Houda Amazal

Mohammed Ramdani

Mohamed Kissi

Distributed Multi-Label Classification Approach For Textual Big Data

Keywords: Big Data, Feature Selection, Machine Learning, MapReduce, Multi-label, Text Classification

With the increased generation of data, classification still a hot research topic in machine learning. Though a lot of works in literature are interested in single-label classification, the huge amount of dimensionality of data requires new approach. Thus, multi-label classification has attracted significant attention in the research community over the last years. This task which is an extension of the single-label classification, consists of associating an instance of data (document) with multiple labels; which is practical in many domains such as image analysis, bio-informatics, and text categorization, among others. Besides that multi-label classification is a challenging task, the high dimensionality requires the use of distributed environment to manage data effectively and efficiently. Thus, in this work we propose a distributed system to classify documents using Hadoop framework. Documents are given to the MapReduce framework which assigns the set of positive labels to the documents using a distributed approach based on the Label Powerset method. Experiments on real-life data were carried out to show that the proposed approach can effectively reduce redundant attributes and improve multi-label classification accuracy.

I. INTRODUCTION

With the rapid development of Internet technology, huge amounts of textual data are permanently produced, given arise to big textual data phenomenon. Actually, traditional machine learning techniques are unable to classify and consequently to extract useful information hidden within these data. Another challenge to overcome is that a large part of the data has the inability to belong to only one category (label). Definitely, the data can be associated with one or more categories; this data is called multi-label data. The classification of multi-label data is called multi-label classification or multi-label learning [START_REF] Gibaja | A tutorial on multilabel learning[END_REF], [START_REF] Tsoumakas | Multi-label classification: an overview[END_REF], [START_REF] Streich | Classification of multi-labeled data: a generative approach[END_REF]. Though multi-label classification is a very difficult classification problem, it has attracted increasing attention in research, due to its high application value; for example, a text document may fall into one of the following categories both in the economy and computer categories, an electronic message may be labelled both as work and research project, and so on.

A lot of supervised learning research are focused on the analysis of single-label data.

Single-label classification [START_REF] Li | Recent advances in feature selection and its applications[END_REF], often known as multi-class classification, is the common machine learning task where a document is assigned a single label l, that is chosen from a previously known finite set of labels L. A dataset D of n documents is composed of documentclassification pairs (𝑑 0 , 𝑙 0 ), (𝑑 1 , 𝑙 1 ), … , (𝑑 𝑛 , 𝑙 𝑛 ). Multilabel classification [START_REF] Pereira | Categorizing feature selection methods for multi-label classification[END_REF] is an extension of this problem, where documents are classified with a subset of labels S ⊆ L. A multi-label dataset D of n documents is composed of document-classification pairs (𝑑 0 , 𝑆 0 ), (𝑑 1 , 𝑆 1 ), … , (𝑑 𝑛 , 𝑆 𝑚 ).

Multi-label classification methods can fall into one of the two main categories: a) problem transformation methods, and b) algorithm adaptation methods [START_REF] Pant | Multi-label Classification Trending Challenges and Approaches[END_REF]. Problem transformation methods consists of transforming the multi-label classification problem either into one or more single-label classification. While algorithm adaptation methods recover methods that extend specific learning algorithms in order to handle multi-label data directly.

In the literature, the majority of the proposed methods address multiple label problems by first transforming a multiple label problem into a set of independent binary classification problems, and then using thresholding for multi-label classification. One of the most used technique is the Binary Relevance Method (BR) [START_REF] Kashef | Multilabel feature selection: A comprehensive review and guiding experiments[END_REF], which treats each class as a separate binary classification problem. The BR method has several advantages. It is theoretically simple, intuitive and generally not very complex from a computer point of view, but this approach ignores the dependencies between labels. For this, another popular transformation method called Label Powerset (LP) [START_REF] Abdallah | An Improvement of Label PowerSet Method Based on Priority Label Transformation[END_REF] is preferred. This method creates its single-label problem simply by treating each document's label subset 𝑆 𝑖 as a single label 𝑙. The set of all distinct labels is used as the set of possible labels L for a single-label classifier to choose from. For example, a document (d, S) where S = {a, c, d} is transformed into the single-label representation (d, l) where l = acd. The reverse transformation is evident. The total number of classes the single-label classifier must learn is the total number of distinct label sets found in the training set. But, as the possible combinations of categories are 2 𝐿 , where L is the number of separate labels in the data set, this method may lead to data sets with a large number of classes, the resulting power set data tends to become scarce and therefore makes the classifier's work more difficult [START_REF] Dembczynski | Consistent multilabel ranking through univariate losses[END_REF]. To deal with this, the original Label Powerset technique has been extended and improved. One of its variants is Pruned Problem Transformation (PPT), proposed by [START_REF] Read | A pruned problem transformation method for multi-label classification[END_REF], which eliminates label sets that occur below a certain minimum threshold value, in our work we adopted this method as Pruned Label Powerset 1 (PLP1). However, clearly a lot of information is potentially lost in this approach. For this another approach we noted PLP2 is proposed. It consists of splitting the combination subset 𝑆 𝑖 , into k sub-subsets (𝑆 𝑖0 , 𝑆 𝑖0 , . . , 𝑆 𝑖𝑘 ) where these sub-subsets do occur more than x times in the training data.

