Distributed Quality-Lifetime Maximization in Wireless Video Sensor Networks

Exclusive benefits gained by using the image and video sensors in Wireless Sensor Networks (WSNs), make the Wireless Video Sensor Networks (WVSNs), design and development of their structures, a trending topic. The main challenges of WSNs which are more critical in WVSNs are throughput and end-to-end delay. The radio transceivers recently used in WSN motes can work on different channels with very low switching time. Therefore, many multichannel protocols are proposed to improve the efficiency and throughput and decrease the packet drop rate of the network. Proposed algorithms rarely provide a particular solution to the end-to-end problem. Such delays are necessary parameters in real-time applications. In this paper a real-time multichannel protocol called RTMCH is suggested to address the end-to-end delay of a stream in WVSN. A flow-based channel assignment strategy is used for this purpose. Orthogonal channels are assigned to each flows based on many-to-one data-flow to minimize the contention and collision between different flows. Transmission power is a parameter that can be controlled to achieve desired link quality and to control link delay. Channel assignment problem aligns to a constrained optimization problem to make the specified end-to-end delay of each flow. A channel assignment and real-time packet forwarding scheme are then presented. Simulation results based on realistic channel and radio model shows that the RTMCH can efficiently use multiple channels and transmission power to meet specified end-to-end delay. The results also show better performance for RTMCH over a recent real-time protocol and basic multichannel schemes.

Wireless sensor networks (WSN) have become the rising stars of technology. Every object in this world tends to be sensorly developped, monitored and controlled. Monitoring an area of interest for security reasons for millitary applications, catastrophic natural events, ... gives each captured frame a huge importance to be able to achieve this surveillance system. Thus, Wireless Video Sensor Networks (WVSN) represents the leading technology to implement this kind of surveillance systems. A WVSN consists of three different layers: The video-sensor node, the coordinator and the sink. A video sureveillance system can have hundreds or thousands of video-sensor nodes. Hence, several challenges exist in such a densly deployed system. The leading challenge is for sure the energy consumption problem for capturing, processing and transmitting several images on the network, but it is not the only challenge. Data transmitted on the network from several sensor nodes to a coordinator must be securized. Thus, the emergence of the security challenge in WVSN. In this paper, on the sensor-node level, for data reduction, a new algorithm has been proposed. This algorithm adapts the frame rate and reduce the number of images sent from the sensor node to the coordinator. This algorithm is compared to our most recent algorithm in Salim et al. (24). For the security challenge, the one-round algorithm from Noura et al. (20) is adapted to our approach and scenario. This approach is validated by experimentations using Cpp for OpenCV on Raspberry Pi 3 and by comparing it to other previous, existing approaches.

Energy consumption is of main concern in the field of Wireless Video Sensor Networks (WVSNs) where energy resources are limited, consisting only in the battery of the sensor nodes that determines their lifetime. In this paper, we propose an empirical parametric model to predict the energy consumption of a High Efficiency Video Coding (HEVC) based video encoder in its intra-only mode, used in the context of the next generation WVSNs. Such model is of great interest to adapt the waste of energy of the encoding phase to the remaining energy budget of the node, while meeting the required video quality. The proposed model predicts the energy consumption, considering the adopted Quantization Parameter (QP) and the Frame Rate parameter (FR). A Raspberry Pi 2 card based video sensor node is used for modelling and validation, considering different configurations and spatial resolutions. The obtained results demonstrate that the proposed model describes well the occurred energy dissipation during the video encoding phase, with an average prediction error of 4.5%.

Wireless video sensor networks (WVSNs) have been envisioned for a wide range of important applications, including battlefield intelligence, security monitoring, emergency response, and environmental tracking. Compared to traditional communication system, the WVSN operates under a set of unique resource constraints, including limitations with respect to energy supply, on-board computational capability, and transmission bandwidth. The objective of this paper is to study the resource utilization behavior of a wireless video sensor and analyze its performance under the resource constraints. More specifically, we develop an analytic power-rate-distortion (P-R-D) model to characterize the inherent relationship between the power consumption of a video encoder and its rate-distortion performance. Based on the P-R-D analysis and a simplified model for wireless transmission power, we study the optimum power allocation between video encoding and wireless transmission and introduce a measure called achievable minimum distortion to quantify the distortion under a total power constraint. We consider two scenarios in wireless video sensing, small-delay wireless video monitoring and large-delay wireless video surveillance, and analyze the performance limit of the wireless video sensor in each scenario. The analysis and results obtained in this paper provide an important guideline for practical wireless video sensor design.

Recent technological advances in microelectronics and nano-systems technologies have made it feasible to equip wireless sensor nodes with small low-cost cameras to capture and transmit video. Wireless video sensor networks are gaining popularity due to numerous potential applications such as video surveillance, environmental and habitat monitoring, and so on. However, due to the limited battery available in wireless video sensor nodes, provisioning of QoS in such a network is a challenging task. We provide a survey on the major issues related to QoS provisioning in wireless video sensor networks and possible solution approaches. A dynamic power management framework is proposed for a wireless video sensor node to improve energy saving performance so that the lifetime of the sensor node can be increased. This framework considers the video traffic arrival process in the sensor node, the sleep and wakeup processes in the camera and wireless transceiver electronics, the queue status, and the wireless channel condition. Performance analysis results show that the proposed mechanism can achieve considerable energy saving in a sensor node while providing a target level of QoS performance.

