Due to the massive existing infrastructure of the AC, most of the grid connected Microgrids tend to operate in the AC (alternating current) domain however, the isolated Microgrids preferably operate in LVDC which results in low losses and also has a possibility to be connected to the grid in the future with advanced PE devices. However, depending on the sources employed in the microgird, connectivity may affect the overall inertia of the grid to which the Microgrid is connected.

Stability can be a major game changer in the deployment of the Microgrids. Strategic control structures are needed to counter the non-synchronous nature of the generating sources. Exchanges through tie lines are possible for better stability in the Microgrids because of which the grid connected Microgrids tend to have better stability than those of islanded operations. Use of secondary storages with RESs can significantly improve the delivery profile of the system.

Total capacity of the Microgrid also determines the threat of imbalance due to small changes. The load-generation ratio is a critical balance in low capacity grid. However, this consideration significantly changes with the Microgrids operating under large capacities of both generation and consumption because larger is the generation and load of the system greater is the inertia and lesser is the swing due to the change in the loading conditions or the generated power. Similarly, the capability of the available sources to control deviations in grid is highly considered in designing. Sources like PV and wind are uncontrollable and thus are put to the category of nonsynchronous and poses a threat to the stability. Also, some generate DC and others generate AC and also at different levels, these factors also determines the choice of the operating voltage and current. However, if the available infrastructure is of AC then preferably the installed sources will deliver AC power for consumption. But new Microgrids which are recently being constructed can be made to work efficiently with DC power with LVDC.

Based on the above discussions, the Microgrids can be classified into DC, AC and Hybrid Microgrids.

The term AC Microgrids, as presented in figure 1, refers to the Microgrids with AC power. Most of the installed or available infrastructure corresponds to AC. All the loads and sources are connected at the point of common coupling (PCC). Exchange of power among the sources and loads and also grid connected operation can be done through the PCC. In terms of grid stability, with the advanced PE converters with the capability of regulating the active and reactive power, AC Microgrids are feasible to participate. Also, conventional sources like diesel generators can also be utilized in cooperation with the other sources to stabilize the grid under disturbances. Most of the protection devices manufactured by the companies focus on the AC systems, thus feasibility of having a AC Microgrid is always more in today's scenario. Due to high use of PEs inverters, the DC power generated by the sources can be efficiently converted to AC power to be used in the AC grids [START_REF] Shimizu | Flyback-type singlephase utility interactive inverter with power pulsation decoupling on the dc input for an ac photovoltaic module system[END_REF]. Sources like PV systems, a major proportion of the sources in DGs, generate power in DC. Also, through the use of DC generators in the micro wind and micro hydro units can be an option towards DC Microgrids. In literature, many authors have suggested use of control mechanisms for the peer power exchange for Microgrid. Smart power routers (SPR) in [START_REF] Nguyen | Smart power router: A flexible agent-based converter interface in active distribution networks[END_REF] can be a good substitute to the centralized Microgrid central controller (MCC) [START_REF] Kaur | A review on Microgrid central controller[END_REF]. SPRs can play a vital role in the future grids in not only controlling the power flow but also in management of the cost of the electricity of the owner. In such grids, the loads are usually all DC loads and the level of operating voltage can also vary in the range 48V to 310V or other levels depending upon the loads, communication system used or the designers. In comparison to the AC Microgrid, the DC Microgrid is simpler as it does not have physical quantities like reactive power, and frequency. Also, phase consideration is absent which makes the coupling of DC devices and sources simpler. A simple representation of the DC Microgrid can be depicted as in figure 2.

