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This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to co-design a low-complexity video encoder based on compressed sensing and a rate-adaptive streaming protocol for wireless video transmission. The proposed rate control scheme is designed with t...
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... the parameters and can be estimated from empirical rate-distortion curves via regression techniques. In this case they were determined through linear least square [29] curve fitting. Three curves showing the derivation of and are shown in Fig. 2. The packet error rate is shown in (6) to be made up of two different components. The first, , represents the packets lost due to channel errors. The second, , represents losses due to packets arriving at the receiver after the playout delay deadline. We will examine each of these ...
Citations
... Sensor nodes in WMSNs have limited computing capability and energy resources without the aid of any established infrastructures, so many studies are conducted considering these limitations for various applications [5][6][7][8][9][10][11]. Multimedia data has a large volume which is different from traditional wireless sensor networks that transmit the simple numerical values, so important distinctions exist which greatly affects how security is achieved. ...
Given its importance, the problem of secure data aggregation in wireless multimedia sensor networks has attracted great attention in the literature. Wireless multimedia sensor networks present some challenges that are common to wireless sensor networks, such as the existence of limited resources, like sensors memory, energy consumption, and CPU performance. Many methods have been proposed to attempt to solve the problem. However, the existing data aggregations do not take into account the redundancy of the multimedia data. In order to improve the energy efficiency for multimedia data, we propose a similarity model and power model. The proposal scheme divides multimedia data into multiple different pieces, and transmits the effective pieces to the selected sensor nodes. Through theoretical justifications and empirical studies, we demonstrate that the proposed scheme achieves substantially superior performances over conventional methods in terms of energy efficiency and data transmission under the resource-constrained wireless multimedia sensor networks.
... EAQoS [9] consistently performs well with respect to real-time and energy metrics, which considers the delay and jitter together in the network. C-DMRC [10] is designed with the objectives to maximize the received video quality at the receiver and to prevent network congestion while maintaining fairness between multiple video transmissions. To balance energy consumption and reliability, ERTP [11] dynamically controls the maximum number of retransmissions at each sensor node. ...
Wireless multimedia sensor networks (WMSNs) are more and more wildly used for a variety of applications nowadays. The quality-of-service (QoS), as a measure of system performance, is very important and attracts many researchers. The limited bandwidth should be adjusted for the packets with different priorities precisely. Even more, when the WMSNs are used for environment monitoring, besides the real-time video playing, the monitoring systems also need to record and playback the video after some time. So the necessary and proper retransmission should be added to the real-time video transmission, without affecting the normal data streams. To emphasize the problem, the paper provides a flexible and reliable traffic control protocol. According to the packet priorities, sensor nodes in one collision area can adjust their sending states adaptively. Variable retransmission tactics are provided for the packets with different priorities. The simulations show that the bandwidth adjustment can protect the packets with high priority effectively, and the performance of the retransmission is well positioned to meet the requirement of the multimedia applications.
... Wireless Multimedia Sensor Networks [1] ...
... We assume that our UAV camera-drones are equipped with positioning system (GPS or indoor positioning system), a camera, storage memory and a wireless transceiver to send the filmed images to a base station and to permit communication and cooperation with other UAV camera-drones. These drones are capable of identifying and localizing a target by some radio frequency identification tag applied on it or by using a sensor network [9], [11], [13] placed at the sides of the field and capable of locating the target and communicating to the drones. We assume that drones are able to recognize when a player is in possession of the ball because player and ball tags will be overlapping. ...
We introduce and formulate the Sport Event Filming (SEF) problem. We are interested in controlling the movement of a set of camera drones filming the event by moving over the field where the event takes place. The objective of the formulation is to maximize the satisfaction of event viewers and/or minimize the distance traveled by the camera-drones. We propose to model the static version of the SEF problem as a Vehicle Routing Problem with Soft Time Window (VRP-STW), where the whole sequence of actions in the event are known a priori. Since this assumption is unrealistic for real sport event, we propose two families of heuristics to solve the dynamic version of the problem, where the camera-drones do not have any knowledge of the input sequence and move in reaction to the movements of the protagonists of the event. The first family (Nearest Neighbor) is based on a technique used in robotics systems, whereas the second family (Ball Movement Interception) is designed based on specific characteristics of the SEF problem. We present extensive simulation results for both families in terms of average viewer satisfaction and traveled distance for the camera-drones, when several parameters vary.
