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  • 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 103 VAQMAC: VIDEO AWARE QoS MAC PROTOCOL FOR WIRELESS VIDEO SENSOR NETWORKS Vijay Ukani 1 , Tanish Zaveri 2 , Sameer Kapadia 3 1 Computer Science & Engineering Department, Institute of Technology, Nirma University, Ahmedabad - 382 481, Gujarat, India. 2 Electronics and Communication Engineering Department, Institute of Technology, Nirma University, Ahmedabad - 382 481, Gujarat, India. 3 IP.Access, Pune, Maharashtra, India. ABSTRACT Multimedia applications over wireless sensor networks are emerging rapidly. There is an increasing interest in the research community to design and develop critical services which require video monitoring or emergency voice calls over WSNs. On the other hand, storage, processing and bandwidth limitations of sensor nodes make multimedia data transmission a challenging issue for WSNs. In such context, multimedia coding and efficient exploration of the application contents at other layer of the stack may enhance the efficiency of WMSN applications. IEEE 802.11e is a variant of IEEE family of LAN standard which is most suited for QoS hungry multimedia traffic in modern Wireless Local Area Networks. However, it fairs poorly capable of handling multimedia flows efficiently in congested networks. The main reason poor performance is due to static resource allocation specified in IEEE 802.11e. The default TXOP limit values and priority assignment to I, B, P frames werestatically assigned. In this work, video aware cross-layer mechanism is proposed.Under the modified EDCA that permits dynamic allocation of TXOP limit values and change the priority of frames during congestion. The proposed protocol also copes with high load situations by selectively prioritizing and mapping multimedia frames to appropriate Access Categories (AC’s). Key words: MAC Protocol, Quality of Service, Video Transmission, Wireless Sensor Network, Cross Layer, Video- Aware Transmission. INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2014): 7.2836 (Calculated by GISI) www.jifactor.com IJECET © I A E M E
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 104 1. INTRODUCTION Wireless Sensor Network (WSN) is a collection of resource constrained tiny devices capable of measuring and retrieving important phenomenon. Applications of WSN are ever increasing due to its capabilities in monitoring applications like traffic monitoring, industrial process monitoring, habitat monitoring etc. Wireless Multimedia sensor network is a network of miniature sensor devices that consist of CMOS cameras and microphones possibly along with scalar sensors that retrieve multimedia content such as audio, video streams, still images and scalar sensor data from the environment[1][23]. Traditional WSN equipped with scalar sensors like temperature, pressure, humidity, electro-magnetic has high false alarm rate in detecting events. Multimedia sensor networks complements the scalar WSN by capturing every minute detail about the phenomena and thus reducing the error rate in event detection/classification thus improving applications like surveillance and disaster monitoring. The coverage model of video sensor nodes is quite different than scalar sensors. Scalar sensors tend to have omnidirectional coverage while video sensors have directional coverage. The coverage of video nodes is modeled as Field of View (FoV). Due to sector like coverage of the video sensor, events occurring behind the node is not detected. So a greater deployment density is expected in the deployment of video sensor network. Due to limited availability of bandwidth (up to 250 Kbps in MICAZ motes [MICAZ]) in WSN, the visual data sensed by source nodes have to be compressed and transmitted to the sink over the sensor network [13]. Also variable environmental conditions leads to high error rates. Looking into limited bandwidth availability and high error rate, some multimedia coding providing error resilience is needed. This may sustain the minimum acceptable end-to-end quality of the application, even when some packets are lost while transmitted over error-prone wireless links. Most of the video encoding techniques creates packets of varying importance. The important packet should be treated with high priority at all levels in the communication stack. The IEEE 802.11e MAC protocol is a member of IEEE family of LAN standard [15]. IEEE has suggested several improvements over standard IEEE 802.11 for enhancing the data rate of transmission. However, due to equal treatment to each packets by DCF in IEEE802.11, enhancement in data transmission rates may be insufficient approach for multimedia applications. DCF implements single queue model and does not support traffic prioritization. This deficiencies of 802.11 lead to development of a new QoS-aware MAC layer protocol capable of assigning different priorities to different packets named as IEEE802.11e [15]. The prioritization offered by IEEE802.11e is static in nature i.e. a specific type of packet is always mapped to a particular level of queue. Sometimes in multimedia transmission, based on current occupancies in the queue, dynamic mapping of packets to different queue is needed. As adaptive approach to mapping video packets to appropriate queue is proposed and evaluated in this work. 2. 802.11E As opposed to single queue model of IEEE 802.11, the new 802.11e proposed to have multiple queues with varying priority. It defined four Access Categories (AC) with each with different precedence. The packets were mapped to either of these ACs using mapping rules. The EDCA mechanism is a modified version of standard DCF designed to support differentiated and distributed channel access [19][22].