According to the data from world bank, approximately 14% of the world's population untill 2016 had no access to electricity [5]. The reasons being economical, political as well as technological. The extreme expense on the power power lines is among the reasons to have an AC or DC Microgrid for these places [START_REF] Radwan | Networked control and power management of AC/DC hybrid Microgrids[END_REF]. In hybrid Microgrids, both AC and DC loads and sources can be present [START_REF] Radwan | Networked control and power management of AC/DC hybrid Microgrids[END_REF]. PCC will have an important role in controlling the power flow. In [START_REF] Loh | Autonomous operation of hybrid Microgrid with ac and dc subgrids[END_REF], operation with AC/DC loads and generation is portrayed. Different control algorithms can be used depending upon the location of the installations [START_REF] Liu | A Hybrid AC/DC Microgrid and Its Coordination Control[END_REF]. Home Area Network (HAN), or Wide Area Network (WAN) being used by the Smart Home Management Systems or MCC respectively are some wide examples of modern day architectures of the Microgrids. Also, the control mechanism of hybrid grids is complex than that of simply AC or DC Microgrid due to the increase in the control parameters like reactive power, phase compensation and frequency [START_REF] Hossain | Design of robust distributed control for interconnected Microgrids[END_REF]. Several authors have studied the real time operation of the hybrid Microgrid and analysed the control structures as in [START_REF] Gaztanaga | Real-time analysis of the control structure and management functions of a hybrid Microgrid system[END_REF]. In view of this explanation, figure 3 is how a hybrid Microgrid structure looks like. Also, integration of Power Electronic (PE) converters with PV systems as in [START_REF] Xue | Towards next generation photovoltaic inverters[END_REF] and [START_REF] Smith | Smart inverter volt/var control functions for high penetration of pv on distribution systems[END_REF], the landscape of DG is changing drastically. Also, new micro-hydro and wind turbines are also contributing to it. However, DG sources are not all stable or continuous sources and they highly depend on climate and geographical locations. The intermittency of the sources affect the grid stability at large in the Microgrids. Use of storage systems alongside RESs are a better way to supply stable and constant power [START_REF] Pepermans | Distributed generation: definition, benefits and issues[END_REF] besides increasing the overall cost of the unit. Primary sources in DG are PV systems, microwind, micro-hydro, biomass and diesel generators divided into two major categories based on the basis of participation in grid control i.e. synchronous and non-synchronous sources. Intermittency in the wind speed leads to unprecidented generation from wind leading to low grid inertia. Where as, the sources like diesel and hydro can be controlled to respond to the power fluctuations and are referred to as synchronous sources maintaining grid inertia.

A. Photovotaic (PV) systems

Sun's heat and light has been a source of energy for humankind for many years. Use of solar insolation for generation of electric power is a major breakthrough for DG. In [START_REF] Jean | Pathways for solar photovoltaics[END_REF], the authors have discussed about the scenario of solar and its advantages in comparison to the conventional power sources. Advanced PE converters have shaped a way for solar power in the grids. Large scale solar farm to rooftop solar installations are widely in application throughout the world. In most of the parts of the world, insolation is approx. 1000W/m 2 . But due to low efficiency of the solar panels, the tapped energy is only 20% of the the total insolation. Rooftop solar panels are bringing a paradigm shift in the distribution network with increasing installations and allowing the consumers home to grid services. In order to obtain the capacity of the solar for any specific application, Equation 1 can be used:

P solar = E req /(ηH sun ) (1)
where, P solar is the required capacity of the solar panels. E req is the energy required for the whole duration and η is the efficiency of the panels with H sun being the hours of sunlight in that location [START_REF] Inam | Architecture and system analysis of Microgrids with peer-to-peer electricity sharing to create a marketplace which enables energy access[END_REF]. Standard solar panel manufacturers manufacture the panels in the range of 250W. Depending upon the applications the size of the panels may vary but with the decrease in size the cost per watt of the panels increases [START_REF] Campanella | An analysis of the viability and competitiveness of dc Microgrids in northern india[END_REF]. Thus, the trade off between the size and cost is a major parameter in determining the installation in the Microgrids [START_REF] Bacha | Photovoltaics in Microgrids: An overview of grid integration and energy management aspects[END_REF]. Also, the PV generation in association with the electrical storage systems (ESS) can be used to participate in LFC.

B. Micro-wind and micro-hydro turbines

Conversion of the kinetic energy of the wind to electrical form or to other required forms has been a practice since ages. Wind power has been utilized to irrigate agricultural field and in recent years to light homes through the wind farms generating power. However, using the wind turbines for lower level of generation has been a challenge but in recent years it has been accomplished through the micro turbines with the capability of generating from watts to few kilowatts. The generalized equation as in Equation 2 for the generation of power through wind is given by:

P ower(P ) = 0.5ρAC P V 3 N g N b (2)
Where, ρ is the air density in kg/m 3 , A is the rotor swept area in m 2 , C P is the coefficient of performance, V is wind velocity, N g is the generator efficiency and N b is the gearbox bearing efficiency. In the review [START_REF] Tummala | A review on small scale wind turbines[END_REF], the authors have discussed about the different possibilities of the wind turbines like horizontal and vertical axis for the domestic use with the designs and their characteristics. The patent [START_REF] Murphy | Savonius wind turbine[END_REF] discusses about savonius, the possible micro wind generation turbine for domestic and low voltage generation. In similarity to the PVs, wind also can participate in LFC but with the ESS [START_REF] He | Cooperation of wind power and battery storage to provide frequency regulation in power markets[END_REF].

