Aya Sakhri
email: aya.sakhri@univ-lorraine.fr

Moufida Maimour

Eric Rondeau

Noureddine Doghmane

Saliha Harize

An Energy-Efficient WMSN-based System for Endangered Birds Monitoring

Keywords: Birds Monitoring, WMSN, Audio Denoising, Audio Classification, Energy Efficiency

Recently, more attention is being payed to the ecological environment because of the rapid decline of animal species especially birds. Wireless Multimedia Sensor Networks (WMSN) can be leveraged to monitor, protect and assess changes in birds populations by capturing and sending images in real time. To deal with their limited bandwidth and energy resources, we propose to reduce the amount of visual data to report by sending only images of interest to the collect station. This can be achieved by identifying the endangered target species locally based on their calls before triggering the camera. However, one prominent obstacle is that in wilderness environment, it is difficult to obtain good quality audio signals. This is due to interfering environmental noises that can mask vocalizations of interest so the audio recognition is likely to fail. Therefore, we set up an automated birdsong recognition along with an appropriate noise reduction-based method in order to improve the audio classification accuracy. This ultimately leads to a better use of the network energy and avoids its waste in transmitting useless images.

INTRODUCTION

Birds are good bio-indicators of ecosystem biodiversity since their decline can cause serious problems to the environmental health and safety. The State of the World's Birds [START_REF] Allinson | State of the world's birds[END_REF] revealed that we lost more than 161 species, which represents an extinction rate much higher than natural. This is because of several important reasons including human activities and climate changes. Hence, the survival of our environmental system from various dangers is a primary responsibility which can be achieved through endangered birds monitoring in their natural habitats. Most of the time, the monitoring is done manually in a limited space and time. Moreover, the physical presence of observers in the field may disturb the birds behavior. This is why, we propose to use wireless multimedia sensor network (WMSN) [START_REF] Almalkawi | Wireless multimedia sensor networks: current trends and future directions[END_REF] to build an automatic system for bird surveillance in their natural habitat. The ultimate goal is to identify and estimate the population of endangered birds species based on their transmitted photographs.

Nevertheless, WMSN are characterized by their limited resources in terms of energy, memory and bandwidth. Besides, the sensor network contains hundreds of nodes deployed in hostile environment, which makes the replacement of batteries a hard task. Therefore, improving the lifetime of the sensor nodes to ensure efficient network utilization is one crucial challenge to deal with. In fact, image processing and transmission are the major energy This work was supported in part by the PHC TASSILI 21MDU323.

consuming stages in a WMSN-based surveillance system. It is therefore essential to reduce the amount of the visual data to capture and transmit. To address this issue, multiple low-cost visual data compression algorithms have been proposed [START_REF] Zaineldin | Image compression algorithms in wireless multimedia sensor networks: A survey[END_REF]. Also, [START_REF] Civelek | Automated moving object classification in wireless multimedia sensor networks[END_REF] considered the extraction of relevant image features, others limited the image capture using motion detection as [START_REF] Magno | Multimodal video analysis on selfpowered resource-limited wireless smart camera[END_REF]. However, all these techniques are not sufficient since camera nodes continue to capture and process images including irrelevant ones which is just a waste of energy and resources.

In this paper, we propose to capture only relevant images of target species. This can be achieved by designing a low-complexity system based on local audio detection and recognition of target species in real time prior to triggering image capture. This is motivated by the fact that visual data processing is more expensive when compared to audio data that is of significantly lower size. This local audio identification allows to avoid the continuous processing and transmission of unnecessary images that can paralyze the entire sensor network. In addition, it provides further consolidation of the recognition accuracy. There are very few works that combine audio-visual data processing in a sensor node to decrease the energy consumption as proposed by [START_REF] Koyuncu | Visual and auditory data fusion for energyefficient and improved object recognition in wireless multimedia sensor networks[END_REF]. However, their model does not take into account the overlapping of various environmental noises, which is a major constraint in our application. It degrades the quality of the target sound [START_REF] Xie | Bioacoustic signal denoising: a review[END_REF] which results in false identification of species.

Hence, to ensure an accurate and robust sound recognition, a noise reduction method is required.

Various methods have been developed to solve the problem of interfering noises. [START_REF] Brown | Automatic and efficient denoising of bioacoustics recordings using MMSE STSA[END_REF] recommended the Minimum Mean Square Error Short-time Spectral Amplitude Estimator (MMSE-STSA) for birds recordings. More recently, Deep learning-based noise reduction showing remarkable success by [START_REF] Fang | A generalized denoising method with an optimized loss function for automated bird sound recognition[END_REF]. Nevertheless, this demands an immense dataset for training and is too complicated to be applied in real-time processing. Because of its low computational complexity, others like [START_REF] Weerasena | Continuous automatic bioacoustics monitoring of bird calls with local processing on node level[END_REF] implemented a spectral subtraction in a sensor node inspired by [START_REF] Boll | Suppression of acoustic noise in speech using spectral subtraction[END_REF]. Nevertheless, it suffers from musical noise. In a constrained environment such as sensor networks, the complexity of noise reduction method must be considered to select a suitable robust and simple denoising method. In view of all that has been mentioned so far, one may suppose that not all types of denoising algorithms are relevant for our case as most of them do not consider energy efficiency. So far, and to the best of our knowledge, this is the first work that study an improved method of spectral subtraction, the Geometric Approach GA proposed by [START_REF] Lu | A geometric approach to spectral subtraction[END_REF], for birds call denoising. Also, very few works have attempted to integrate audio data in a sensor network for energyefficient purposes.