This paper reviews the energy efficiency of several popular block-matching motion estimation algorithms that can be used in wireless video sensor network applications. Full search motion estimation provides the best image quality but requires high computing power. Therefore, only fast search algorithms are considered for deployment in wireless video sensor networks, which generally operate in remote, battery-constrained areas. However, the image quality of fast search algorithms also needs to be considered in the comparison. In this paper, those block-matching algorithms are compared using two criteria: 1) computing cost/energy consumption, and 2) image quality. The purpose of this review is to select the most energy efficient algorithm that retains image quality and can be applied to wireless video sensor network video compression applications.

Wireless Video Sensor Networks are foreseen to be of a paramount importance in realising a wide spectrum of applications, mainly surveillance, target tracking and environment monitoring. In this paper, we propose a combination of an improved clustering algorithm and Directed Diffusion (DD), a well–known data–centric routing paradigm in sensor networks. On one hand, clustering allows to save bandwidth required by rich and intensive data of video applications. On the other hand, the network lifetime is prolonged by implementing an energy–aware load–balancing feature through modifying Passive Clustering (PC) rules for building/maintaining its topology. We performed extensive computer simulations and show in this paper that our solution outperforms original DD as well as when it is combined with PC with energy considerations but without load balancing.

Most multi-object detection and tracking techniques suffer from the well-known "multi-object occlusion" problem. The abundant nodes of wireless video sensor networks (WVSNs) can be utilized to solve the problem, and the video nodes in WVSN have limited calculation capability and energy. In order to achieve effective multi-object tracking using WVSN, the main contributions of our proposed method are that: (1) the limits of field of view (FOV) of every video nodes are utilized to establish the consistent labeling of the objects in different views. (2) Mobile Agent is employed to communicate among network nodes, so the objects are assigned correct labels after multi-object occlusion. The performance of the approach has been demonstrated on real-world and the experimental results show that the proposed method is effective for resolving multi-object occlusions and meets the requirement of WVSN.

Distributed video coding (DVC) is an emerging video coding paradigm designed to allow low-complexity compression by resource-constrained video sensor nodes in wireless video sensor networks (WVSN). Several DVC architectures have been proposed in literature but there are hardly any security mechanisms designed to validate the integrity and authenticity of the decoded video. In this paper, we propose a novel, low-complexity, blind, and imperceptible video watermarking scheme based on DVC for WVSNs. The watermark information is embedded into key frames under selected GOP (Group of Pictures) configurations. Considering the information-intensive yet resource-constrained WVSN environment, the proposed scheme aims to limit the impact of watermark embedding complexity by hiding the secret information using non-zero quantized AC coefficients. We implemented the proposed watermarking scheme for a DVC based codec and evaluated its processing complexity (encoding/decoding). We also evaluated the average normalized correlation between the embedded and extracted watermarks for different video sequences by varying the redundancy in embedding.

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi-channel wireless multihop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems - flow control; next-hop routing; rate allocation and scheduling; power control; and channel allocation - and finally solved by a low-complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.

Conventional wireless sensor networks rely mostly on simple scalar data (such as temperature or humidity) and specialize in single-purpose applications. Taking a fundamental departure, in this article we motivate information-rich wireless video sensor networks that emulate the compound eyes found in certain arthropods. Although constrained by scarce resources, sensor nodes can only serve extremely low-resolution video streams; the availability of vast amount of such streams due to deployment redundance can suffice for the need of information hungry applications. Unfortunately, the unique characteristics of wireless video sensor networks will introduce novel uncertainty-driven challenges in the information-intensive and yet resource-constrained environment. Correspondingly, we describe key research problems in the areas of networking, security, sensor design, and video-data analysis

The availability of low cost networked wireless devices and video cameras is enabling wireless video sensor networks (WVSNs), which can be used in scenarios such as healthcare, agriculture, smart cities, intelligent transportation systems, and surveillance. These scenarios typically require that each node sends a video stream to a server located in the cloud. The IEEE 802.11 is considered a suitable technology for transmitting video wirelessly, as it supports high data rates. However, when using a multi-hop topology to extend the IEEE 802.11 coverage, the IEEE 802.11-based WVSNs suffer from three problems: low network capacity, throughput unfairness, and energy inefficiency. To overcome these problems, we propose a holistic solution, named Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling (GREENNESS). GREENNESS combines a node polling mechanism with the use of out-of-band signaling over a low power radio to signal when a video sensor should switch ON and OFF its IEEE 802.11 interface, thus saving energy. The results obtained for random network topologies show that GREENNESS can achieve energy savings up to 92%, and improve network capacity and throughput fairness when compared to state of the art CSMA/CA-based WVSN solutions.

Network lifetime optimization for wireless video sensor networks with network coding/ARQ hybrid adaptive error-control scheme

Wireless video sensor networks (WVSNs) are opening the door for many applications, such as industrial surveillance, environmental tracking, border security, and infrastructure health monitoring. In WVSN, energy conservation is very essential because: 1) sensors are usually battery-operated and 2) each sensor node needs to compress the video prior to transmission, which consumes more power than conventional wireless sensor networks. In this paper, we study the problem of minimizing the total power consumption in a cluster-based WVSN, leveraging cross-layer design to optimize the encoding power, the transmission power, and the source rate at each sensor node. To realize this problem, we devise a resource optimization framework, which takes into account the video signal distortion due to compression, in addition to packet loss in the wireless channel while trying to allocate network resources among multiple video sensors. Leveraging duality theory, we design a proximal minimization algorithm that is capable of achieving the optimal allocation of network resources, source rates, and encoding and communication powers, while providing application-level quality of service represented by video distortion. The algorithm is extended for sensor nodes with hybrid energy sources, leveraging energy harvesting to minimize the aggregate power, while addressing the tradeoff between renewable versus grid energy sources.

Low-complexity distributed multi-view video coding for wireless video sensor networks based on compressive sensing theory