Since ages water has been the sources of energy and life. The kinetic energy of the flowing water has been used in irrigation, powering windmills for harvesting the crops and in many other applications. With the use of water for generation of electricity with the application of a simple equation as in Equation 3, a reliable sources was aggregated in the conventional sources.

P = HF g; (3)
Where, P is the power that can be generated in W atts with a head of height H in meters and g is the acceleration due to gravity in m/s 2 . The available power is not completely harvested due to the involvement of the electromechanical units like turbines and synchronous generators. Frictional losses and other losses in the generation modules constitute a considerable sum of the generated power. However, to put hydro power in renewable resource or not has always been a matter of discussion. However, only large dams were used for generation of power due to the limitations on the efficiency of small generating units. But with recent developments in the field of micro hydro units as in [START_REF] Guo | Variable speed micro-hydro power plant: modelling, losses analysis, and experiment validation[END_REF] and [START_REF] Marquez | Dynamic modeling, simulation and control design of an advanced micro-hydro power plant for distributed generation applications[END_REF] can be sought as a future distributed generation resources. With minimum construction requirements, generation of power using microhydro turbines can vary from watts to few kilowatts as presented on website [23], which introduces a new concept of level differential hydro turbine model for micro generation.

In the designs, the flow of water can be controlled and thus the power generated can be in the sync with the power demanded. Thus, this type of generation unit increases the inertia of the grid.

C. Biomass and diesel generators

Largely available but scarcely used is one of the best and reliable sources of energy i.e. biomass. Use of biomass has been into production of compost, green fuels and for heat through burning [START_REF] Hoogwijk | Exploration of the ranges of the global potential of biomass for energy[END_REF]. Biomass is considered as one of the best sources for generation of electricity either directly or through its byproducts. Gasification of biomass [START_REF] Caputo | Economics of biomass energy utilization in combustion and gasification plants: effects of logistic variables[END_REF] and then using the gases for generating electricity through the fuel cells or simply exploiting the calorific content of the biomass is a worldwide applied technique. Biomass is finding its applications in the Microgrids with time, because of the offered controllability and participation in the control methods [START_REF] Stergaard | A renewable energy scenario for aalborg municipality based on lowtemperature geothermal heat, wind power and biomass[END_REF]. The report [START_REF] Mann | Life cycle assessment of a biomass gasification combined-cycle power system[END_REF] presents a elaborated study on the impacts of biomass being used for energy generation on environment but the studies can be utilized for other geographical locations as well. Also, due to its controllability it can be used for the development of the grid.

In the Microgrids, stable operation is highly desired as most of the sources used in the generation are intermittent. Thus, in most of the studies as in [START_REF] Cecati | Smart operation of wind turbines and diesel generators according to economic criteria[END_REF], wind turbines are usually coupled with ESSs or diesel generators to deliver controlled power output for improved efficiency. Different sizing methods and strategies are proposed by authors in their work of which using DFT is mentioned in the work by Xiao et. al. in their work [START_REF] Xiao | Sizing of energy storage and diesel generators in an isolated Microgrid using discrete fourier transform (dft)[END_REF]. Time constant of the diesel generator being low, responding to LFC is a major ability of the diesel generators. Many studies as in [START_REF] Datta | A frequency-control approach by photovoltaic generator in a pv diesel hybrid power system[END_REF] and [START_REF] Bansal | Automatic reactive-power control of isolated wind diesel hybrid power systems[END_REF], portray the idea of using the wind or PV generation units in conjugation with the diesel units for better control and stability. When operated together, the fast response from the diesel generator mitigates the intermittent nature of the natural sources.

IV. STORAGE DEVICES

In the world of intermittent devices the storage system plays a very important role in stabilizing and mitigating the grid frequency excursions [START_REF] Kaur | A colored petri net based frequency support scheme using fleet of electric vehicles in smart grid environment[END_REF]. However, the cost of the system increases with the involvement of the storage. Coordinated use of the DG sources with the storage systems also provides the controllability for LFC. Depending upon the time and purpose of use, the storage devices can vary. The most common storage systems used in the grids are batteries, fuel cells, capacitor banks with either the conventional capacitors or super-capacitors. Different tools such as ragone plots can be helpful in determining the choice of the storage used for specific purposes [START_REF] Christen | Theory of ragone plots[END_REF]. Batteries need charge controller for stable operation. In case where the system is AC, use of PE inverters will be important. Advance PE converters also are impacting the grid with high amount of penetration of harmonics in the grid [START_REF] Enslin | Harmonic interaction between a large number of distributed power inverters and the distribution network[END_REF]. Due to perturbations, storage devices can also be shifted to capacitors where the fast response may help to achieve improved response.