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 105 Figure 1: Multiple backoff with different priorities 802.11e provides service differentiation by providing four different levels of priorities from 0 to 3, with 3 being the highest priority [16][17]. It implements four ACs corresponding to 4 priority level. A frame is marked by upper layer into a priority class before being sent to MAC layer for forwarding. 802.11 has designated the four ACs for specific type of traffic. These are: AC(3) for voice traffic, AC(2) for video traffic, AC(1) for best effort traffic and AC(0) for background traffic[5]. Each AC works as an independent back-off entity, and the differentiation among ACs is provided by specific parameters. The differentiating parameter are minimum contention window, maximum contention window and Arbitration Inter Frame Space (AIFS) as shown in figure 1.The parameters are shown on table 2. Each AC is variation of DCF called EDCAF and each frame is mapped to appropriate AC according to its priority value as shown in Table I. Table 1: Priority to access category mappings Traffic Type Priority Access Category (AC) Voice Background Traffic 3 3 Video 2 2 Best Effort 1 1 Background 0 0 Each MAC frame is delivered through multiple backoff instances within one station, each parameterized with its specific parameters. During the contention period, each AC contends for a transmission opportunity (TXOP) and independently starts a backoff after detecting the channel being idle for an AIFS[18]. The AIFS has at least DIFS duration, and can be enlarged individually
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 106 for each AC. After waiting for AIFS, each backoff sets a counter to a random number drawn from the interval [1, CW+1]. The minimum and maximum value for CW based on AC as shown in Table 2. Table 2: Parameters of EDCA for different access categories Parameters AC(0) AC(1) AC(2) AC(3) AIFS 7 3 2 2 CW_MIN 31 31 15 7 CW_MAX 1023 1023 31 15 TXOPLIMIT 0 0 0.006016 0.003264 In case of voice transmission, smaller packets are transmitted. Each packet transmission needs to contend for channel, leading to delay in transmission. To allow such stations to transmit multiple packets without contending for each packet, 802.11e provides a station the right to transmit for time indicated by Transmission Opportunity (TXOP) leading to Contention Free Burst (CFB) transmission. The default values of AIFS, CW and TXOPLIMIT is specified by IEEE standard. The CFB is enabled for multimedia ACs(AC(3) and AC(2)) but not available for other ACs. Authors in [14] developed a dynamic TXOP limit adaptation scheme based on the average number of packets allocated in QAP queues in an attempt to alleviate the downlink/uplink problem. The values of TXOP is adaptively computed by each AC at the beginning of each beacon interval.A distributed enhanced TXOP (ETXOP) limit adjustment mechanism was proposed in [11] which computes new value whenever a AC(2) or AC(3) i.e video or audio AC’s wins the contention. This mechanism of computing TXOP is based on the priority of ACs. In both cases, the TXOP value is computed based on the current occupancy in the AC queue. In [12], Ksentini et al proposed a QoS cross-layer mechanism involving application and MAC layer cross communication for improving H.264 video transmission over IEEE 802.11e networks. The approach relies on data partitioning technique at the application layer. Thepackets are prioritized by the encoder and are mapped to appropriate ACs based to their importance. The multimedia traffic is mapped to AC(3), AC(2) and AC(1) while AC(0) is used for all other traffic. The retry count parameter is used to protect various frames unequally. High priority frames have higher retry count compared to low priority frames. A similar mechanism for MPEG-4 video involving multilevelqueue by assigning I-frames to AC(3), P-frames to AC(2), B-frames to AC(1) and non-video frames to AC(0) was proposed in [6].An algorithm for dynamically mapping video frames to appropriate ACs was proposed in [2]. The packets were differentiated and higher priorities are given to forward packets. When queue length of AC(2) fills up, forward packets are remapped to lower access category AC(1). However with multiple queues also it has been proved that for multimedia traffic under congestion condition, it deemed insufficient [3][4][10]. Thus, it is necessary to provide multimedia QoS provisioning by following a crosslayer design that combines the multimedia traffic characteristics along with strategies offered by lower layer. Also other concepts like TXOPLIMIT adaptation and acknowledgement policy can be exploited.