Through this paper, we propose an accurate and energyefficient system to monitor endangered birds in complex environment. We focus on the impact of the chosen audio-denoising technique on the classification performance. Also, we examine the energy consumption of our proposed overall system for reducing the processing and transmission of visual data in WMSN. The remainder of the paper is organized as follows. Section 2 describes our proposed monitoring system. The performance evaluation of the overall system is given in Section 3. Finally, the conclusion and perspectives are outlined in Section 4.

THE PROPOSED SYSTEM

Our proposed system, is devoted to minimize the energy consumption per node for visual data processing and transmission to prolong the network lifetime (Fig. 1). This can be provided by identifying the captured audio data in a sensor node before triggering the camera. The audio data is first processed, denoised and then classified as target species or not. If a target species is identified (including false positive ones), an image is captured, otherwise the camera stays on standby. Prior to its transmission to the end user, the image is compressed using a low cost encoding technique.

The proposed real-time audio recognition phase comprises three main steps. The first step consists in denoising the sensed audio signal to eliminate environment noises. To do so, we made use of a new geometric approach (GA) to spectral subtraction proposed by [START_REF] Lu | A geometric approach to spectral subtraction[END_REF] that improves the traditional spectral subtraction used by [START_REF] Weiss | Study and development of the intel technique for improving speech intelligibility[END_REF]. Despite the fact that the latter is less complex, the former solves known shortcomings of the former like the musical noise and signal distortion. Finally, the extracted features are compared to reference ones using Dynamic Time warping (DTW ) distance for classification purposes using a threshold based matching. DTW is one of the known low-complexity algorithms for measuring the similarity between two time sequences. It aligns two feature vector sequences by repeatedly warping the time axis to find the optimal match between the two vectors [START_REF] Müller | Dynamic time warping[END_REF]. If the processed sound is identified as a target, the camera will be activated and an image of the target is captured to be transmitted to the end user.

The decision of capturing images is based on the results of audio classification. Visual data is compressed at the sensor node before being sent to the end-user. We applied a low complexity image compression using the open source tool Sensevid [START_REF] Maimour | SenseVid: A traffic trace based tool for QoE video transmission assessment dedicated to wireless video sensor networks[END_REF].

PERFORMANCE ANALYSIS OF THE PROPOSED SYSTEM

In this section, we examine the proposed overall system. Firstly, we study the performance in terms of precision to highlight the impact of the the chosen pre-processing step (Denoising) on the classification accuracy. Secondly, we appraise the usage of energy by the overall system. We assume that the monitoring is performed periodically N times during a day and compare three diverse scenarios (Figure 2) :

• S0: an image is captured and transmitted periodically with no local audio identification ; • S1: an image is only captured and transmitted when a local audio identification detects a bird of interest excluding the denoising phase ; • S2 (our proposed system) that operates as in scenario S1 but with a denoising phase prior to the audio identification.

Comparative Study of Denoising Methods

We first carried out a performance comparison of GA against that of MMSE-STSA [START_REF] Brown | Automatic and efficient denoising of bioacoustics recordings using MMSE STSA[END_REF] in terms of time execution and noise removal efficiency. We are interested in studying three different species of wetland birds threatened of extinction. Table 1 summarises their types and characteristics. Since we cannot find bird's noisy songs dataset that covers diverse cases of noises interference encountered in nature, we created a dataset that contains about 500 noisy calls of the target specimens.

The calls were manually polluted with different types of noises presented in wetlands at different intensities. Noises were chosen based on our multiple visits to the natural habitat during all seasons of the year. The dataset was created by the help of AUDACITY 1 tool.

We considered three categories of target birds, where each category contains 12 noisy sounds for testing. The calls were corrupted by three types of noise (wind, rain with thunder, and lake atmosphere). Four values of signal to noise ratio (SNR) were considered (-5dB, 0dB, +5dB and 10dB). The average duration of each recording is 2 seconds.

The denoising is a pre-processing step in the overall automatic sound-recognition system. Therefore, it should not take too long for being processed. Otherwise, the majority of the sensor energy will be wasted. We found that GA is about 9 times faster than MMSE-STSA for the three different categories of birds. Therefore, GA appears better suited for our needs. Despite that, we also evaluated it in terms of signal quality performance to approve our choice.

Since collecting bioacoustic data has increased, new efficient evaluation metric was created and has become an important metric for building real-time processing systems, so-called composite measures [START_REF] Hu | Evaluation of objective measures for speech enhancement[END_REF]. These measures were specially intended to evaluate audio enhancement techniques by predicting the quality of the enhanced noisy signal. They are derived by linearly combining existing objective measures involving subjective evaluations. In this work, we present our results based on the Background Noise Distortion (BAK) measure having five-point scale which varies from 1 (very conspicuous) to 5 (not noticeable). Figure 3 shows that GA outperforms MMSE-STSA for the three categories for removing noise.