In this work we have chosen the TFIDF technique [START_REF] Chase | Learning Multi-Label Topic Classification of News Articles[END_REF], which is one of the most well-known method for feature selection, and we have evaluated it along with a common transformation technique for the multi-label classification. We have also adjusted the TFIDF feature selection technique to handle large multi-label data set directly using Hadoop cluster. To evaluate the performance of this work, parallel Naïve Bayes [START_REF] Amazal | A Text Classification Approach using Parallel Naive Bayes in Big Data Context[END_REF] classifier is used. Our goal is to perform a comprehensive study of the performance of distributed multi-label feature selection techniques based on document frequency and report how it varies when coupled with two different multi-label approaches using large data sets.

The remainder of this paper is organized as follows.

In Section II, we introduce related works. In Section III, we describe the multi-label classification process we adopted. In Section IV, we report the experiments and analysis. Finally, in Section V, we make our concluding remarks and point to directions for future research.

II. RELATED WORKS

Multi-label classification is in very high demand because of its fast growing requirement in applications. In [START_REF] Pereira | Information gain feature selection for multi-label classification[END_REF] authors evaluate the performance of information gain for multi-label classification problem. The work adapted the common transformation methods then employ feature selection measure to select the best features. The goal being to report the performance of multi-label feature selection techniques using information gain measure and state variations when coupled with different multi-label classifiers and data sets.

In [START_REF] Malarvizhi | Multilabel classification of documents with mapreduce[END_REF], an algorithm based on MapReduce is used to assign labels to web documents. The proposed algorithm assigns the set of positive labels to the documents of the web using binary classification of binary classifier. The proposed approach satisfactorily classifies the labels to the documents of the web.

In [START_REF] Song | Improving Multi-label Classification of Documents Using Fine-Grained Weights[END_REF], authors introduced a new fine-grained weight method to improve the performance of the multinomial naive Bayes classification of documents. The idea of fine-grained weight method is to assign a different weight to each world frequency. The result show that the new weighting method could significantly improve the performance of multinomial nave Bayes learning.

In [START_REF] Lee | Multi-label classification of documents using fine-grained weights and modified co-training[END_REF], an extension of the previous work is presented. Thus, to improve multi-label classification of documents using fine-grained weight method, this work uses the dependencies between labels.

In [START_REF] Zhang | Lift: multi-label learning with label-specific features[END_REF], an algorithm for Multi-label learning with label specific features is introduced. The algorithm does clustering analysis on positive and negative patterns and based on that, constructs label specific features. Then it queries the clustering results to perform training and testing.

Multi-label Classification Using Ensembles of Pruned Sets is introduced in [START_REF] Read | Multi-label classification using ensembles of pruned sets[END_REF], this pruned set method (PS) treats sets of labels as single labels. First it converts the multi-label problem into a single label problem. This way the correlation between labels are taken into account. After this, corresponding to each training instance, it considers the label sets as a single class if the label set occurs more than a threshold value. Otherwise the label set is broken into smaller subsets so that it occurs more than the threshold.

In [START_REF] Biswas | Parallelization of Multi-label classification for large data sets[END_REF], authors attempt to improve the performance and accuracy of a multi-label classification algorithm for large data sets, using parallel computing in a distributed manner. The proposed algorithm can reduce the dimensionality of large data sets with very large number of features by removing the redundant features using a feature selection method (Fscore) [START_REF] Murty | Introduction to pattern recognition and machine learning[END_REF] to improve the accuracy and reduce the time taken for training phase of the multi-label classification algorithm.

It can be noted that a lot of works in literature are developed using no distributed environment, this constitutes a limitation of these works since they cannot handle all data for experiments and this will affect their results. The contribution of our research can be resumed on elaborating a parallel system for multi-label classification using parallel Naïve Bayes based on document-based feature selection technique coupled to two variations of Pruned Problem Transformation.

III. METHODOLOGY

This section presents our complete methodology to perform feature selection for multi-label classification problems. Our data set is divided into training data and testing data. Then training data are preprocessed and each document is assigned to its categories. In the PLP1 method, documents belonging to categories with less than a threshold value are eliminated. While in the PLP2 method this kind of documents are assigned to a subcategories. Then documents are loaded in HDFS. Next, the TFIDF score of each feature is calculated using MapReduce, and features of each document are ranked according to their scores. Features having score superior to a threshold value are indexed as relevant and all others are eliminated. The most relevant features are used by a parallel implementation of Naïve Bayes to build the classifier. Then, test documents are introduced to be classified using the previous classifier. These steps are described in Fig. 1.

IV. EXPERIMENTS

In this paper, we use the OHSUMED dataset [START_REF] Le | A comprehensive filter feature selection for improving document classification[END_REF] for evaluating the performance of our parallel documentbased filter feature selection approach. The dataset used is composed of medical abstracts from 270 medical journals over a five-year period (1987-1991) categorized into 23 cardiovascular diseases categories. The detailed description of this dataset is presented in Table 1. At first, the data set includes 181 categories, this huge number of categories was reduced to 54 categories using the PLP1 method. Categories used are obtained by removing categories represented by less than a threshold which is 10 documents, given that the category represented by the largest number of documents include 723 instances. This steps leads to delete 319 documents from training phase which will certainly affects classification performance since a lot of information will be lost. To achieve better performance further experiments are needed to determine the optimal threshold to use for retrieving categories. As another alternative approach, the PLP2 method is used. This method is an upgrading of the PLP1 method where all documents of the training phase are used, thus no training document will be removed. The results of classification using PLP1 and PLP2 methods are given in Table 2. It can be noted from Table 2 that both PLP1 and PLP2 have a very low accuracy rate 31,43% and 35,52%Respectively. This can be due to the document based feature selection technique which ignores relation between features and labels and also to the imbalanced aspect of data. Effectively this can explains the difference of precision among some categories; for instance C04 and C14 have the highest scores and the highest number of documents in the data set. Fig. 2. Shows that PLP2 method perform better than PLP1 for all measures, this can be due to the fact that this approach uses all training documents. For the future, it will also be interesting to use class-based feature selection techniques to better benefit from the relationships between labels and introducing other measures related to multi-label classification. Using other large data sets for test will also be suitable.