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 107 3. MULTIMEDIA CHARACTERISTICS AND EVALUATION Any application requiring streaming of multimedia data is always demanding than any other data sensing applications in wireless sensor networks. This is due to the extensive requirements for complex audio/video encoding. Due to limited resource availability on sensor nodes, it demands video coding which is simple, low data rate, error resilient and energy efficient as faras possible. In order to achieve good quality transmission at lower transmission rates, codecs should reduce the total data using some compression technique. The compression tends to be time consuming and processor hungry and at the same time usually lead to some information loss. Several encoding video techniques were proposed in literature for resource constrained WSN including distributed source coding and Multi Descriptive Coding (MDC)[1]. As of now, most of current Video sensor network platforms and prototypes focus on well-known intra-frame compression algorithms and rely on in-network processing for data reduction. MPEG is one of the most widely used video encoding technique [20]. MPEG encoder the raw video in three types of frames viz. intra-coded frame I-frame, predictive coded frame P-frame and bidirectional coded B- frame. The I-frames are intra coded, i.e. they are coded using spatial compression and can be reconstructed without any reference to other frames. The P-frames are inter-coded, uses spatial and temporal compression and are forward predicted from the last I-frame or P-frame. Reconstruction of a P-frame needs data of another frame (I or P). The B-frames like P-frames are inter-coded but are both, forward and backward predicted from the last/next I-frame or P-frame, i.e. there are two other frames necessary to reconstruct them. In a typical MPEG encoder I, P and B frames are organized into Group of Pictures (GoP). The sequence of frames in a GoP that make up a GoP is called GoP sequence. A GoP contains sequence of frames starting from a I-frame to next I-frame (non inclusive).A GoP sequence is characterized by the number of frames, N in a GoP and the number of B-frames, M between successive P-frames [8][20].For e.g. G9B2 has 9 frames in GoP with 2 B-frames between successive P-frames leading to structure like I B B P B B P B B I. I-frames are intra-coded frame exploiting only spatial redundancy using JPEG encoding hence they can be decoded without depending on any other frame. Other frames P and B exploit temporal redundancy and are inter-coded with reference to the preceding I or P-frame using motion estimation and compensation. As temporal redundancy is exploited by P and B frame, they tend to achieve higher compression ratio but at the same time they are dependent on other frames for decoding [9]. As I-frame is the only intra-coded frame, loss of I-frame will leave a GoP completely undecodable. The next important frame is P-frame as subsequent P and B-frames are dependent on it. Quality loss incurred due to loss of P frame depends on its relative position in the GoP. An MPEG decoder uses the I-frame as the reference frame for remaining frames in a GoP. Any Impairment in I-frame will lead to artifacts that propagates through that GoP, and the video will recover only in the next GoP when an unimpaired I-frame is received. Due to large size of I-frame, it may need to be transmitted in multiple IP packets. Loss of apacket at the beginning of the I-frame which carries frame header information will lead topixelization that will continue through the GoP. A Packet loss bearing I-frame but not carrying header information will result in slice errors that will continue through the GoP. Loss of a P-frame also has some effect in decoding quality. A GoP contains many P-frames. The effect of P-frame loss depends on the position of lost frame in the GoP. P-frames in initial part of GoP create alonger impairment because many subsequent frames are used as reference frame by following P or B-frames. Alsolike an I-frame loss, for video sequence with higher motion, loss of P-frame also results in greaterthe pixelization. B-frames are considered as low significant frames in the GoP as they are not referencedby other frames and loss of B-frame do not have much effect on video quality.