The present study confirmed that using a low complexity method is worthy since it has a very acceptable performance.

1 https://www.audacityteam.org/ Fig. 3. BAK-based denoising evaluation

Impact of Denoising on the Classification Accuracy

Here, the principal focus is to investigate the impact of the denoising step on the classification performance. In this part, we selected from our constructed dataset, 117 noisy target calls corrupted by the three different environmental noises at three different SNR measurements : 0 dB , 5 dB and 10 dB. In order to assess the behavior of the system when faced with non-target sounds, we added another 50 recordings. Among them, 30 are retrieved from Xeno-Canto website2 and belong to 11 non-target bird species that live in the same area as our three target species. The remaining 20 recordings concern other various sounds (insects, water, other animals . . . etc.) that can be encountered near lakes, the natural habitat of our target birds. We, finally, used 40 clean target audio samples for reference. In total, 167 recordings were tested.

To attest the performance of our classification, the following metrics were considered : P recision = T P/(T P + F P ) Recall = T P/(T P + F N ) F measure = 2 × P recision × Recall P recision + Recall Accuracy = T P + T N T P + T N + F P + F N where T P , T N , F P and F N represent true positive, true negative, false positive, and false negative respectively. Precision is the proportion of relevant audio data among the retrieved sounds in the dataset. Recall (or sensitivity) indicates the ratio of the number of correctly identified birds to the total number of relevant calls in the dataset. F measure balances between precision and sensitivity. Accuracy denotes the proportion of the total number of predictions that were correct in the dataset. The matrix confusion is represented in Table 2.

Figure 4 provides the classification evaluation of the audio chain system in S1 and S2 respectively. We can clearly notice that the results of S2 are statistically significant for all metrics compared to that of S1. The low performance of S1 for Recall, F-measure and Accuracy is attributable to the distortion of the audio call. This last was corrupted by environmental noises, thus, the system was not able to identify the majority of the target bird calls. However, and S2 were able to identify the non target calls with equal performance. The only difference is that in S2, we recognized birds call much better. In summary, according to all performance metrics, the chain audio in S2 achieved better performance than in S1, confirming the importance role of the denoising phase in our proposal.

Energy Consumption

The main focus of this section is to investigate and estimate the energy consumption per day from end-toend for our different scenarios that includes the amount of energy needed to process an audio recording E audio and the energy required to capture, encode and transmit one image E img . The energy can be computed for a given processor using : E = processor power × cycles/clock rate (1) where cycles denotes the number of cycles required to perform a given processing. The total power of the audio identification system can be computed using :

P audio = P op × op i N i op (2)
where N i op is the number of operations op (multiplication, division, addition, subtraction) required to perform a given phase i ∈ {GA, M F CC, DT W } and P op is the power consumption of operation op. Table 3 gives for one audioframe, the number of operations required to perform the different stages of the audio recognition model. With respect to our three scenarios, the total required energy consumed by the network is given by :

In what follows, we consider a sensor node equipped by an ARM Cortex M3 micro-controller cor (2018) and an ATMEL radio interface. Its main features are summarized in Table 4. Table 5 shows the energy consumption for each step of the overall system including image energy demand derived using SenseVid for an image of resolution 640 × 360. The estimated maximum and minimum energy for the audio chain are given for a 2-second audio call and is related to the maximum and minimum number of cycles for multiplication and division operations. It can be derived that the total energy required for visual data is much higher than for audio data. All the other used parameters in our study are provided in Table 6.

Figure 5 plots the energy consumed in the different scenarios as a function of the probability p that a target bird occurs in one period during the day. It is assumed that the sensor node is able to reach the collect station in one hop. For S1 and S2, we have estimated the maximum and minimum of energy according to the energy demand of audio chain system. It is clear that S0 consumes more energy than the other two scenarios. Its energy consumption remains stable since it does not depend on TP and FP values. We send all the captured images, including the ones of interest and non of interest. Also the higher consumption of S0 refers to the total energy consumed by each image. The low energy consumption of S1 compared to S2 is explained by the low number of TP. The system was not able to identify well the target birds, as a consequence, fewer images were processed. Note that this counts for all transmitted images (after compression) including target and non target ones. For both scenarios 1 and 2, the number of transmitted images is the sum of TP and FP. The highest values of ratio belong to S1, as we transmit less images and we loose significant amount of important data. In contrast to S2 where we send a satisfactory amount of data of interest even though we loose some. For S0, it has the lowest ratio since we send the total of captured images without selection.

Figure 7 plots the useful energy per byte given as follows :

E U =    E N × imageSize
for S0 E T P × imageSize for S1 and S2

(5)

For both scenarios 1 and 2, the number of transmitted images of interest is the number of TP. It is clear that S2 has the minimum ratio, because the majority of transmitted visual data are of interest, which is not the case for S0 and S1.

Figure 8 illustrates the wasted energy estimated by the following equation : We note that S0 wastes about 1.3 J when the probability of occurrence is zero, which is about 6 times higher than S1 and S2. Then it decreases with the probability of the occurrence of an event of interest. The plots also show that S2 wastes less energy than S1, which is due to the low FN values compared to those in S1.