Fig. 1 .

 1 Fig. 1. Flowchart of the proposed approach



  For text representation we use bag-of-words technique.  The PLP1 and PLP2 methods are used separately.  The training bag-of-words vectors are reduced by a parallel TF-IDF.  Then, they are used for building a leaning model using parallel NB classifier All experiments were carried out on a 2.70GHz Intel(R) Core i7-4600U Processor with 4 gigabytes of memory running Ubuntu 16.4. This environment used in a multi nodes Hadoop cluster including 5 nodes.

Fig. 2 .

 2 Fig. 2. Comparison of measures for PLP1 and PLP2 methods V. CONCLUSION In this paper a parallel feature selection technique coupled to a pruned problem transformation method to incorporate the dependencies among the class values is used to improve multi-label classification and reduce space dimensionality. A parallel Naïve Bayes classifier is used to evaluate the performance of the selected features. Parallel algorithms are implemented using different number of nodes in a Hadoop cluster. The results of the experiment show that both feature selection techniques and transformation methods affect considerably the performance of multi-label classification.For the future, it will also be interesting to use class-based feature selection techniques to better benefit from the relationships between labels and introducing other measures related to multi-label classification. Using other large data sets for test will also be suitable.

Table 1

 1 The description of Ohsumed dataset

	Category	Train doc	Test doc
	C01	423	506
	C04	1163	1467
	C06	588	632
	C08	473	600
	C10	621	941
	C12	491	548
	C14	1249	1301
	C20	525	695
	C21	546	717
	C23	1799	777

Table 2

 2

		The performance of our approach using
		the PLP1 and PLP2 methods
	Category Mesaure	PLP1	PLP2
		Precision	27.08% 31.82%
	C01	Recall	28.19% 32.66%
		F1-measure	27.62% 32.23%
		Precision	39.95% 42.47%
	C04	Recall	39.14% 41.42%
		F1-measure	39.54% 41.94%
		Precision	22.63% 27.53%
	C06	Recall	23.37% 28.48%
		F1-measure	22.99% 28.00%
		Precision	29.67% 35.00%
	C08	Recall	30.17% 34.54%
		F1-measure	29.92% 34.77%
		Precision	29.86% 32.73%
	C10	Recall	29.18% 31.92%
		F1-measure	29.52% 32.32%
		Precision	29.01% 33.39%
	C12	Recall	27.99% 32.62%
		F1-measure	28.49% 33.00%
		Precision	45.50% 50.12%
	C14	Recall	45.68% 53.66%
		F1-measure	45.59% 51.83%
		Precision	29.78% 34.10%
	C20	Recall	30.53% 34.35%
		F1-measure	30.15% 34.22%
		Precision	32.78% 36.40%
	C21	Recall	32.32% 34.94%
		F1-measure	32.55% 35.66%
		Precision	28.06% 31.66%
	C23	Recall	28.42% 31.14%
		F1-measure	28.24% 31.40%
	The experimental steps are as following:
	 For preprocessing, stop words are removed. The
	stemming process is executed with Porter
	Stemming algorithm (Porter 1997).


Mohamed Tarbouch
email: mtarbouch@gmail.com

Abdelati Reha
email: 1abdelati.reha@isga.ma

Abdelkebir El Amri
email: 2elamri_abdelkebir@yahoo.fr

Youssef Mejdoub
email: ymejdoub@yahoo.fr

A COMPACT PIFA ANTENNA FOR INTERNET OF THINGS NETWORK LoRaWAN AT 900 MHz BAND

Keywords: CADFEKO, Internet Of Things, LoRaWAN, PIFA Antenna, Miniaturization. I

In this paper, a planar inverted-F antenna (PIFA) for LoRaWan network is studied. The proposed structure covers the 868MHz radio frequency band used by LoRaWan in Europe and the 915 MHz radio frequency band used in North America. The miniaturization in the antenna size is achieved by introducing a meandered microstrip line. The setup of the meandered line allows obtaining lower resonant frequency and large -6dB bandwidth and important gain. The antenna has compact size of 40x26x1.6mm3. The radiation patterns in the operating bandwidth are almost the same as the omnidirectional characteristics. The influences of various design parameters are investigated using CADFEKO, a Method of Moment (MoM) based solver.

INTRODUCTION

LoRa (Long Range) is a patented digital wireless data communication technology developed by Cycleo of Grenoble, France, and acquired by Semtech in 2012. LoRa is a longrange wireless communication protocol that competes against other low-power wide-area network (LPWAN) wireless such as NB-IoT or LTE Cat M1. Compared to those, LoRa achieves its extremely long range connectivity, possible 100km+, by trading off data rate. Because its data rates are below 50kbps and because LoRa is limited by duty cycle and other restrictions, it is suitable in practice for non-real time applications in which one can tolerate delays [1].

Since LoRa defines the lower physical layer, the upper networking layers were lacking. LoRaWAN was developed to define the upper layers of the network. LoRaWAN is a media access control (MAC) layer protocol but acts mainly as a network layer protocol for managing communication between LPWAN gateways and end-node devices as a routing protocol, maintained by the LoRa Alliance. Version 1.0 of the LoRaWAN specification was released in June 2015 [2].

