Low-Cost Prioritization of Microorganism Image Data in Wireless Visual Sensor Network

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1 2016 International Computer Symposium Low-Cost Prioritization of Microorganism Image Data in Wireless Visual Sensor Network Fang Zhou *, Zhonglou Lu, Jun Liu, Yunfei Xia College of Electrical and Information Engineering Anhui University of Technology City of Maanshan, China 163.com Abstract Survivability is the primary problem in wireless visual sensor networks (WVSNs) when they are utilized for sewage monitoring with limited resources. In this paper, we propose a low-cost data prioritization framework for sewage microorganism microscopic images to overcome this frangibility of WVSNs. Firstly, an improved fast visual background extraction (Vibe) algorithm is proposed to rapidly detect indicative motion objects in wastewater. Then, based on the detection results and central weighted criteria, each image macro block is classified into different priority levels. Finally, we propose a flexible dynamic routing protocol depending on image data priorities to keep good balance between transmission delay and energy consumption. Experiment results demonstrate the efficiency and effectiveness in terms of microorganism detection and transmission cost. Compared with other state-of-the-art approaches, the whole network lifetime achieves significant increase under the same condition of network delay. Keywords wireless visual sensor network; data prioritization; motion object detection; dynamic routing I. INTRODUCTION As a new type of intelligent system, wireless visual sensor networks have been widely applied into various practical domains, such as patient monitoring, remote security surveillance et al, which also have attained considerable concerns in sewage treatment system recently [1]. It has been proved that the statistical properties of microorganisms in sewage, such as their population, abundance and activities play an important indicative role in water quality judgement [2]. Therefore, developing micro-image sensors and wireless intelligent networks in sewage system can significantly improve the efficiency of water treatment. However, the actual implementation of WVSNs remains several major challenges, which can be summarized as follows: 1) The battery power of sensor nodes are very finite and the lifetime of any individual sensor node directly decides the whole network lifetime; 2) Extensive image processing to detect anomalies from the raw video streams require huge energy consumption; 3) A large amount of network bandwidth is required for numerous sensors to send their raw video streams; 4) Wireless transmission has been confirmed on the verge of The work was supported by National Natural Science Foundation of China (No , No , No , No ), Anhui Natural Science Key Research Project for Universities (No.KJ2012A040, No MF129), and Teaching Research Project of Anhui University of Technology (No. 2013jy25). Xin Wei College of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunication City of Nanjing, China xwei@njupt.edu.cn its energy efficiency limit [3]; 5) When multiple sensor nodes transmit data at the same time, the probabilities of packet collision and delay will rapidly increase. To overcome these issues, recent researches make focus on such aspects as visual data coverage, cooperative image processing and energy efficiency tradeoff [4-8]. In [7], the authors proposed an available wireless video transmission system based on salient region segmentation, which can dynamically prioritize each image packet by a cost effective algorithm. However, this system only thought about the single sensor node streams, and did not offer any solution for reliable transmission of high priority data. Irfan et al [8] presented a prioritization frame for visual data in single and multi-camera wireless system based on salient motion detection, which can provide reliable transmission for high priority image data, but fail to consider the selected nodes working load and the whole network lifetime. Thus, in this paper, we propose a low-cost untied prioritization scheme with indicative microorganism detection and a flexible dynamic routing protocol based on image content to keep a better balance among the resource consumption, reliable transmission and packet delay, moreover to prolong the entire wireless network lifetime efficiently. The remainder of this paper is organized as follows. A fast moving microorganism extraction method is presented in Section II. The strategy of optimization grading for every object macro block is introduced in Section III. Section IV describes the novel dynamic routing protocol based on different prioritization levels in detail. Some experiment and comparison results are demonstrated in Section V. And the conclusions are drawn in Section VI. II. FAST MICROORGANISM DETECTION Vibe is a fast samples-based background subtract algorithm, which can assure a smooth exponentially decaying lifespan for the samples in image background model by using a type of random selection policy [9]. However, Vibe is only a pixelbased method and it cannot integrate the image region information into background model, which results in its severe fragilities to noise disturbance and high computation consumption. Therefore, in this section, we propose a regionbased Vibe algorithm to build background model for fast /16 $ IEEE DOI /ICS