V. EFFECTS ON POWER GRIDS

In the context of additional sources in the conventional grid, penetration of RESs needs many prior investigations and information gathering of the grid to identify the nodes of penetration. Due to the resultant low inertia it is required to identify the node of the grid where the penetration of the RES can be more effective with least effect on the grid stability [START_REF] Bhatt | Analysis of large scale pv penetration impact on ieee 39-bus power system[END_REF]. As mentioned in dispersed way in the previous sections, the effects of the Microgrids on the electrical power grids can be summarized in the following points:

• Reduction of the inertia of the overall system due to the penetration of more and more renewable sources [START_REF] Debbarma | Inertia emulation from HVDC links for LFC in the presence of smart V2G networks[END_REF]. • Need for better and faster control infrastructures is increased which leads to the increment in the capital cost. • More number of control parameters like power tapping from the sources and frequency regulation, maximum power point tracking (MPPT) and ESS management pose a major optimization challenge for the power system engineers.

• Microgrids however can be used to reduce the stress on the coventional generation sources if they are provided with secondary storages or through EVs, which may be effective in handling the frequency excursions through fast responses [START_REF] Strauss | AC coupled PV hybrid systems and Microgrids-state of the art and future trends[END_REF].

VI. ECONOMICS OF MICROGRIDS The energy management and economics of Microgrid are active fields of research which aim to minimize operating costs namely maintenance costs, fuel costs and energy purchase cost from the utility grid [START_REF] Khan | A compendium of optimization objectives, constraints, tools and algorithms for energy management in Microgrids[END_REF]. These formulations of optimization have been classified on the basis of objective functions, constraints, optimization types, solution approaches and tools used to solve the energy management problem. However, the cost of construction of the infrastructures also constitute a major portion in determining the overall operation and costing of the Microgrids [START_REF] Cox | Microgrid infrastructure modeling for residential Microgrids[END_REF].

VII. OPTIMIZATION AND CONTROL OF MICROGRIDS

After an established infrastructure, an optimized control has a great contribution towards the stable and efficient operation of a Microgrid. The control can be exercised in both centralised [START_REF] Hajar | Optimal centralized control application on Microgrids[END_REF] or decentralized control. To optimize the operations of the sources and the grid as a whole several objective functions need to be described. The objective functions in Microgrid are based on geographical area, user preferences, capacity of Microgrid, equipments installed in Microgrid, government regulations, energy storage and generation, types of tariff. However, the optimization of economic dispatch is highly recommended as stated and performed in [START_REF] Hajar | Optimization of a Microgrid with renewable energy and distributed generation: A case study[END_REF] and [START_REF] Ovalle | Decentralized control of voltage source converters in Microgrids based on the application of instantaneous power theory[END_REF]. Some of the methods are:

• Linear Programming: In [START_REF] Nemati | Optimization of unit commitment and economic dispatch in Microgrids based on genetic algorithm and mixed integer linear programming[END_REF] as a universal tool, two dispatch-optimizers are proposed for energy management system. An enhanced mixed integer linear programming (MILP) based method and improved genetic algorithm code have been developed to schedule the economic dispatch and the unit commitment of Microgrid units, furthermore a novel method has been introduced to deal with the limitations of the MILP algorithm for dealing with the non-linear power system topology constraints.

In [START_REF] Jiang | Energy management of Microgrid in grid-connected and stand-alone modes[END_REF] a novel double-layer coordinated control approach for Microgrid energy management is proposed: the schedule layer and the dispatch layer. The schedule layer obtains an economic operation scheme based on forecasting data and the dispatch layer provides power of controllable units based on real-time data. [START_REF] Guo | A two-stage optimal planning and design method for combined cooling, heat and power Microgrid system[END_REF] presents a two-stage optimal planning and design method for combined cooling, heat and power Microgrid system. The stage of a MILP algorithm which determines the optimal operation strategy, and the stage of genetic algorithm (GA) which used to solve the optimal dispatch problem. [START_REF] Li | Microgrid sizing with combined evolutionary algorithm and MILP unit commitment[END_REF] proposes a combined sizing and energy management methodology, formulated as a leader-follower problem.