LoRaWAN defines the communication protocol and system architecture for the network, while the LoRa physical layer enables the long-range communication link. LoRaWAN is also responsible for managing the communication frequencies, data rate, and power for all devices [3]. Devices in the network are asynchronous and transmit when they have data available to send. Data transmitted by an end-node device is received by multiple gateways, which forward the data packets to a centralized network server [4]. The network server filters duplicate packets, performs security checks, and manages the network. Data is then forwarded to application servers. The technology shows high reliability for the moderate load, however, it has some performance issues related to sending acknowledgements [START_REF] Bankov | On the Limits of LoRaWAN Channel Access[END_REF].

LoRaWAN uses license-free sub-gigahertz radio frequency bands like 169 MHz, 433 MHz, 868 MHz (Europe) and 915 MHz (North America). LoRaWAN enables long-range transmissions (more than 10 km in rural areas) with low power consumption [START_REF] Sanchez-Iborra | Performance Evaluation of LoRa Considering Scenario Conditions[END_REF].

Because the PIFA antenna operates at a resonant length of ( 4 /  ), it is highly conducive to a small and lightweight design, and thus well-suited for use as an internal antenna for LoRaWAN boards. The PIFA antenna has the advantage of a low profile, but its narrow bandwidth makes it difficult to realize multiband capability with a single resonator. While this problem can be resolved by using additional resonators [START_REF] Liu | A new multiband antenna for WLAN/cellular applications[END_REF], such additions tend to increase the size of the antenna. This means that with a PIFA, it is difficult to simultaneously achieve miniaturization and multiband capability.

The miniaturization can affect radiation characteristics, bandwidth, gain, radiation efficiency and polarization purity. The miniaturization approaches are based on either geometric manipulation (the use of bend forms, meandered lines, PIFA shape, varying distance between feeder and short plate, using fractal geometries [START_REF] Chen | Pifa with a meandered and folded patch for the dual-band mobile phone application[END_REF][START_REF] Reha | Fractal Antennas : A Novel Miniaturization Technique for wireless Networks[END_REF][START_REF] Sun | Miniaturised patch hybrid couplers using asymmetrically loaded cross slots[END_REF][START_REF] Chi | Antenna Miniaturization Using Slow Wave Enhancement Factor from Loaded Transmission Line Models[END_REF]) or material manipulation (Loading with a high-dielectric material, lumped elements, conductors, capacitors, short plate [START_REF] Skrivervik | PCS Antenna Design: The Challenge of Miniaturization[END_REF]), or the combination of two or more techniques [START_REF] Reha | The behavior of a CPW-Fed miscrostrip hexagonal patch antenna with H-Tree Fractal slots[END_REF].

In this paper, we introduce a meandered microstrip line in the PIFA structure to generate a lower frequency. The meandered microstrip line is a powerful miniaturization technique which is used to lengthen the path over which current travels, thus helps reducing the resonant frequency. By combining the technique of shorting used by the PIFA and the technique of folding a microstrip line it is possible to obtain a small antenna .The final antenna has a suitable bandwidth of 102 MHz [848 -950 MHz] and a reasonable gain.

II. DESIGN METHODOLOGY

Fig. 1(a) shows the 3-D geometry of the proposed antenna, which consists of a meandered microstrip radiator printed in the center of the antenna. For efficient radiation, the ground (GND) found on the bottom surface of the substrate was turned by 180 degrees. The system GND for the antenna has a dimension of 75 mm length and 40 mm width and it is connected with the top radiators elements using a shorting strip. The volume under the radiating elements is filled by air except a thin region 1.6 mm who is composed of FR4_epoxy substrate with relative permittivity of 4.4. antenna geometry as seen from above in mm Fig. 2 shows the simulated reflection coefficients for the proposed antenna. As Fig. 2 shows, the proposed antenna has one resonant frequency fr=896 MHz with a reflection coefficient of -44.66 db.

Fig.2. Simulated S11 versus frequency graph of the final antenna

The analysis of the S11 parameter (Fig. 2) shows that, for the band of 800 -1000 MHz, the antenna has -6dB bandwidth of 102MHz (848 -950 MHz).

The choice of Lm parameter:

By varying the parameter Lm from 10.2 mm to 11 mm the S11 parameter of the antenna versus frequency is shown in Fig. 3. From the simulation results, it's clear that the parameter Lm affect the bandwidth and the resonant frequency of the antenna. In addition from the graph we note that as Lm is increased, the resonant frequency is decreased. Table 1 summarizes the bandwidth and resonant frequency obtained by varying the parameter Lm. From table 1 we note that Lm=10.6mm will allows us to have a good antenna in term of bandwidth and also in term of impedance matching. From the previous parametric study of the proposed antenna, we obtained an optimized structure which has a compact size and an interesting reflection coefficient in the operating studied band. Fig. 4 shows the maximum gain of the proposed antenna. The maximum gain is stable and it is equal to 2.1 dBi in the whole band. In the next work, fabrication and measurement should be done to confirm the simulated results. Also, more refinements should be done to cover the 169 MHz and 433 MHz bands.

Fig. 1

 1 Fig. 1(b) shows in details the geometry of the printed radiator as seen from above. The antenna takes up an area occupying 40*26 mm on the upper surface of the substrate. The radiators elements are placed at a height D=1.7 mm from the horizontal plan of the ground plane. The height D is composed of 1.6 mm the thickness of the substrate and a 0.1 mm of the Air. The proposed structure is excited with a microstrip feed line of input impedance (Z0= 50 ohms).

Fig. 3 .

 3 Fig. 3. Simulated S11 versus frequency by varying Lm in mm.

Fig. 4 .