2 object detection. A. Primary Partition An efficient graph-based image partition approach was presented in [10], which transformed each image to a graph with vertexes and edges. The image pixels were equivalent to the graph vertexes, and the edge value was obtained with the difference of neighboring vertexes. Then, Kruscal algorithm was utilized to divide raw image into different partitions, which had been proved to run in nearly linear time. In addition, we design an adaptive threshold based on Ostu algorithm for the graph edges to improve this approach. Once an edge value exceeds this threshold, it will be excluded from the graph edge set, which will considerably reduce the numbers of graph edges and the computation complexity for image primary division. B. Data Initialization Based on the primary region partition for each frame image of micro-video as above, we calculate every region mean value of all the pixels in this region, and then initialize each pixel as follows: (1) These pixel values will be applied to build and update the background model for object detection based on background subtract method. C. Region Classification For each input pixel I x, calculate the minimal and the second minimal Euclidean distances d 1 and d 2 respectively in all the samples, which are selected out by traditional Vibe initialization way. Assume the pixel values in every region satisfy the normal distribution, the probability of occurrence for pixel I x in one region can be defined as follows: (2) The accumulate weighted distance d R of region R can be calculated as: (3) Based on distance d R, the region R can be easily classified as background, foreground and undetermined as follows: (4) According to the region classification, all the pixels in this region obtain the same label. Only these pixels belonging to undetermined region need to be further identified by traditional pixel-based Vibe algorithm. III. OPTIMIZATION GRADING STRATEGY In order to improve the wireless network resource utilization, only the detected result data need to be transmitted to sink node in wireless network for abnormal monitoring. Moreover, the detection results will be further optimized and graded into different crucial levels for subsequent different transmission. 1) We introduce Gaussian function and Euclidean distance function to define the central weighted variable as follows: (5) Where, (x c,y c) is the central position of current frame image, and, in which W and H are the height and width of the image, respectively. 2) We design the salient optimization of the obtained image by sensor node i at time t as follows: (6) Where, [0,1] is the preset empirical value by experienced users for determining the importance of the surveillance domain of sensor node i. is the image area of detection objects of node i. 3) By setting different thresholds, the detection results can be divided into different optimization levels: (7) We map the optimization level into each data macro block whose size is 88, then every packet will conclude 66 bytes, in which 2 bytes are the macro offset values. IV. FLEXIBLE DYNAMIC ROUTING PROTOCOL According to the theory of reactive opportunistic routing, we propose a flexible dynamic and hierarchical routing protocol closely relevant to image data crucial levels in this section. The core idea can be described that only very important data occurs, the best routing discovering process starts. In addition, each relay node selection of next hop will comprehensively consider the energy efficiency, transmission delay and error rates of each sensor node in its neighbor domain, which ensure the higher optimization level data arrive the sink node accurately and timely. A. Channel Model In wireless network, the packet error rate (PET) cannot be avoided for any sensor node to transmit any type of packet through any link channel. For more realistic to reflect the damaged wireless link channel, we adopt binary phase shift keying method to define PER as follows: Here, P t is the transmission power, V is the length of data, (8) 686