The leader problem focuses on sizing which is solved using a genetic algorithm while the follower problem : the energy management issue which is solved with a MILP. In [START_REF] Erol-Kantarci | Cost-aware smart Microgrid network design for a sustainable smart grid[END_REF] the authors propose the Cost Aware Smart Microgrid Network (CoSMoNet) design approach which is based on an Integer Linear Programming ILP formulation that allow economic power transactions among SMGs. • Nonlinear Programming: [START_REF] Marzband | Experimental validation of a real time energy management system for Microgrids in islanded mode using a local day-ahead electricity market and minlp[END_REF] presents an EMS algorithm based on mixed-integer nonlinear programming MINLP for MG in islanding mode by different scenarios.

In [START_REF] Comodi | Multiapartment residential Microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies[END_REF] the authors propose a mixed integer non linear programming based consumption framework to evaluate the performance of the hybrid renewable energy system. In [START_REF] Zhao | Shortterm operation scheduling in renewable-powered Microgrids: A duality-based approach[END_REF] the authors solve the operation scheduling problem in renewable powered Microgrids based on integer nonlinear programming. In [START_REF] Logenthiran | Optimal sizing of distributed energy resources for integrated Microgrids using evolutionary strategy[END_REF] the problem is considered as a nonlinear mixed-integer problem which minimizes capital and annual operational cost of DER and a evolutionary strategy was developed for solving the problem.

In [START_REF] Stluka | Energy management for buildings and Microgrids[END_REF] a mixed integer nonlinear optimization to solve the technical challenges is presented. A non linear model is presented in [START_REF] Garcia | Regulation service for the short-term management of renewable energy Microgrids with hybrid storage using model predictive control[END_REF] and [START_REF] Garcia | Optimal economic dispatch for renewable energy Microgrids with hybrid storage using model predictive control[END_REF] the MPC problem is solved as Mixed Integer Quadratic Programming (MIQP). • Dynamic programming: The authors proposed in [START_REF] Nguyen | Optimization in energy and power management for renewable-diesel Microgrids using dynamic programming algorithm[END_REF] a dynamic programming algorithm for a energy optimization analysis and power management in Microgrids with renewable energy resources and diesel generators. In [START_REF] Kanchev | Environmental and economical optimization of Microgrid long term operational planning including pv-based active generators[END_REF] the authors presented the framework of Microgrid control, and performed the long term operational planning of the Microgrid by an algorithm including optimization of the unit commitment problem by a dynamic programming model. A dynamic programming is used in [START_REF] Hino | A versatile clustering method for electricity consumption pattern analysis in households[END_REF] to solve the problem of the electricity consumption patterns in a household which modeled by a mixture of Gaussian distributions. • Stochastic programming: In [START_REF] Zakariazadeh | Smart Microgrid energy and reserve scheduling with demand response using stochastic optimization[END_REF] an stochastic energy and reserve scheduling method with different types of demand responses, to facilitate different types of customers in reserve scheduling and energy is proposed. A multiobjective dynamic stochastic programming approach is presented in [START_REF] Ioakimidis | Design, architecture and implementation of a residential energy box management tool in a smartgrid[END_REF] to maximize comfort preferences and minimize cost. In [START_REF] Bao | A multi timescale and multi energy-type coordinated Microgrid scheduling solutionpart ii: optimization algorithm and case studies[END_REF] the authors proposed an improved particle swarm optimization algorithm (PSO) to solve the Microgrid electricity coordinated scheduling and day-ahead cooling. In [START_REF] Su | Stochastic energy scheduling in Microgrids with intermittent renewable energy resources[END_REF] a stochastic optimization formulation in Microgrid is used. The effectiveness of this proposed approach to minimizes the power losses and the expected operational cost of the Microgrid with intermittent renewable energy resources is demonstrate.

VIII. CONCLUSION AND FUTURE PROSPECTIVE

This paper provides a detailed study of the Microgrids and their components used in the current world. Also, different test benches on which this technology is being tested and different technologies through which it can be implemented. Among all of these, conventional method and methods like blockchain are highly contradicting. However, a paper like this will help the fello researchers to grasp the knowledge of Microgrids and also direct them in specified directions based on their field of research.

Fig. 1 .

 1 Fig. 1. Schematic representation of an AC Microgrid

Fig. 2 .

 2 Fig. 2. Schematic representation of a DC Microgrid

Fig. 3 .

 3 Fig. 3. Schematic representation of a Hybrid Microgrid

ACKNOWLEDGEMENT

This work has been partly supported by the m2M-GRID project of the joint programming initiative ERA-Net Smart Grids Plus.