 4 Fig.4. Maximum gain of the proposed antenna

Fig. 5 .

 5 Fig.5.The 2D radiation pattern of the final antenna

Table . 1

 . : Resonant frequency and bandwidth versus the parameter Lm

	Lm(mm)	Resonance	Bandwidth
		frequency(MHz)/S11	(MHz)
		(dB)
	10.2	892/-37.80	98:(846-944)
	10.4	884/-33.21	93:(841-934)
	10.6	896/-44.66	102:(848-950)
	10.8	870/-27.85	87:(830-917)
	11	864/-26.42	84:(825-909)
	III. THE ANTENNA PERFORMANCE


Abdelhadi Eloudrhiri Hassani

Aicha Sahel
email: sahel_ai@yahoo.fr

Abdelmajid Badri
email: abdelmajid_badri@yahoo.fr

Impact of RPL objective functions on energy consumption in Ipv6 based wireless sensor networks

Keywords: WSN, IoT, 6LowPAN, IEEE 802.15.4, RPL, MRHOF, OF0, Cooja, Contiki OS

With the arrival of the internet of things concept, a new vision of wireless sensor networks has been adopted allowing them to be addressed with ipv6 addresses, thus forming 6LowPAN networks. It is categorized as a new technology being developed and improved. This causes issues about the networks performance to create the communication path and collecting data. Therefore, IETF has proposed an IPv6 based routing protocol with low cost and power constraints RPL that builds a Destination Oriented Directed Acyclic Graph (DODAG) based on a set of metrics and constraints via a specific Objective Functions (OFs). This objective function selects the best parents and construct the routes. Our research is focus on performance analysis of two objective functions that are Minimum Rank with Hysteresis Objective Function (MRHOF) and Objective Function Zero (OFO) in a small area under a large scenarios and topologies. This comparison is focused on energy consumption of the network in the given scenrios to distinguish which objective function is the most optimal to guarantee long life expectancy of the sensor networks especially in static environment.

INTRODUCTION

Internet of Things (IoT) is a recent paradigm that enables the communication between things in a ubiquitous way through different technologies. IoT scenarios can cover different application such as urban sensor, industrial monitoring and home automation. In order to allow an IoT application to achieve its objectives, it is necessary that a routing protocol can provide data communication with best performances. The routing protocols should take into consideration an important requirement that is the power consumption that allows a long lifetime of the IoT network. Otherwise, IoT is based on Low-Power Wireless Personal Area Networks (LoWPAN), that are embedded devices with constrained in term of energy, processing and memory. With the arrival of IPV6 era, the Internet Engineering TaskForce (IETF) defined IPv6 addressing on LoWPAN networks (6LowPAN) which allows these devices to be connected to the internet, thus supporting the integration vision of wireless sensor network (WSN) into the internet of things. For that the IETF defined a new routing protocol for low-power and lossy networks RPL (IPv6 routing protocol for low-power and Lossy networks). RPL protocol uses routing algorithms and Destination Oriented Directed Acyclic Graph (DODAG) in order to build a graph-based topology. The DODAG uses an objective function as a path selection mechanism to the root node that collect all data into the WSN. RPL offers two objective functions ; Minimum Rank with Hysteresis Objective Function (MRHOF) uses the minimum Expected Transmission Count (ETX) metric on the path to the route. In the other hand, objective function zero (OF0) is based on the minimum number of hops on the path to the route. Our primary purpose in this paper is to provide a large study of the two objective functions MRHOF and OF0 in two kind of toplogies in order to have an idea of suitability performance of RPL in each scenario. The rest of this paper is organized as follow. In section II we give a overview of the routing protocol RPL, Section III : the methodology, Section IV we present results that we obtain and a discussion of performances in each scenario and finally a conclusion is given in Section V.

IPV6 ROUTING PROTOCOL FOR LOW POWER AND LOSSY NETWORKS (RPL)

Topology construction RPL is the IPv6-based vector distance routing protocol designed for sensor node in network. It's also designed to integrate the routing protocol at the standard of IEEE 802.15.4 with the IPv6-based IP protocol in order to cope with the heterogeneity in the IoT concept [START_REF] Nyoman | Zolertia Z1 Energy Usage Simulation with Cooja Simulator[END_REF]. RPL has to discover links and then select peers in such a way that no cycles are present. To this end, a Directed Acyclic Graph (DAG) built according to one or more Destination Oriented DAGs (DODAGs), one DODAG per root using an Objective Function (OF) to reach specific objectives [START_REF] Khallef | Multiple Constrained QoS Routing with RPL[END_REF]. RPL use ICMPv6 control messages to form and manage the nodes that constitute the WSN. Tree messages are present : DODAG Information Object (DIO), DODAG Information Solicitation (DIS) and the DODAG Destination Advertisement Object (DAO). In order to build a DODAG, the root sends periodically DIO to its neighbors containing several parameters such as rank, metric, routing cost and DODAGID. Then neighbours nodes decide to join DODAG accor-ding to the OF. The algorithm of rank calculation is illustrated in fig1.

