RTMCH: real-time multichannel MAC for wireless video sensor networks

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.

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.

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.

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.

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

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.

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%.

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.

Owing to the availability of low-cost and low-power CMOS cameras, Wireless Video Sensor Networks (WVSN) has recently become a reality. However video encoding is still a costly process for energy and capacity constrained sensor nodes and its optimal joint control with the communication protocols has a direct impact on the network lifetime. In this paper we propose a distributed quality-lifetime control algorithm where quality is simply measured by the visual signal quality. In order to formulate the quality-lifetime problem, we consider the Power-Rate-Distortion (P-R-D) model of the video encoder together with the rate control, medium access and routing functions of the underlying communication protocol and formulate the problem as a Generalized Network Utility Maximization (GNUM) problem. Then we construct a distributed solution based on duality and proximal point methods with necessary convergence analysis. Simulation results support that optimal quality-lifetime control is possible through the proposed distributed algorithm, where the desired point of operation is simply adjusted by the sink via a configuration parameter.

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.

The spatial correlation among the images retrieved from distributed video sensors leads to considerable data redundancy, thus resulting in significant performance degradation in energy efficiency and QoS satisfaction. In this paper, a correlation-aware QoS routing algorithm (CAQR) is proposed to efficiently deliver visual information under QoS constraints by exploiting the correlation among video sensors. Firstly, a correlation-aware differential coding scheme is designed to reduce the amount of traffic generated by correlated video sensors. Then, a correlation-aware load balancing scheme is proposed to prevent network congestion by splitting the correlated flows that cannot be reduced to different paths. Finally, these correlation-aware schemes are integrated into an optimization QoS routing framework with an objective to minimize energy consumption subject to QoS constraints. Simulation results show that the proposed algorithm achieves efficient delivery of visual information under QoS constraints in wireless video sensor networks.

Lately Video Sensor Networks (VSN) are increasingly being used in the context of smart cities, smart homes, for environment monitoring, surveillance, etc. In such system, the trade-off between Quality of Service (QoS) and energy consumption is always a big issue. As the wireless transmission part plays the dominant role in power consumption, many researches propose energy saving schemes based on the adjustment of duty cycle by adaptively switching between wake-up/sleep state of nodes. However, the main drawback of this method is that it affects streaming quality in terms of throughput and delay. Therefore, one of the most important challenges when designing an energy-aware VSN is to keep the balance between energy consumption and video delivery quality. This paper proposes an Enhanced scheme for Adaptive Multimedia Delivery (eAMD) that dynamically adjusts the wake-up/sleep duration of video sensor nodes based on the node remaining battery levels and network performance. A Markov Decision Process (MDP)-based framework is used to formulate the problem and an innovative algorithm based on Q-Learning is proposed to find the optimal policy for video sensor nodes. Using both a systematic and algorithmic approach, our proposed system architecture and algorithms hold the potential to improve the trade-off between video streaming quality and energy efficiency in comparison with other state-of-the-art adaptive video based algorithms.

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.

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

Existing protocols for medium access in wireless sensor networks opt for staggered wakeup scheduling among clusters of nodes. However intra-cluster contention and interference has remained an unaddressed issue. In this work we propose PELLMAC: power efficient and low latency MAC for wireless sensor networks, a schema that addresses node scheduling for channel access. By limiting contention between adjacent branches of the data gathering tree, we are able to reduce the latency of the network, as well as optimize energy utilization. Simulation results, in accordance with our claims, show that PELLMAC enhances performance in energy savings, latency and delivery ratio