... No No No Yes No STCP [56] Yes Yes No Yes No RCRT [57] No Yes No Yes Yes TRCCIT [58] Yes Yes No Yes Yes MDTSN [59] Yes Yes No No No DMRC [60] Yes Yes No No Yes ...
Wireless Sensor Networks (WSNs) have enjoyed dramatic developments over the last decade. The availability of CMOS cameras and microphones enlarged the scope of WSNs paving the way to the development of Wireless Multimedia Sensor Networks (WMSN). Among the envisaged WMSN applications, Real-time Multimedia Monitoring constitutes one of the most promising. However, the resource requirements of these applications place difficult challenges in terms of network lifetime and scalability. This paper starts by identifying the main characteristics and requirements of Real-time Multimedia Monitoring applications and then highlights key research directions that may help to overcome those challenges.
... However, the key problem of enabling real-time quality-aware video streaming in large-scale multihop wireless networks of embedded devices is still open and largely unexplored. There are two key shortcomings in systems based on sending predictively encoded video (e.g., MPEG-4 1 A preliminary shorter version of this paper [1] appeared in the Proceedings of IEEE SECON 2010, Boston, MA, June 2010. This paper is based upon work supported in part by the National Science Foundation under grant CNS1117121 and by the Office of Naval Research under grant N00014-11-1-0848. ...
This article presents the design of a networked system for joint compression, rate control and error correction of video over wireless multimedia sensor networks (WMSNs) based on the theory of compressed sensing. First, compressed sensing based video encoding for transmission over WMSNs is studied. It is shown that compressed sensing can be used to overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among different videos while maximizing the received video quality. It is shown that the rate of compressed sensed video can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. Finally, the entire system is evaluated through simulation and software-defined testbed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality.
... With respect to image and video transmission, [12] designed a video encoder based on CS and a streaming protocol for wireless video transmission. To exploit temporal redundancy, the difference frame of the I frame and the target frame is compressive sensed in [12]. ...
... With respect to image and video transmission, [12] designed a video encoder based on CS and a streaming protocol for wireless video transmission. To exploit temporal redundancy, the difference frame of the I frame and the target frame is compressive sensed in [12]. This means the original video frame is used as the reference frame in the encoder side, while the decoder uses the recovered image as the reference frame. ...
... When the measurements are lost, we just eliminate the corresponding rows in the measurement matrix and estimated the quantization error ∆ again in the decoder side. For SVCCS, we compare our proposed GOP structure with that described in [12], i.e., the whole compressive sensed I frame is served as a reference frame. Assume that the DCT coefficients are received correctly.Fig. ...
Channel coding such as Reed-Solomon (RS) and convolutional codes has been widely used to protect video transmission in wireless networks. However, this type of channel coding can effectively correct error bits only if the error rate is smaller than a given threshold; when the bit error rate is underestimated, the effectiveness of channel coding drops dramatically and so does the decoded video quality. In this paper, we propose a low-complex, scalable video coding architecture based on compressive sensing (SVCCS) for wireless unicast and multicast transmissions. SVCCS achieves good scalability, error resilience and coding efficiency. SVCCS encoded bitstream is divided into base and enhancement layer. The layered structure provides quality and temporal scalability. While in the enhancement layer, the CS measurements provide fine granular quality scalability. In addition, we incorporate state-of-the-art technologies of compressive sensing to improve the coding efficiency. Experimental results show that SVCCS is more effective and efficient for wireless videocast than the existing solutions.
In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.
Wireless sensor networks (WSN) is a novel data centered self-organizing network that can be used in acquiring and processing information. With the limited energy of sensor nodes and the increasing requirement of secure data transmission, how to save nodes energy as well as realize a transmission of sensitive information in WSN effectively is becoming an interesting research problem recently. In this paper, we present a novel scheme to transmit sensitive information in the pattern of energy efficient way by utilizing compressive sensing (CS) which is an emerging technology in recent years. In this method, sensor nodes need not to do complex computing but simple linear operations which can save the energy extensively, and thus extend the life time of the wireless sensor networks. Theory analysis and detail simulation results demonstrate the effective of our method, and the sensitive information can be accurately reconstructed even when the communication channel is loss.