Figure 9 demonstrates a comparison of useful energy consumption in the three scenarios when the number of hops toward the collect station is raised to 2 and 7. We averaged the maximum and minimum values and set the Fig. 9. Useful energy for 1, 2 and 7 hops probability of occurrence of target birds to 0.2. We can see that the energy consumption for 7 hops is higher than for 2 hops, and that the latter consumes more energy than 1 hop. This is attributed to the required energy for transmission and reception of images in each node. S2 seems to be the preferable compared to the other scenario for the three number of hops in the network.

As a conclusion, the findings attest that S2 or our proposed system is the most suitable scenario. It does not waste excessive energy as in S0, which consequently prolongs the energy gain of the node's lifetime and improves the network performance. Furthermore, it does not loose important informative data as in S1, which is the best compromise between preserving energy and interest data at the same time.

CONCLUSION AND PERSPECTIVES

This paper presents a proposed method to extend the lifetime of WMSN for our threatened bird's surveillance application. The idea is to reduce unnecessary visual data processing and transmission through low-energy local identification based on audio bird calls. The detection of the target species in the area will trigger the camera connected to the sensor node. However, in a complex acoustic environment, a denoising method is required for robust and improved automatic bird call detection.

In this work, we have focused on evaluating the performance of the proposed system in terms of accuracy and mainly energy consumption. The experimental results show that the proposed method performs well in identifying bird calls thanks to the denoising phase. Furthermore, our test results attest that the proposed system is able to achieve a per-node energy gain and therefore extend its lifetime.

In future work, we will investigate the impact of an adaptive threshold on the accuracy of audio bird classification. Furthermore, we will optimize the audio chain, in particular the denoising phase, with the goal of minimizing the energy consumption per-node.

Fig. 1 .

 1 Fig. 1. WMSN for birds surveillance Then, to only consider meaningful information of the audio signal, distinctive features are extracted using Mel Frequency Cepstral Coefficients (MFCC ), key element of audio recognition. Their role is to identify the relevant components of the audio signal by eliminating irrelevant information. They are characterized by their computational efficiency and high performance under clean conditions Gupta et al. (2013).

Fig

  Fig. 2. Scenarios

Fig. 4 .

 4 Fig. 4. Classification performanceit was able to identify the non-target calls much better due to the threshold used for classification. This is what justify the high value of Precision of S1. In fact, both S1 and S2 were able to identify the non target calls with equal performance. The only difference is that in S2, we recognized birds call much better. In summary, according to all performance metrics, the chain audio in S2 achieved better performance than in S1, confirming the importance role of the denoising phase in our proposal.

Fig. 5 .

 5 Fig. 5. Overall consumed energy Figure 6 represents the energy consumed per byte estimated by dividing the overall energy by the amount of visual data transmitted :

Fig

  Fig. 6. Energy per byte

Table 1 .

 1 Target Birds Categories and Noises

	Category Target species	Scientific name	Noise type
	Cat-1	Ferruginous Duck	Aythya nyroca	Wind
	Cat-2	Common Pochard	Aythya ferina	Rain with thunder
	Cat-3	White Headed Duck Oxyura leucocephala Lake Atmosphere

Table 2 .

 2 Matrix confusion

		TP FP	FN	TN
	S1	13	2	104	48
	S2	93	2	24	48

Table 3 .

 3 Computational cost for one audioframe

	Step	Mult.	Div.	Add.	Sub.
	Denoising (GA)	14353 7176	3601	7176
	Feature extraction (MFCC)	2485	1636	1073	2218
	Classification (DTW)	-	-	11	3

Table 4 .

 4 Cortex M3 parameters

	Sensor Processor	Cortex M3
	Clock rate	72 MHz;
	Processor power	23 mW;
	Cycles count	Add.[1], Sub.[1], Mult.[1 or 2],
		Div.[1 to 12].

Table 5 .

 5 Energy consumption of the different phases.

	Step		Energy consumption (mJ)
			Min.	Max.
	Denoising (GA)	0.59	2.29
	MFCCs extraction	0.29	1.13
	DTW classification	0.10	0.10
	Image capture	9.540	9.540
	Image encoding	8.058	8.058
	E =	N E img		for S0

(T P + F P )E img + N E audio for S1 and S2

Table 6 .

 6 Experimental Setup

	Sound duration	2 seconds
	Frame samples	444
	Sampling frequency	22050 Hz
	Coding	16 bits
	Image resolution	640x360
	Size compressed image	4863 Bytes
	Packet maximum payload	128 Bytes
	Number of packets	50
	DCT	Triangular BIN DCT; ρ=8
	Entropy coder	Exponential-Golomb (EG)
	Sensor Data Rate	250 kBytes/sec
	Number of hops	1, 2 and 7
	Reception sensitivity	-101 dBm
	I Rx	12.3 mA
	I T x	14mA; 11.6mA; 7.4mA.