fig 1 : Process of calculation rank node

The process of building DODAG continue with nodes that decide to join the DODAG. Once this process is completed, each node has a routing table to its parent, hop-by-hop take to the root node. Also , the node may sends a DIS used to request information from its neighbors when waiting for DIO message. In order to maintain the network more stable, RPL uses the Trickle algorithm, to periodically refresh the DODAG. The DAO are transmitted according to two mode of operation (MOP); storing mode and non-storing mode. In the first one, once the neighbors nodes recept the DAO from the sensors, RPL maintain a routing table containing all reachable destinations. In second mode, the DAO messages are directly unicast to the root [START_REF] Ibtissem Zaatouri | Performance Evaluation of RPL Objective Functions for multi-Sink[END_REF]. The structure of RPL and its creation has been illustrated in fig. 2. The Objective Function is used to define one or more metrics to help nodes to translate these metrics into ranks. It is responsible for selecting the routes in a DODAG. Rank computation is fulfill using the OF depending on routing metrics as a reference like link quality and delay, and the other side to express the distance between a node in the network and the DO-DAG root [START_REF] Shimaa | RPL Routing Protocol Performance in Smart Grid Applications Based Wireless Sensors: Experimental and Simulated Analysis[END_REF]. RPL can separate the OFs from the core of the protocol which allows it to meet the different optmization criteria required. Each DODAG instance in RPL's DAG is associated with a particular OF. Otherwise, two objective functions are defined in RPL : Objective Function Zero (OF0) and Minimum Rank Hysteresis Objective Function (MRHOF). MRHOF is an objective function that use minimum value of Expected Transmission Count (ETX) on parent node selection. ETX is an expected number of transmission that required for it to be received without error at its destination [START_REF] Lamaazi | Performance Assessment of the Routing Protocol for Low Power and Lossy Networks[END_REF]. OF0 is an objective function that use minimum value of hop to reach root node. Each node will calculate rank based on the hop value to the root node. The fewer number of jumps will get higher priority link to be selected by OF0. Rank value on child node is always higher than the parent node.

METHODOLOGY

The performance analysis of the objective functions implemented in RPL has been evaluated under Cooja [START_REF] Sharma | Quantitative Analysis and Evaluation of RPL with Various Objective Functions for 6LoWPAN[END_REF]. It's a cross approach that allows to emulat the hardware and software levels of a mote or node based on Contiki OS [START_REF] Qasem | Performance Evaluation of RPL Objective Functions[END_REF] [START_REF] Zikria | A survey on routing protocols supported by the Contiki Internet of things operating system[END_REF]. It's a lightweight, highly portable open source operating system, it is dedicated for WSNs applications. It's known as being the king of operating systems for environment monitoring applications. Otherwise, four algorithms are used in our simulation : udp-sink.c, udp-send.c, rpl-config.h, of0.c and mrhof.c, distribued as open source codes, that we modified according to the aims of our study. Collect View allowed us in the other hand to collect the parameters that we want to exploit.

Network setup

In this study we have simulated two topologies with a single sink node: Linear and Random. The nodes are distributed in a squared area with a side L= 100 meters, and the sink placed 30 meters near the area monitored by senders motes. We have designed our RPL network with the two objective functions MRHOF and OF0, and two densities (10 and 37 nodes) including the sink mote. Likewise, we varied the RX value (50, 75, and 100%) and study the performance of the routing protocol in term of power consumption. The default parameters used in our study are represented in Tab. The deployement of wireless sensors in streets of any city will be close to linear and random topologies as shown in Fig. 1 and Fig. 2. We have two types of nodes. The node with the green color represents the sink mote, and it's placed 30m near of all the other nodes. On the other hand, the nodes with the yellow color represent the senders motes, placed in a area with side 100m. According to Fig. 5678, we can conclude that both topologies, OF0 perform better than MRHOF-ETX in term of power consumption and this comes back to the fact that the MRHOF-ETX prefer long paths with more stable link quatilies than short paths with poor link qualities which leads more nodes in the routing of a packet and therefore higher power consumption for transmission radio. In addition to that, the calculation algorithm of the metric ETX for MRHOF is more complex than OF0 which is based on the rank of the node. Also, OF0 is based on minimizing the number of hops to the root what leads to less retransmition of packets hop by hop and less usage of radio. Otherwise, we can see from Fig. 9-16 that the average power consumption is widely spent in listenning for both objective functions that can be explained by the number of packets that circulate in the network, so each node consumes more energy in listening to the channel in order to recept or transmit a packet to avoid the collisions . In our work, we evaluated the two objectives functions MRHOF-ETX and OF0 of RPL in two topologies under different parameters and constraints which allowed us to see that OF0 perform better than MRHOF-ETX in term of power consumption. Finally, we can conclude that for applications which monitor a small area, we must choose OF0 as an objective function for RPL. So as a future work, we prospect to design a new objective function to minimize more the power consump-tion of the network thus extend the lifetime of the network.

fig 2 :

 2 fig 2 : Process of DODAG construction with RPL

1 :

 1 Parameters of simulationMetrics evaluatedIn this paper, we are interested to evaluate the RPL performance in term of power consumption. The power consumption indicates the energy measured from nodes in the network over the network lifetime[START_REF] Shimaa | RPL Routing Protocol Performance in Smart Grid Applications Based Wireless Sensors: Experimental and Simulated Analysis[END_REF]. It's the sum of power used in each node on the network. There are four types in power measurement : Low Power Mode (LPM), CPU power, radio listen, and radio transmit. Power consumption is a total of all calculation off all type above. LPM is a power consumption parameter that indicates the power used when in sleep condition. CPU power is a power parameter that indicates the level of node processing. While radio listen and transmit is parameter related with node communication (transmit and receive).The formula used to calculate the energy of nodes according to the following equation. Energy (mJ) = (Transmit * 19.5 mA + Listen * 21.5 mA + CPU time * 1.8 mA + LPM * 0.0545 mA) * 3 V / (32768) Simulation network topologies :

Fig. 3 :

 3 Fig.3 : Linear topology with 37 nodes

Fig. 5 :Fig. 6 :Fig. 7 :Fig. 8 :Fig. 9 :Fig. 10 :Fig. 11 :

 567891011 Fig.5 : Average PC vs RX in linear topology for 10 nodes

Acknowledgment

This work was supported by the TIC center of university Hassan II Casablanca as a part of the "Big Data& Connected Objects" research project.