  • 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 108 Several experiments were carried out to study the effect of frame loss on quality of reception. The quality of reception was measured with video quality measures like Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Video Quality Measure (VQM). The experiment was performed on foreman QCIF video sequence with 176x144 resolution with 300 frames and frame rate of 30 fps. The GoP length was set to 30. The plots in Figure 2 shows quality of individual frames of a GoP measured with PSNR, SSIM, and VQM. The plots shows effect of losing the only I-frame, first P-frame and 11th P-frame. Figure 2: Effect of frame loss on received video quality The PSNR metric compares the quality of the video received by the user with the original video by frame by frame comparison. ThePSNR is expressed in dB (decibels). For a video to be considered as good quality, itshould have an average PSNR of at least 30dB. The PSNR values typically lies between 30 dB to 50 dB, with higher values considered better than lower ones.The SSIM measures the structural distortion of the video, which tries to obtain a better correlation with the user's subjective impression. The value of SSIM varies between 0 and 1 with values closer to 1 indicates better video quality. The VQM metric is a measurement of the "perception damage" the
  • 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 109 video has experienced. It works on the Human Visual System (HVS) characteristics, including distinct metric factors such as blurring, noise, color distortion and distortion blocks. VQM results varies from 1 to 5, where values closer to 1 indicates better video quality. 4. VIDEO-AWARE QoS MAC (VAQMAC) A cross-layer video aware MAC protocol is proposed in this work, whichintegrates the video information with MAC layer strategies. Considering the characteristics of WSN, where an event is detected, captured and reported by large number of nodes, congestion conditions are frequently possible. VAQMAC is an integrated QoS provisioning mechanism that combines application and MAC layer features to achieve its purpose. As per IEEE802.11, all the video frames are to be mapped to AC(2). In VAQMAC, the video frames are mapped to AC(2) and AC(3). It maps I and P frames to AC(3) as I and P video frames are considered as the most important video frames, they must be transmitted with the highest possible priority[7]. B frames being the lower priority video frames are mapped to AC(2). Data traffic is mapped to AC(1). Once contention is won by an AC, appropriate strategy is to be selected for frame transmission. As AC(3) contains I and P frames which are larger in size, it might quickly run out of buffer in case of congestion. It is proposed to adapt TXOPLIMIT to transmit more frames and clear the buffer as early as possible. Now to overcomethe congestion situation, queue occupancy is monitored constantly. Once the queue occupancy of AC(3) crosses the threshold, which is taken as 70% of the initial queue length, it means network traffic is high in AC(3) and action to alleviate this congestion is to be taken. Threeapproaches are used in VAQMAC, first the incoming P frames are redirected mapped to AC(2) and second TXOPLIMIT is increased by a factor of αwhich is a function of current queue occupancy(higher the queue occupancy, higher the value of α) and third the traffic is delayed by 0.2 unit of time. Once the queue length reaches to its stable condition i.e. less than or equal to threshold value, incoming P frames are again mapped to AC(3).This will lead to fluctuation in the mapping process. So RED like thresholds are used with a lower threshold and an upper threshold. Upon crossing the upper threshold, the P-frames are mapped to AC(2) until the queue occupancy falls below lower threshold. When queue occupancy of AC(2) is above upper threshold value, three approaches are used, first the incoming B frames are mapped to AC(1), and second TXOPLIMIT is increased by a factor of αwhich is a function of current queue occupancy. Increase in TXOPLIMIT is needed as P-frames were redirected to AC(2) and due its large number and size, it tends to get full in short time.The third approach is to delay the traffic by 0.2 unit of time. However when queue length reaches lower threshold value, incoming B-frames are again mapped to AC(2), TXOPLIMIT will be restored to original value as earlier. In existing 802.11e EDCA the TXOP limit parameter is static and different for all AC’s as shown in Table 2 but in VAQMAC, the TXOP limit parameter is tuned according to the queue length of respective AC’s. The proposed approach is demonstrated in Figure 3 and 4. 5. SIMULATION SETUP The VAQMAC was simulated in ns-2 with WSN support being provided by nrlsensorsim. IEEE 802.11e EDCA patch is used for 802.11e support. The network consisted of 51 nodes. Sources generates video traffic using evalvid[21] and CBR traffic. Initial energy of each node is set to 20 joules. One of the node is sink node. The number of sources generating traffic is varied to induce congestion situation.