https://www.xeno-canto.org/


Aya Sakhri
email: aya.sakhri@univ-lorraine.fr

Oussama Hadji

Chakir Bouarrouguen

Moufida Maimour

Nasreddine Kouadria

Abderrezak Benyahia

Eric Rondeau

Noureddine Doghmane

Saliha Harize

Audio-visual low power system for endangered waterbirds monitoring

Keywords: Birds Monitoring, WMSN, Audio Denoising, ROI Extraction, Energy Efficiency

INTRODUCTION

Wetlands (marshes, lakes, wet meadows, . . . ) cover about six percent of the earth's surface. Characterized by a unique biodiversity, they represent one of the richest and most diverse ecosystems on our planet. Wetlands are home to a very wide variety of animal and plant species. They play a major role in biodiversity, in particular, they are privileged places for tens of thousands of waterbirds of different species to winter or make a temporary halt. Birds respond to possible changes in the environment and are good indicators of biodiversity. They also have a great role in environmental security. The decline of some avian species can cause serious problems in the food chain and affect environmental safety or even public health.

The State of the World's Birds [START_REF] Allinson | State of the world's birds[END_REF] revealed that we lost more than 161 species, which represents an extinction rate much higher than natural. Although some species can still be considered as breeding, others are already considered extinct in France. This gives great importance to wetlands as more than half of the wetlands have been destroyed over the last century due to urbanization, agricultural intensification and pollution. The survival of our environmental system from various dangers is a primary responsibility which can be achieved through endangered birds monitoring. Fauna and flora This work was supported in part by the PHC TASSILI 21MDU323.

monitoring in general and birds in their natural habitat in particular, has several interests [START_REF] Archaux | On methods of biodiversity data collection and monitoring[END_REF]. It mainly provide reliable indicators for regular assessment of the number of threatened birds, which serves environmental and even public health. Most of the time, environmental monitoring is done manually in a limited space and time. The presence of human observers in the field may disturb the birds behavior. Wireless sensor networks and more recently multimedia ones ( WMSN Akyildiz et al. (2007)) appear as a good alternative to set up an automatic environmental monitoring system [START_REF] Dyo | Wildsensing: Design and deployment of a sustainable sensor network for wildlife monitoring[END_REF]; [START_REF] Kizilkaya | An effective forest fire detection framework using heterogeneous wireless multimedia sensor networks[END_REF]. For birds monitoring purposes, [START_REF] Stattner | Acoustic scheme to count bird songs with wireless sensor networks[END_REF] proposed to use acoustic sensors for automatic counting of songbirds.

In this work, we propose a three-tier monitoring architecture that leverages both audio and visual sensors to identify and estimate the population of endangered birds species. To cope with WMSN limited resources especially in terms of bandwidth and energy, we suggest to reduce the amount of visual data to be processed as images captures, encoding and delivery are the main energy consumers in the whole system. A sensor network may evolve hundreds of nodes deployed in a harsh environment, which makes battery replacement a difficult task. Therefore, increasing the lifetime of the sensors to enable efficient operation of the network is a key issue. Multiple low-cost visual data compression algorithms have been proposed [START_REF] Zaineldin | Image compression algorithms in wireless multimedia sensor networks: A survey[END_REF]. The extraction of image features have been investigated by [START_REF] Civelek | Automated moving object classification in wireless multimedia sensor networks[END_REF] and others limited the image capture using motion detection as [START_REF] Magno | Multimodal video analysis on self-powered resourcelimited wireless smart camera[END_REF]. However, all these techniques are not sufficient since camera nodes continue to capture and process images including irrelevant ones.

In our system, we propose to only capture relevant images of target species. This can be achieved through a lowcomplexity audio-based recognition prior to triggering the camera. There are very few works that combine audiovisual data processing in a sensor node to decrease the energy consumption as proposed by [START_REF] Koyuncu | Visual and auditory data fusion for energy-efficient and improved object recognition in wireless multimedia sensor networks[END_REF]. However, they do not consider the overlapping of various environmental noises, a major constraint in our application since they degrade the sound quality which may result in false identification of species. To ensure an accurate and robust sound recognition, we additionally implement a noise reduction method prior to the audio-based recognition. The proposed overall system implements an efficient cooperative architecture with low-complexity audio-visual information processing with the ultimate objective of efficiently utilizing network resources while maximizing the amount of relevant information delivered to the end user.

The remainder of this paper is organized as follows. Section 2 describes our WMSN based bird surveillance architecture. The performance of our proposal with respect to other strategies is discussed in Section 3 before concluding.

WMSN-BASED BIRDS SURVEILLANCE SYSTEM

We propose the use of a surveillance infrastructure based on WMSN to monitor migratory birds in their natural habitat (wetlands). Acoustic sensors equipped with microphones and image sensors are to be deployed in conjunction with other types of sensors for presence detection, for example. The objective of this monitoring is to identify, recognize and count the number of a species of birds (considered threatened) on the basis of their vocalizations and photographs.