Mohammed Razzok

Abdelmajid Badri

Yassine Ruichek

Aicha Sahel

Street crossing pedestrian detection system: A comparative study of descriptor & classification methods

Keywords: Pedestrian detection, Computer vision, Trajectory estimation, Risk evaluation, Driver alert, Image descriptors, Classification methods

in recent years, the number of people killed on roads has increased enormously, several pedestrian detection techniques in monocular images have been proposed to address this problem. We present our pedestrian protection system from moving vehicles using video cameras installed on the vehicle, this system combines pedestrian detection, trajectory estimation, risk evaluation, and driver alert. First, we focus on the pedestrian recognition task. Different combinations of image descriptors and classification methods have been evaluated on this task. Experiments are performed on a dataset captured on-board a vehicle driving through urban environments. Results show that the best model is HOG&RbfSVM.

I. INTRODUCTION

With the increasing popularity of automobiles over the last century, road accidents have become a major cause of death. World Health Organization [1] announces that there are more than 270,000 pedestrians lose their lives on the world's roads each year accounting for 22% of the 1.24 million road traffic deaths. Development of the pedestrian protection system is very active in order to increase the safety of the vehicle and its environment. At present, the main objective is to provide drivers certain information about their environment and any potential danger. One of all useful information is the detection and localization of a pedestrian in front of a vehicle.

The pedestrian application on-board vehicle is particularly difficult for a number of reasons. Variations of background, illumination, clothing, body pose, and size. This paper is organized as follows. In Section II and III, we briefly review some image feature extractors and classifiers, the mono Daimler Pedestrian Classification Dataset used in this paper and experiment results are presented in Section IV, Discussion results are explained in Section V. Finally, Section VI presents conclusions.

II. FEATURE EXTRACTORS

A. Histogram of Oriented Gradients descriptor

In the context of object recognition, the use of edge orientation histogram has gain popularity [START_REF] Shashua | Pedestrian detection for driving assistance systems: Single-frame classification and system level performance[END_REF], [START_REF] Dalal | Histograms of Oriented Gradients for Human Detection[END_REF].Histogram of oriented gradients (HOG) is a method introduced by Dalal et al [START_REF] Dalal | Histograms of Oriented Gradients for Human Detection[END_REF]. The goal of such a method is to describe an image by a set of local histograms by dividing the image into small cells. These histograms count occurrences of gradient orientation in each cell. Within each overlapping block, a feature vector is extracted by sampling histograms from the contributing spatial cells. The feature vectors for all blocks are concatenated to produce a final feature vector, See Fig. 1.

B. LBP descriptor

Given a pixel in the image, an LBP [START_REF] Ojala | Acomparative study of texture measures with classification based on feature distributions[END_REF] code is computed by comparing it with its neighbors:

LBP P,R = P -1 p=0 s(g p -g c ) • 2 p , s(x) = 1 x ≥ 0 0 x < 0 (1)
Where g c is the gray value of the central pixel,g p is the value of its neighbors,P is the total number of involved neighbors and R is the radius of the neighborhood. The histogram of binary patterns is used for extraction the feature vector,Fig. 2 shows the extraction process of the LBP feature for a pixel's circular neighborhoods with R = 1 and P = 8.

The most important properties of LBP are its tolerance against illumination changes and its computational simplicity. Given a training set of N data points {x k , y k } N k=1 ,where x k ∈ R n is the k th input pattern and y k ∈ {-1, 1} is the k th output pattern, the support vector method approach aims at constructing a classifier of the form:

y(x) = sign[ N k=1 α k y k ψ(x, x k ) + b] (2)
Where α k are positive real constants and b is a real constant. For ψ(., .) one typically have the following choices:

• ψ(x, x k ) = x T k x (Linear SVM) • ψ(x, x k ) = (x T k x + 1) d (Polynomial SVM of degree d) • ψ(x, x k ) = exp(-γ x k -x 2 2 ) (Rbf SVM) where γ > 0 • ψ(x, x k ) = tanh[κx T k x + θ]
where κ, θ are constants

B. Decision tree

A decision tree(DT) [START_REF] Bernard | Decision trees and diagrams[END_REF], [START_REF] Safavian | A survey of decision tree classifier methodology[END_REF] is a flowchart-like tree structure where each internal node represents feature (or attribute), each branch represents a decision rule, and each leaf node represents a class label see Fig. 3 The process of making a decision tree classifier is as follows:

• Choose the best attribute using Attribute Selection Measures (ASM). • Make that attribute a decision node and split the dataset into smaller subsets. • Starts tree building by repeating this process recursively for each child until we find leaf nodes in all the branches of the tree. (3)

Gini(S) = 1 - m j=1 P 2 j ( 4
)
Where P j is the relative frequency of class j in S. Based on the entropy or the gini index, we can compute the information gain if attribute A is used to partition the dataset S :

Gain(S, A) = Entropy(S) - v∈A |S v | |S| Entropy(S v ) (5) Gain(S, A) = Gini(S) - v∈A |S v | |S| Gini(S v ) (6)
Where v represents any possible values of attribute A,S v is the subset of S for which attribute A has value v,|S v | is the number of elements in S v ,|S|is the number of elements in S.