  • 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. Figure 6. RESULT ANALYSIS The proposed protocol, VAQMAC is compared with standard 802.11e Packet Delivery Fraction (PDF), End Standard deviation in PSNR and PSNR ( shows a frame of the video transmitted and corresponding frame reconstructed at the sink. Figure to 10 depicts the results obtained from simulations for the average average PDF (for video+CBR traffic deviation. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 110 Figure 3: VAQMAC approach The proposed protocol, VAQMAC is compared with standard 802.11e EDCA in terms of , End-to-end delay as network quality of service metrics and PSNR (Peak signal to noise ratio) asvideo quality metrics shows a frame of the video transmitted and corresponding frame reconstructed at the sink. Figure depicts the results obtained from simulations for the average PDF (for video traffic), average PSNR, average End-to-end delay and International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – © IAEME EDCA in terms of as network quality of service metrics and quality metrics. Figure 5 shows a frame of the video transmitted and corresponding frame reconstructed at the sink. Figure 6 video traffic only), end delay and PSNR standard
  • 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. Figure 4 shows difference in quality of video frames of transmitted (A) Transmitted Video frame Figure PDFResults (Video traffic only) Figure 5 shows that the packet delivery ratio of VAQMAC is better that 802.11e EDCA when only video data is transmitted.Initially with less number of traffic sources, the PDF is almost similar but as the number of traffic sources are increased, the packets drops rapidly overflow of queues at intermediate nodes whereas TXOP limit of each queue and mapping the frames to appropriate AC’s Figure 5: Avg p PDF Results(Mixed traffic) As shown in Figure 6, the avg. PDF existing EDCA, as the number of traffic sources are increased, the packets drops rapidly whereas VAQMAC can handle more traffic and shows improvement in packet drops. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 111 shows difference in quality of video frames of transmitted video and reconstructed frame (B) Reconstructed Video frame Figure 4: Reconstructed Video shows that the packet delivery ratio of VAQMAC is better that 802.11e EDCA when only video data is transmitted.Initially with less number of traffic sources, the PDF is almost similar as the number of traffic sources are increased, the packets drops rapidly in EDCA overflow of queues at intermediate nodes whereas VAQMAC can handle more traffic by tuning the TXOP limit of each queue and mapping the frames to appropriate AC’s. vg packet delivery fraction with 51 nodes As shown in Figure 6, the avg. PDF for video and CBR traffic shows the similar results for existing EDCA, as the number of traffic sources are increased, the packets drops rapidly whereas can handle more traffic and shows improvement in packet drops. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – © IAEME constructed video. Reconstructed Video frame shows that the packet delivery ratio of VAQMAC is better that 802.11e EDCA when only video data is transmitted.Initially with less number of traffic sources, the PDF is almost similar in EDCA due to can handle more traffic by tuning the for video and CBR traffic shows the similar results for existing EDCA, as the number of traffic sources are increased, the packets drops rapidly whereas
  • 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. Figure 6: A End-to-end delay The avg. end-to-end delay in Figure delay of frames is more as compare to 12 are experiencing more delay due to congestion at intermediate nodes whereas in to dynamic mapping of frames during congestion, the traffic sources experience lesser delay. Figure 7: Average end Peak Signal to Noise Ratio (PSNR) The quality of reception is measured in terms of PSNR. The PSNR of a video is computed by comparing individual frames of transmitted and received video. The avg. PSNR is computed for different video sources for EDCA and VAQMAC and is demonstrated in Figure values are above 25 dB, whileexisting EDCA sensor nodes. PSNR values above 30 dB dB for a video isconsidered as poor quality video. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 112 Avg PDFfor mixed traffic with 51 nodes in Figure 7shows that for existing EDCA the average end delay of frames is more as compare to VAQMAC. As it can be observed, the traffic sources 4, 7 and 12 are experiencing more delay due to congestion at intermediate nodes whereas in to dynamic mapping of frames during congestion, the traffic sources experience lesser delay. Average end-to-end delay with 51 nodes Peak Signal to Noise Ratio (PSNR) The quality of reception is measured in terms of PSNR. The PSNR of a video is computed by comparing individual frames of transmitted and received video. The avg. PSNR is computed for CA and VAQMAC and is demonstrated in Figure 8. Majority of PSNR above 25 dB, whileexisting EDCA suffers from lower PSNR values due to alues above 30 dB normally indicates a good video quality, anything for a video isconsidered as poor quality video. Obviously, VAQMACperforms better than International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – © IAEME shows that for existing EDCA the average end-to-end traffic sources 4, 7 and 12 are experiencing more delay due to congestion at intermediate nodes whereas in VAQMAC, due to dynamic mapping of frames during congestion, the traffic sources experience lesser delay. The quality of reception is measured in terms of PSNR. The PSNR of a video is computed by comparing individual frames of transmitted and received video. The avg. PSNR is computed for Majority of PSNR suffers from lower PSNR values due to congestion at anything below 25 performs better than
  • 11. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. existing EDCA due to the prioritization of significant frames to appropriate AC’s during congestion. Figure 9, also shows that for existing EDCA the deviation in received video from original video is more as compare to VAQMAC. More the deviation, lesser the quality of received video. Figure Figure 7. CONCLUSION VAQMAC exploits the multimedia packet semantics to selectively prioritize and protect important frames. It also adaptively assigns network resources to frames based on state of the network. The TXOPLIMIT adaptation results in transmitting the queued packets as a burst leading to decrease in end-to-end delay of the important packets. Also mapping of frames to different ACs based on current occupancy of the queue improves the performance of video transmissi wireless sensor network. By integrating application layer semantics with MAC layer strategies, VAQMAC has proven to be extremely beneficial in terms of QoS evaluation metrics. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 113 prioritization of important video frames and dynamic mapping of least significant frames to appropriate AC’s during congestion.The standard deviation of PSNR shows that for existing EDCA the deviation in received video from original video is ore the deviation, lesser the quality of received video. Figure 8: Avg PSNRwith 51 nodes Figure 9: Standard deviation in PSNR VAQMAC exploits the multimedia packet semantics to selectively prioritize and protect important frames. It also adaptively assigns network resources to frames based on state of the adaptation results in transmitting the queued packets as a burst leading to end delay of the important packets. Also mapping of frames to different ACs based on current occupancy of the queue improves the performance of video transmissi . By integrating application layer semantics with MAC layer strategies, VAQMAC has proven to be extremely beneficial in terms of QoS evaluation metrics. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – © IAEME video frames and dynamic mapping of least tandard deviation of PSNR as in shows that for existing EDCA the deviation in received video from original video is ore the deviation, lesser the quality of received video. VAQMAC exploits the multimedia packet semantics to selectively prioritize and protect important frames. It also adaptively assigns network resources to frames based on state of the adaptation results in transmitting the queued packets as a burst leading to end delay of the important packets. Also mapping of frames to different ACs based on current occupancy of the queue improves the performance of video transmission over . By integrating application layer semantics with MAC layer strategies, VAQMAC has proven to be extremely beneficial in terms of QoS evaluation metrics.
  • 12. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 114 REFERENCES [1] D. G. Costa and L. A. Guedes., “A survey on multimedia-based cross layer optimization in visual sensor networks”, Brazil, May 2011. [2] J. Zhang and J. Ding, “Cross-layer optimization for video streaming over wireless multimedia sensor networks”, ICCASM International Conference on Computer Application and System Modeling, China, 2010. [3] A. Politis, I. Mavridis and A. Manitsaris, Enhancing multimedia traffic performance in IEEE 802.11e networks, in Proc. of ICWMC 10, Valencia, Spain, pp. 125-130, September 2010. [4] M. H. Yaghmaee and D. Adjeroh, “A new priority based congestion control protocol for wireless multimedia sensor networks”, Morgantown. [5] Naceen Chilamkurti, Sherali Zeadally,”Wireless multimedia delivery over 802.11 e with cross-layer optimization techniques”, Multimedia Tools April 2010, pp. 189. [6] A. M. Jama, S. Issa and O. O. Khalifa, Performance evaluation of MPEG-4 video transmission over IEEE 802.11e, IJCNS, Vol. 2, No. 5, pp. 11-15, May 2010. [7] C.