Network Model

We propose to adopt a three-tier architecture following the Cloud-Fog-Edge model as shown in Figure 1. The core of the network (edge) is composed of multimedia and scalar wireless sensors. In addition to capturing the data of interest, the sensors are capable of performing a number of processing tasks of moderate complexity with limited memory requirements. They transmit the raw or processed data to the base station or gateway (fog level) where moderate complexity processing can be performed. This level allows for greater computing resources while still being close to the data source and the end user. The cloud level allows for the most powerful analysis algorithms to be run, requiring more resources. This three-tiered processing also optimizes network bandwidth compared to single cloud-level processing. The cloud will also host a message broker based on protocols such as MQTT Standard (2014); [START_REF] Hunkeler | Mqtt-s a publish/subscribe protocol for wireless sensor networks[END_REF] which, through its publish/subscribe paradigm, allows a monitoring agent (subscriber) to be informed of any detected critical situation. With respect to our target application, instead of repeatedly sending images despite the absence of relevant information in the area being monitored, audio sensors are leveraged to allow for target detection before triggering the camera. The captured image is then compressed and transmitted to the cloud to confirm the presence of an event of interest as well as to perform further visual analysis such as counting and localization. The audio data recorded by the audio sensors are sent to the base station or gateway (fog level) to perform the audio based identification of the targeted birds. If a target species is identified, a request is sent back to a visual sensor node that covers the area of the audio origin to capture an image to be sent to the cloud.

Audio-based Target Identification

The proposed real-time audio recognition phase comprises three main steps. The first step consists in denoising the sensed audio signal to eliminate environment noises. To do so, we made use of a new geometric approach (GA) to spectral subtraction proposed by [START_REF] Lu | A geometric approach to spectral subtraction[END_REF] that improves the traditional spectral subtraction used by [START_REF] Weiss | Study and development of the intel technique for improving speech intelligibility[END_REF]. Despite the fact that the latter is less complex, the former solves known shortcomings of the former like the musical noise and signal distortion.

Then, to only consider meaningful information of the audio signal, distinctive features are extracted using Mel Frequency Cepstral Coefficients (MFCC ), key element of audio recognition. Their role is to identify the relevant components of the audio signal by eliminating irrelevant information. They are characterized by their computational efficiency and high performance under clean conditions [START_REF] Gupta | Feature extraction using mfcc[END_REF].

Finally, the extracted features are compared to reference ones using Dynamic Time Warping (DTW ) distance for classification purposes using a threshold based matching. DTW is one of the known low-complexity algorithms for measuring the similarity between two time sequences. It aligns two feature vector sequences by repeatedly warping the time axis to find the optimal match between the two vectors [START_REF] Müller | Dynamic Time Warping[END_REF]. If the processed sound is identified as a target, the camera will be activated and an image of the target is captured.

Image Processing

A captured image has to be sent to the end-user. In order to reduce the amount of data to be transmitted to the Fig. 2. Example of detected ROI Sink, we suggest compressing and transmitting only image blocks that contain regions of interest (ROI). Afterwards, we apply a low complexity compression on the selected blocks as to limit the processing resources and save energy.

ROI Detection.

Regions of interest are determined based on the features detected in the captured scene. A feature extraction method combines and evaluates variables to extract features, reducing the volume of data that must be processed, while maintaining a high level of accuracy and completeness of the original data. Image local features (also called hot spots and key points) can be defined as a single specific pattern from its immediately nearby pixels, which is usually associated with one or more properties of the image. These properties include edges, corners, regions, etc. These local features are converted into numerical descriptors representing a single and compact summary of these local characteristics. Local features (descriptive and invariant) provide a powerful tool that can be used in a wide range of computer vision applications. Many feature detection algorithms exist in the literature but there is no "one size fits all" solution because of the extreme variety of settings and possible scenes [START_REF] Salahat | Recent advances in features extraction and description algorithms: A comprehensive survey[END_REF].

To select a given algorithm, one has to consider its computation cost, its detected features and its accuracy. Considering the constraints of our edge nodes, the most vital aspect is the computational cost. This is why, we eliminate SIFT [START_REF] Lowe | Sift-the scale invariant feature transform[END_REF] although it is one of the most used keypoints detection algorithms with a high accuracy. SIFT exhibits a heavy computational burden which does not suit the limited resources at the edge wireless sensors. With respect to our bird surveillance application, we aim to detect moving birds that pose at different angles translation and rotation are much more important than scale [START_REF] Tareen | A comparative analysis of sift, surf, kaze, akaze, orb, and brisk[END_REF]. KAZE algorithm [START_REF] Alcantarilla | KAZE Features[END_REF] emerges as a good candidate since it is more accurate with rotation and translation. In KAZE algorithm, a predefined value of the primary scene feature number is stored. When a new scene arrives, the algorithm extracts the new features from the last captured and compares these values with the stored ones if the new value (number of features) is greater than the previous one, which means that the region of interest is detected ; otherwise the scene is empty. An example of detected ROI is shown in Figure 2.

ROI Compression.