3 and is the noise power. In addition, (9) (10) Where, A is a constant, describes the distance between sender node s and next hop node i, and n is on behalf of the loss component of wireless channel. Among them, P t is very crucial to energy consumption and completely connectivity for overall wireless network. If P t is too high, it will cause energy waste and increase the signal conflict probability among the adjacent sensor nodes. At the same time, it also will increase the amount of relay nodes. On the contrary, if P t is too low, it will influence the network connectivity, thus it only depends on increasing sensor nodes to deal with, which will enhance the whole network delay. Therefore, in this paper, we design different PER ranges for different data optimization levels to allocate reasonable transmission power for every sensor node. B. Packet Grading Mechanism After the microorganism objects are automatically detected and graded, optimization level information will be inserted into every packet, which is helpful for other relay nodes to understand the importance of this packet. When one packet has a higher level, a better path is chosen to send it under the conditions of current sensor node and link channels. Once various data with different optimization levels need to send simultaneously, the lower level data have to wait. In addition, when there is no or little communication flows in wireless network, each sensor node sends location packet to sink node periodically, which makes it easy to record the update of all sensor node location and amount. We define the optimization level of location packet as 0. The details of packet grading mechanism are presented as follows: Levels L k=0 L k=1 L k=2 TABLE I. PACKET GARDING MECHANISM Mechanism Description Not important No important packet should be transmitted immediately. Little important The packet should be transmitted immediately, but it can tolerate some delay. Very important The packet should be transmitted immediately with minimum delay. All the other packetes have to wait. C. Data Transmission Scheme Except the DATA packet, there are also three kinds of packets in our dynamic routing protocol. They are ACK, RTS and CTS packet respectively, in which RTS is the packet of request of send and CTS is the packet of clear to send. Each type of packet is restricted by PER. When one sensor node need send data packet, it firstly makes a RTS/CTS handshake with its adjacent nodes or candidate nodes to discover an available channel, then flooding a RTS packet to wait the first response. According to the current network condition and the distances among different sensor nodes, there will several nodes can receive the RTS packet from sender. Assume the node k has received this RTS packet, and it does not have other packets with higher optimization levels to deliver, the backoff time for node k sending CTS packet is defined as: (11) Where, is the evaluated mean value of all the packet optimization levels in microorganism surveillance system according to grading strategy as previously mentioned. D describes the distance from node i to node k. SIFS represents short inter-frame space. B 0 and B 1 are two fixed constants. When node k detects an available channel C i, it will send CTS response packet through this channel, otherwise, it will continue wait. While node i successfully receives the first response packet from node k, it will send image data packets through the same channel C i immediately, and neglect the other CTS responses from other sensor nodes in the meantime. After that, node i will wait for the ACK packet from node k. From this transmission scheme, we can see that the selection of next hop relay node in wireless network, is not only associated with the position of sensor nodes, but also closely related to the packet optimization levels. When packet has low optimization level, the node closest to the sender node will achieve least amount of backoff time, which will result in more jumps for the packet to transmit to sink node. Conversely, when packet has higher optimization level, the edge relay node will be selected out at the maximum transmission range of sender node, which can effectively reduce the whole network delay. In addition, the simple handshake and acknowledge schemes in our protocol have also ensured the reliable data transmission. What is more, every relay node performs the same operation process, so subsequent packets can choose different channel and path according to different network condition and data optimization levels at present, which fully reflects the dynamic and hierarchical characteristics of the proposed routing protocol. V. EXPERIMENT AND COMPARISON Our simulation platform is under Matlab.R2015a with Inter Core i5 2.45GHz CPU, 4GB RAM and 64bits Window 7 OS. The resolution of micro-video frame is about In this section, we mainly give some of comparison results including object detection and dynamic routing to demonstrate efficiency and effectiveness of our proposed united prioritization algorithm. A. Object detection There are several parameters in our proposed region-based Vibe method, they are respectively set as follows:,,, and. In addition, Five most common metrics are adopted, which are Precision, Recall, F- 687