C. Random forest

The random forest(RF) classifier (often also referred to as Randomized Trees) were introduced in the machine learning community by [START_REF] Amit | Shape quantization and recognition with randomized trees[END_REF], [START_REF] Breiman | Random forests[END_REF] consists of a combination of tree classifiers where each classifier is generated using a random vector sampled independently from the input vector, and each tree casts a unit vote for the most popular class to classify an input vector.

The operating principle of RF is summarized as follows, step-by-step, and illustrated in Fig. 4. • Each DT casts a unit vote for the most popular class, and then optimal results are determined. Compared with other classification approaches, several advantages have made the RF an attractive tool for classification: it does not overfit when the number of decision trees increases [START_REF] Breiman | Random forests[END_REF], it contains a built-in cross-validation method, and doesn't need to separate testing dataset for evaluating the performance because the out-of-bag samples act in the same way as the testing dataset and give realistic prediction error estimates.

D. AdaBoost

The AdaBoost classifier was originally proposed by Freund and Schapire [START_REF] Freund | Experiments with a new boosting algorithm[END_REF], [START_REF] Freund | A decision-theoretic generalization of online learning and an application to boosting[END_REF]. The AdaBoost classifier combines multiple weak classifiers to form a strong classifier.

Suppose the input dataset is expressed by:

S = {(x 1 , y 1 ), ..., (x N , y N )}, y ∈ {-1, +1} (7)
Where x i represents the i th vector, and y i the label of a target class. AdaBoost calls a given weak or base learning algorithm repeatedly in a series of time intervals t = 1, .., T .Each training sample is denoted with an equally initialized weight:

D 1 (i) = 1 N (8)
Where N is the number of samples. The training set was trained for T rounds. The target of training is to find an optimal classifier h t and optimize it to be a strong classifier. It can be realized by increasing or decreasing the weights of classified examples after each training round to focus on the hard samples in the training set. The rule for updating the weight is given by:

D t+1 (i) = D t (i)e -αtyiht(xi) Z t = e -t j=1 αj yihj (xi) N t j=1 Z j (9) D t+1 (i) = e -yiFt(xi) N t j=1 Z j (10)
Where Z t is a normalization factor, h t is the base classifier, α t is a parameter that iteratively minimizes the potential of h t ,and F t (x i ) is the constructed classifier decision.

Z t = N i=1 D t (i)e -αtyiht(xi) (11)
Where D t (i) is the weight of distribution on training example i at round t [START_REF] Schapire | Improved Boosting Algorithms Using Confidence-rated Predictions[END_REF].Thus, the final classifier H can be obtained by combining many base classifiers thru the weighted majority vote.

H(x) = sign{ T t=1 α t h t (x)} (12)
The skeleton of AdaBoost can be outlined as follows:

• Establish (x 1 , y 1 ), ..., (x N , y N ), y ∈ {-1, +1}

• Initialize and normalize the weight • Repeatedly execute the following steps from t = 1, ..., T • Train the training set with the distribution D t • Get the base classifier h t which results in the least error • Update the weight focused on incorrect sample and set the new weight, eventually generating the final strong classifier H. Choosing a proper weak classifier is important in performing AdaBoost algorithm. The following two criterions were followed to generate a good classifier: (1) the weak classifier should have the capacity of coping with reweighing of the data, and (2) the weak classifier should not result in over-fitting. the decision tree method was selected as the weak learning machine.

IV. RESULTS

For our experiments, we use the Daimler mono pedestrian dataset used in [START_REF] Munder | An experimental study on pedestrian classification[END_REF] to evaluate different combinations of image descriptors and classification methods, the classification dataset consisting of five folder, three folder for training and two folder for testing(T1,T2), each folder contains a total of 4800 pedestrian and 5000 non-pedestrian samples cut out from video images and scaled to common size of 18x36 pixels. In the model construction phase we use the K-Fold Cross Validation method [START_REF] Kohavi | A study of cross-validation and bootstrap for accuracy estimation and model selection[END_REF] (in our case K=3), using the cross validation help us to compare the performance of different machine learning models, selecting suitable parameters. Results are obtained using a computer Intel Xeon X3430 quard core 2,4GHz CPU and 8Go RAM.

In our work we use python as a programming language and scikit-learn library for image processing,

Fig. 1 .

 1 Fig. 1. Extraction of HOG feature

Fig. 2 .

 2 Fig. 2. Extraction of LBP feature

Fig. 3 .

 3 Fig. 3. Decision tree classifier

•

  Step 1: k subsets of the training sample D 1 , D 2 ,..., D k , are drawn from the total training sample set D using the bootstrap sampling method. The sample size of subsets D k is the same as the total training sample set D. • Step 2: k DTs are constructed according to the k subsets and k classification results obtained. If there are M input variables, a number m M is specified at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m remains constant during the forest's growth. The size m of feature selected considered at each split is typically equal to √ M or [log 2 (M ) + 1].

Fig. 4 .

 4 Fig. 4. Random forest classification principle

  Table. I shows parameters of image descriptor, Table. II and Table. III show

average validation rate, prediction and construction time of different models.

V. DISCUSSION

Experiments reveal a number of interesting points:

• HOG feature extracts a lot of information from pedestrian images than LBP basic. • Best Model for pedestrian detection in this study is HOG+RbfSVM. • AdaBoost classifier takes more time for construction.

• Random forest classifier accuracy is good as AdaBoost and sometimes better.

VI. CONCLUSION

We have presented our comparative study on pedestrian recognition methods. The main goal is to find the best model that gives the biggest detection rate, results are very promising, but there are still some perspectives for our future search. Firstly, we want to use other techniques to increase the detection rate, ensure real-time pedestrian detection and detecting pedestrian at night using infrared camera.