Blocks identified as containing relevant visual information as a result of the previous paragraph are compressed using a low complex compression proposed in [START_REF] Maimour | SenseVid: A traffic trace based tool for QoE video transmission assessment dedicated to wireless video sensor networks[END_REF] where a fast pruned DCT is used with a triangular pattern in which only coefficients

PERFORMANCE ANALYSIS OF THE PROPOSED SYSTEM

In our surveillance application, we are concerned by monitoring three species of waterbirds threatened of extinction, namely, the Ferruginous Duck, the Common Pochard and the White Headed Duck. We assume that the monitoring is performed periodically N times during a day and compare the following scenarios (Figure 4) :

• S0 (resp. S0-ROI) : an image (resp. a ROI) is transmitted every period without audio identification ; • S1 : an image is only captured and transmitted when the audio identification detects a bird of interest without denoising. In S1-ROI, only ROI are transmitted ; • S2 (resp. S2-ROI, our proposed system) operates as S1 (resp. S1-ROI) but with a denoising phase prior to the audio identification.

In order to evaluate the performance of the different scenarios, we made use of iFogSim [START_REF] Gupta | iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments[END_REF] that allows modeling three-tier (edge-fog-cloud) architectures and considers physical, logical and management levels. The physical includes machine parameters at each level that are summarized in Table 1 as well as the edge devices characteristics. In our study, we considered sensor nodes equipped with an ARM Cortex M3 micro-controller cor (2018) and an ATMEL radio interface (Table 2). An audio sensor captures periodically a 2-second audio with a sampling frequency 22050 Hz. A visual sensor captures images of resolution 640 × 360 and compresses the image (or its ROI) prior to its transmission. The computational cost of the audio identification and the ROI extraction are summarized in Table 3. We assumed a link latency of 2 ms between the edge and the fog and 100 ms between the fog and the cloud. In iFogSim, an application is a collection of modules (tasks) linked by tuples (data flows) and modeled as a directed acyclic graph. We placed the modules at the appropriate level and set the characteristics of the tuples according to our scenarios.

Audio-based Identification Accuracy

We first, investigate the impact of the denoising step on the identification performance. We built a dataset of 500 noisy calls of our three target specimens that integrates a variety of noises encountered in our wetland. From our dataset, we selected 117 noisy target calls at three different SNR levels : 0 dB , 5 dB and 10 dB. In order to assess the behavior of the system when faced with non-target sounds, we added another 50 recordings. Among them, 30 belong to non-target bird species of the same area as our target ones, retrieved from Xeno-Canto website 1 . The remaining 20 recordings consist in various sounds that can be encountered in the natural habitat of our target birds. We, finally, used 40 clean target audio samples for reference. In total, 167 recordings were considered.

Figure 5 depicts the performance of the audio-based classification in S1 and S2 based on four metric. Precision is the proportion of relevant audio data among the retrieved sounds in the dataset. Recall (or sensitivity) indicates the ratio of the number of correctly identified birds to the total number of relevant calls in the dataset. F -measure balances between precision and sensitivity. Accuracy denotes the proportion of the total number of predictions that were correct in the dataset. We can clearly notice that S2 statistically outperforms S1 whatever the metric. The low performance of S1 for Recall, F -measure and Accuracy is attributable to the distortion of the audio call corrupted by environmental noises and the system was not able to identify most of the target bird calls. However, it was able to better identify the non-target calls due to the threshold The only difference is that in S2, birds calls were better recognized. To sum up, the S2 audio chain behaves better than S1 confirming the importance of the denoising phase in our proposal.

Latency and network usage

Figure 6 depicts the improvement obtained by our proposal over S0 and S1 strategies and their ROI variants with based on latency and network usage when considering the relevance of the reported information. We consider here the end to end delay required to obtain a useful information in which an event of interest is detected, from the audio capture until the end-user receives the visual information from the monitored area. Figure 6(a) plots the ratio of the delay achieved by our proposal (S2-ROI) with respect to the other scenarios as a function of the probability p that a target bird occurs in one period during the day. Figure 6(b) shows a similar metric but applied to network usage.

Our proposal improves in terms of both latency and network usage upon S0 and S1 where the entire captured images are sent which consumes more network resources.

The improvement is more pronounced with respect to S0 especially when the occurrence probability of a target is low. Audio identification at the fog leads to a great improvement in network utilization in contrast to S0 and S0-ROI where we send the images arbitrarily without prior identification.

Compared with S0-ROI and S1-ROI, our proposed system behaves better in almost cases. In fact, relevant visual data are more likely to be sent. Moreover, sending only ROI allows farther reducing the network resources up to 59% in all scenarios. When the probability of events of interest exceeds 0.8, S1-ROI achieves the same performances as our proposal. However, we lose a significant amount of important visual data when compared to S2. S0-ROI becomes attractive when events of interest are more frequent.

Energy Consumption

In this section, we investigate the performance of the different scenarios based on their energy requirements. To do so, we estimate the overall energy consumed in one day. This, depending on scenarios, includes the energy Figure 7 plots the ratio of the overall energy consumed in the different scenarios to the one of our proposal (S2-ROI) when the sensor node is able to reach the collection station in 5 and 10 hops respectively. First, we note that sending the ROI instead of the entire image reduces energy consumption for all the scenarios. The additional cost introduced by the ROI extraction is compensated by the energy required to transmit visual data. The improvement is higher when the number of crossed hops increases. It is clear that our proposal allows for an improvement when compared to S0 and S0-ROI that decreases with events of interest likelihood. The low energy consumption of S1 compared to S2 is explained by the low number of images identified as positive (TP). Without denoising, the system in unable to recognize the target birds which translates in fewer images being processed and transmitted. A significant amount of important data is lost.