-H. Lin, C. -K Shieh, An adaptive mapping algorithm for MPEG4 video transmission over IEEE 802.11e WLAN, Telecommun Syst, 2009, pp.223-234. [8] A. Lazaris, P. Koutsakis and M. Paterakis, A new model for video traffic originating from multiplexed MPEG-4 videoconference streams, ELSEVIERSs PEVA, Vol. 65, No. 1, pp. 51-70, 2008. [9] A. Politis, I. Kotini and A. Manitsaris, Streaming video timing analysis in wireless ad-hoc networks, in Proc. ISCC 08, Marrakech, Morocco, pp. 7-12, July 2008. [10] A. Andreadis and R. Zambon, QoS enhancement for multimedia traffics with dynamic TXOPlimit in IEEE 802.11e, in Proc. of PIMRC 07, Athens, Greece, pp. 16-22, 2007. [11] A. Ksentini, A. Nafaa, A. Gueroui and M. Naimi, ETXOP: A resource allocation protocol for QoS-sensitive services provisioning in 802.11 networks, ELSEVIERs PEVA, Vol. 64, No. 5, pp. 419-443, June 2007. [12] A. Ksentini, M. Naimi and A. Gueroui, Toward an improvement of H.264 video transmission over IEEE 802.11e through a cross-layer architecture, IEEE Commun. Mag., Vol. 44, No. 1, pp.107-114, January 2006. [13] I. F. Akyildiz and K. R. C. Tommaso Melodia, “A survey on wireless multimedia sensor networks”, Atlanta, October 2006. [14] J. Majkowski and F. C. Palacio, Dynamic TXOP configuration for QoS enhancement in IEEE 802.11e wireless LAN, in Proc. of SoftCOM 06, Dubrovnic, Croatia, pp. 66-70, 2006. [15] IEEE Std. 802.11e, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Amendment 8: Medium Access Control (MAC) Quality of Service Enhancements, 2005. [16] Amendment 8, Medium Access Control (MAC) Quality of Service (QoS) Enhancements, IEEE Std 802.11e, Jul. 2005. [17] IEEE 802.11e/D4.0, Draft Supplement to Part 11: Wireless Medium Access Control (MAC) and physical layer (PHY) specifications: Medium Access Control (MAC) Enhancements for Quality of Service (QoS), November 2002. [18] Sunghyun Choi, Javier del Prado, Atul Garg, Maarten Hoeben, Stefan Mangold, Sai Shankar, and Menzo Wentink, Multiple Frame Exchanges during EDCF TXOP, IEEE 802.11- 01/566r3, January 2002. [19] IEEE 802.11 WG, Draft Supplement to STANDARD FOR Telecommunications and Information Exchange Between Systems - LAN/MAN Specific Requirements - Part 11: Wireless Medium Access Control (MAC) and physical layer (PHY) specifications: Medium
  • 13. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME 115 Access Control (MAC) Enhancements for Quality of Service (QoS), IEEE 802.11e/D2.0, Nov. 2001. [20] International Organization for Standardization (1999). Overview of the MPEG-4 standard. [21] http://www.tkn.tu-berlin.de/menue/softhardwarecomponents/software/experimentalcode/ evalvid -- a video quality evaluation tool--set/ [22] Politis, Anastasios, Ioannis Mavridis, Athanasios Manitsaris, and Constantinos Hilas. "X- EDCA: A cross-layer MAC-centric mechanism for efficient multimedia transmission in congested IEEE 802.11e infrastructure networks", 2011 7th International Wireless Communications and Mobile Computing Conference, 2011. [23] B. Tavli, K. Bicakci, R. Zilan, and J. Barcelo-Ordinas, “A survey of visual sensor network platforms" Multimedia Tools and Applications, vol. 60, pp. 689-726, Oct. 2012. [24] V. Bapuji, R. Naveen Kumar, Dr. A. Govardhan and Prof. S.S.V.N. Sarma, “Maximizing Lifespan of Mobile Ad Hoc Networks with QoS Provision Routing Protocol”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 150 - 156, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [25] Jayashree Agrakhed, G. S. Biradar and V. D. Mytri, “Optimal QoS Routing with Prioritized Region Scheduling Over WMSN”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 289 - 304, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [26] Sowmya B J, Mohan Kumar S and Jagadeesha S N, “Video Streaming Using Wireless Multi- Hop in Android Phones”, International journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 482 - 492, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [27] Dr. Suresh Kumar D S and Mahesh D.S, “Idle Node Real Time Power Saving MAC Layer Oriented Radom Routing in WSN”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 470 - 477, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [28] Basavaraj S. Mathapati, Siddarama. R. Patil and V. D. Mytri, “Power Control with Energy Efficient and Reliable Routing MAC Protocol for Wireless Sensor Networks”, International journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 223 - 231, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.