4 measure, PWC, and Average Processing Rate of per frame [2]. Four state-of-the-art object detection algorithms are utilized to compare with our proposed method [2]. Some of experiment results are presented in Fig.1, Tab.2 and Tab.3. From these figure and tables, we can clearly see that our method has achieve better performance, especially, it obtains better balance between detection quality and process efficiency. B. Dynamic routing In this subsection, we randomly distribute the sensor nodes into a (m 2 ) domain. The communication parameters are listed in following Tab.4, which are selected according to IEEE TABLE IV. SIMULATION PARAMETERS OF NETWORK Parameter Value Unit DATA/RTS/CTS/ACK Packet 66 byte SIFS 10 us Pt 15 mw Maximum Deliver Range 10 m Bit Rate 250 kbps B B 1 8 In addition, we assume each sensor node has two pairs of AA batteries with 2Ah capacity, so anyone of them has about 22J initial energy. Fig.2 has presented the comparison results of whole network lifespan under different sensor distribution densities between our proposed dynamic routing protocol based on data optimization levels and traditional fixed routing protocol, which deliver all the packets towards sink node through the same best chosen path or fixed pre-allocated path [8]. Fig.1. Quality comparison results of microorganism detection on (a) Original sequences, (b) Ground truth, (c) MoG, (d) CB, (e) SOBS, (f) Vibe, and (g) Our method TABLE II. QUANTITY COMPARISON OF DETECTION RESULTS Precision Recall F-measure PWC MoG CB SOBS Vibe Ours AVR (s/f) TABLE III. AVERAGE PROCESSING RATE (AVR) MoG CB SOBS Vibe Ours Network lifetime (hour) Fixed routing Dynamic routing Average neighbor number of each sensor node Fig. 2. Wireless network lifetime under various sensor distribution densities From above chat, we can see that following fixed routing scheme, the sensor nodes keep forwarding all kinds of packets and eventually run out of energy quickly, which greatly shorten the whole network lifetime. On the contrary, the proposed dynamic routing scheme tries to discover different paths for different important data, which comprehensively consider the location of next hop and the optimization level of every packet simultaneously. For more significant packets, the edge nodes with sufficient energy in sender transmission range are prefer to be chosen in precedence, which reduce the packet delay effectively. Because sensor nodes on best path do not participate in every packet delivering, they can well conserve their energies. Moreover, as the sensor density increases, more node choices can be obtained, which will prolong the network lifetime obviously. 688

5 VI. CONCLUSION In this paper, we propose a low-cost united prioritization frame for micro-video data processing and transmission in wireless sensor network, which is very crucial for wireless surveillance in sewage treatment system. The scheme mainly includes three sections, one is a region-based Vibe algorithm for fast microorganism detection from complicated sewage background, the other is a data optimization grading mechanism based on object detection results, and the final one is a novel dynamic routing protocol relevant to data optimization levels. Experiment results show that our proposed united prioritization scheme not only can detect objects efficiently and effectively, but also improve the wireless network lifetime significantly. ACKNOWLEDGMENTS We would thank all of the academic experts who have contributed this paper in valuable discussion and assistance with the experiments. The work was supported by National Natural Science Foundation of China (No , No , No , No ), Anhui Natural Science Key Research Project for Universities (No.KJ2012A040, No MF129), and Teaching Research Project of Anhui University of Technology (No. 2013jy25). REFERENCE [1] Y.P. Han, J.X. Liu, X.S. GUO, L. Li. Micro-environment characteristics and microbial communities in activated sludge flocs of different particle size. Bioresource Technology. Vol.124, No.11, 2012, pp [2] F. Zhou, J. Liu, T, Zhang, Fast Background Subtraction for Microorganism Detection in Wireless Visual Sensor Networks, Journal of Internet Technology, Vol. 17, No.3, 2016, pp [3] B. Sahoo, S. Rath, D. Puthal, Energy efficient protocols for wireless sensor networks: a survey and approach, Int. J. Computer, Vol.2012, No.44, 2012, pp [4] R. Tan, G. Xing, B. Liu, J. Wang, X. Jia, Exploiting data fusion to improve the coverage of wireless sensor networks, IEEE/ACM Trans. on networks, Vol.2012, No.20, 2012, pp [5] O. Demigha, W. Hidouci, T. Ahmed, On energy efficiency in collaborative target tracking in wireless sensor network: a review, IEEE Commun. Surveys Tutorials, Vol.15, No.3, 2013, pp [6] L. Zhou, R. Hu, Y. Qian, and H.-H. Chen, Energy Spectrum Efficiency Tradeoff for Video Streaming over Mobile AD Hoc Networks, IEEE Journal on Selected Areas in Communications, Vol.31, No.5, pp , May [7] K. Irgan, C. Ünsalan, S. Baydere, Low-cost prioritization of image blocks in wireless sensor networks for border surveillance, J. Netw. Comput. Appl. Vol. 38, No.2014, pp [8] M. Irfan, S. Muhammad, E. Waleed, W.B. Sung, Saliency-directed prioritization of visual data in wireless surveillance networks, Information Fusion, Vol. 24, No.2015, pp [9] O. Barnich and M. V. Droogenbroeck, Vibe: A Universal Background Subtraction Algorithm for Video Sequences, IEEE Transactions on Image Processing, Vol. 20, No. 6, pp , December, [10] P. Felzenszwalb and D. Huttenlocher, Efficient graph-based image segmentation, IJCV, Vol.59, No.2, 2004, pp