To measure the impact of the loss of pertinent information in S1 when compared to S2, we consider the wasted energy to transmit irrelevant visual data using :

E W = (F P + T N + F N )E visual for S0 F P × E visual + (F P + T N + F N )E audio for S1 and S2
Figure 8 plots, for S1 and S2 and their ROI variants, the percentage of wasted energy to the overall consumed energy. We observe that when only ROIs are compressed and sent, the wasted energy is slightly higher with respect to their corresponding strategy where the whole image is considered. This is due to the fact that the overall energy is lower. We note, as expected, that scenario S1 wastes more energy compared to S2 especially when a target bird is less likely to show up in the monitored area. The amount of wasted energy is higher for lower number of hops to cross from the visual sensor to the end-user.

As a conclusion, the findings attest that our proposed system (S2-ROI) is the most suitable scenario. We do not waste excessive energy as in S0 and S0-ROI, which consequently prolongs the energy gain of the node's lifetime and improves the network performance. Furthermore, we do not loose important informative data as in S1 and S1-ROI. As a result, our system offers the best compromise since it preserves network resources in terms of latency, bandwidth and energy while allowing for relevant data to be provided to the end user.

CONCLUSION

In this paper, we proposed a three-tier cooperative architecture following the Cloud-Fog-Edge model and propose an adequate placement of different surveillance tasks. To limit network resources utilization and hence prolonging its lifetime, we mainly suggested to reduce the amount of visual data to transmit. We implemented and evaluated low-complexity methods for a more accurate audio-based identification as well as an efficient technique for ROI extraction that is more suitable to our targeted species.

Our performance evaluation show that the proposed system performs well in the identification of bird calls thanks to the denoising phase. Moreover, they attest that the transmission of compressed ROI instead of the entire image allows to reduce the network resources utilization in terms of bandwidth and energy while the amount of pertinent information is maximized. This proved the efficiency of our proposed architecture, in particular, the benefit of the cooperation between its three tiers.

As a future work, we expect to farther reduce the complexity of the different processing phases including the audiobased identification and the ROI extraction methods.

Fig. 1 .

 1 Fig. 1. Energy-efficient audio-visual surveillance strategy

Fig. 3 .

 3 Fig. 3. ROI block encoding sequence (ρ = 4).

Fig. 4 .

 4 Fig.4. Scenarios located at the upper left triangle of side length ρ ≤ 8 are considered as depicted in Figure3. The resulting DCT block coefficients are quantized using the JPEG standard quantization matrix. Trade-off between quality level and compression rate can be obtained by choosing an appropriate quality factor (QF). An image visual quality ranges from the poorest (QF = 1) to the best quality (QF = 100). The obtained block is then zigzag linearized before applying an exponential-Golomb code[START_REF] Teuhola | A compression method for clustered bit-vectors[END_REF] as a lossless entropy encoding.

1

  Fig. 5. Classification performance used for classification. This is what justify the high value of precision of S1. In fact, both S1 and S2 were able to identify the non target calls with equal performance.The only difference is that in S2, birds calls were better recognized. To sum up, the S2 audio chain behaves better than S1 confirming the importance of the denoising phase in our proposal.

Fig. 8 .

 8 Fig. 7. Improvement of overall consumed energy

Table 1 .

 1 Physical level parameters

	Parameters	Cloud Fog	Edge
	CPU(MIPS)	44800	2800	100
	RAM(MB)	40000	16000 128
	Uplink bandwidth(Kbps)	10000	10000 250
	Downlink bandwidth(Kbps) 10000	250	250

Table 2 .

 2 IoT-LAB M3 board parameters

	Micro-controller	Cortex M3 -72 MHz, Power = 23 mW;
	Cycles count	Add.[1], Mult.[2], Div.[12].
	Radio chip	ATMEL AT86RF231
		P ower

T x = 3 dbm, data rate = 250 kbps I T x = 14 mA, I Rx = 12.3 mA

Table 3 .

 3 Computational cost

	Step	Mult.	Div.	Add.
	Audio denoising (GA)	823,038	411,420	618,128
	Audio feature extraction (MFCC)	407,517	203,561	306,671
	Classification (DTW)	-	-	313,638
	KAZE nonlinear scale-space comp.	3,686,400	3,686,400 7,372,800
	KAZE feature detection	-	-	921,600
	KAZE feature description	-	30,464	-

  + F P ) E visual + N E audio for S1 and S2

		8							S1 /S2-ROI S0 /S2-ROI S1-ROI /S2-ROI S0-ROI /S2-ROI		16 32							S1 /S2-ROI S0 /S2-ROI S1-ROI /S2-ROI S0-ROI /S2-ROI
		4
	Latency ratio	2									Network usage ratio	2 4 8
		1
												1
		0.5 0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1	0.5 0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1
					Events of interest frequency							Events of interest frequency
						(a) Latency							(b) Network usage
	Fig. 6. Latency and network usage improvement.
	needed to process the audio recording, E audio , and the energy required to capture, encode and transmit visual data, E visual . The total required energy consumed by the network is given by :
		E =	N E visual (T P				for S0