Wireless Video Sensor Networks Research Articles

Online Data Transmission Reduction Scheme for Energy Conservation in Wireless Video Sensor Networks

Online Data Transmission Reduction Scheme for Energy Conservation in Wireless Video Sensor Networks

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed randomly over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. The reduction of the number of active VSNs decreases energy consumption but at the same time reduces the area coverage and connectivity. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming-based approach (PA_1). But PA_1 is not tractable for large instances as the problem is NP-Hard. Hence, a heuristic approach (PA_2) based on an advanced genetic algorithm is also proposed in the present work for obtaining a near-optimal solution. Simulation studies are carried out to compare the performance of PA_1 and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among the three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1 and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates a suboptimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment.

Retraction Note: QoS and QoE Enhanced Resource Allocation for Wireless Video Sensor Networks Using Hybrid Optimization Algorithm

Retraction Note: QoS and QoE Enhanced Resource Allocation for Wireless Video Sensor Networks Using Hybrid Optimization Algorithm

Multi-objective optimization for coverage aware energy consumption in wireless 3D video sensor network

This paper addresses the problem of minimizing energy consumption as well as maximizing the coverage area for 3D video sensor nodes deployed over a 2D target area in a wireless video sensor network. Reducing the number of active video sensor nodes helps decrease energy consumption, but decrease the coverage area as well as connectivity in the network. The problem is solved initially by a multi-objective integer linear programming based approach (APP_7) which provides an optimal solution but leads to intractability for a large-sized network due to the non-deterministic polynomial time-hard complexity of the problem. This fact motivates us to design a non-dominated sorting genetic algorithm-II based heuristic (APP_8), which generates a near-optimal solution and that is substantiated by comparing the simulation results of APP_7 with APP_8, and two existing greedy heuristics (EX_1, EX_11) for small-sized problem. Simulation experiments are conducted also to compare the results of APP_8 with EX_1 and EX_11 for large-sized network. Between two heuristics, EX_11/(EX_1) is better in terms of energy consumption/(area coverage). It has been observed that for the large-sized network (i.e. node density 100 on the target area of size 75 × 75 square meters) APP_8 is able to reduce energy consumption by 47.76% from EX_11, whereas area coverage is decreased by 0.51% from EX_1, which confirms the acceptability of APP_8 as the proper heuristic in such a scenario.

Analysis of Smart Grid Using Multimedia Sensor Networks with Effective Resource Allocation

In recent days, for smart grid network updates, video sensors are used where each node needs to be compressed before transmission. This is very much useful for developing countries as in future the smart grid communication process will play a major role in entire society. Therefore, in this article, minimization of power, energy consumption, and delay for wireless video sensor networks have been computed by providing better quality of service at a similar period. Also, the problem of optimizing transmission and delivery rate has been studied. For solving the aforementioned problems, the intuitive migrant algorithm has been implemented for providing better energy consumption. Additionally, modified larvae optimization tool has been integrated for providing better convergence rate, where all the nodes will be compressed at a better rate with necessary quality of service. The simulation results show that by applying algorithms in two folds, and each video sensor node is compressed by satisfying necessary constraints with fast convergence rate.

Progressively refined scheme for wireless video sensor networks

Progressively refined scheme for wireless video sensor networks

Adaptive Rate Block Compressive Sensing Based on Statistical Characteristics Estimation.

In some video compressive sensing (CS) applications, the sparsity of original signals is unknown to the sampling device. The computing power, memory space and power consumption of the sampling device are also limited, which makes it difficult to achieve adaptive rate compressive sensing (ARCS). A new blocked ARCS method for surveillance videos is proposed, which fully considers the limitations mentioned above. By observing the result of CS measurement, the statistical characteristics of the original signal are estimated. The sparsity of the original signal is reasonably estimated by using these statistical characteristics. Therefore, blocks can be divided into more classes with higher accuracy. The proposed method has the advantages of low computational complexity, small memory footprint and low power consumption, which makes it suitable for implementing in applications such as wireless video sensor networks (WVSN) and single pixel cameras (SPC). The experiment results show that the proposed method can well adapt to the change of sparsity, allocate appropriate sampling rate for each block, effectively reduce the sampling rate, and improve the quality of the reconstructed image. Meanwhile, the amount of calculation in the sampling process is much lower, and the sampling speed is obviously accelerated. The overall performance of the proposed method is better than the previous state-of-the-art method.

Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network

Live virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be deployed more widely. To solve the bandwidth and energy challenges, VR video viewport prediction has been proposed as a feasible solution. However, the existing works mainly focuses on the bandwidth usage and prediction accuracy and ignores the resource consumption of the server. In this study, we propose a lightweight neural network-based viewport prediction method for live VR streaming in WVSN to overcome these problems. In particular, we (1) use a compressed channel lightweight network (C-GhostNet) to reduce the parameters of the whole model and (2) use an improved gate recurrent unit module (GRU-ECA) and C-GhostNet to process the video data and head movement data separately to improve the prediction accuracy. To evaluate the performance of our method, we conducted extensive experiments using an open VR user dataset. The experiments results demonstrate that our method achieves significant server resource saving, real-time performance, and high prediction accuracy, while achieving low bandwidth usage and low energy consumption in WVSN, which meets the requirement of live VR streaming.

An optimized deep neural network based DoS attack detection in wireless video sensor network

Due to the technological advancements in sensor and modern devices, the Wireless Multimedia Sensor Network (WNSMs) have gained attraction among various communities. It makes it firm and remarkable achievements in wireless communication, media transfer and digital transmission by adopting portable technologies. In recent decades, in applications such as agriculture and industry the sensor nodes are intended to measure the parameters such as temperature, moisture content and other environmental factors. WNSMs have also facilitates the applications by providing the devices with self-governing access for sending and processing the data associated with suitable audio and video information.Numerous video sensor network researches focus on reducing power consumption and optimizing transmission capacity, but data reliability is the real need. The hindrance involved in the sensor nodes result into various attacks on WMSN especially Denial of Service (DoS) attacks. The core of this attack is to incapacitate proper functioning of the network. Both Attacker and attackers, focus on attacking and preventing the legitimate network nodes from using the network resources. Distributed attack is defined as the network which is being attacked by multiple attackers. This type of attack causes significantly more issues in functionality of the network than attacks involved on a single node. Redemption of the network from the threats of DoS attacks, an enhanced machine learning algorithm has to be presented. For detecting DoS in WMSN an optimized Deep Neural Network algorithm is proposed. The parameters required are chosen from adaptive particle swarm optimization algorithm. The approach’s efficiency to be measured by the ratio of packet transmission, energy consumption, latency, network length, and throughput.

Bio‐inspired Mimosa pudica algorithm for energy‐efficient wireless video sensor networks

SummaryWireless video sensor network (WVSN) has attracted much research interest due to its multi‐faceted potential applications in different fields. An energy‐efficient protocol is fundamental for WVSN since the network has to deal with traffic‐intensive multimedia data, such as images and videos. A few issues come in front for any such setting like energy inefficiency, throughput unfairness, and low network capacity. In many cases, we need to strike a balance between required parameters or aspects, but there is no alternative to ensuring appropriate energy efficiency in the process. Keeping this in mind, this paper proposes a holistic solution named energy‐efficient bio‐inspired Mimosa pudica (EEBIMP) algorithm. EEBIMP for WVSN can reduce the time delay and avoid unnecessary energy consumption due to clustering the network. Our proposed algorithm has been compared with one of the most recent notable solutions, like GREENNESS (Green wiReless vidEo sENsor NEtworks uSing out‐of‐band Signalling), PSO (Particle Swarm Optimization), and CUCKOO search algorithm. The result shows that EEBIMP achieved 95% of energy efficiency and throughput when compared with other algorithms of GREENNESS, PSO, and CUCKOO.

Dynamic Node Cooperation Based on Moving Multi-target Tracking in Double Wireless Video Sensor Network

Dynamic Node Cooperation Based on Moving Multi-target Tracking in Double Wireless Video Sensor Network

JsrNet: A Joint Sampling-Reconstruction Framework for Distributed Compressive Video Sensing.

Huge video data has posed great challenges on computing power and storage space, triggering the emergence of distributed compressive video sensing (DCVS). Hardware-friendly characteristics of this technique have consolidated its position as one of the most powerful architectures in source-limited scenarios, namely, wireless video sensor networks (WVSNs). Recently, deep convolutional neural networks (DCNNs) are successfully applied in DCVS because traditional optimization-based methods are computationally elaborate and hard to meet the requirements of real-time applications. In this paper, we propose a joint sampling–reconstruction framework for DCVS, named “JsrNet”. JsrNet utilizes the whole group of frames as the reference to reconstruct each frame, regardless of key frames and non-key frames, while the existing frameworks only utilize key frames as the reference to reconstruct non-key frames. Moreover, different from the existing frameworks which only focus on exploiting complementary information between frames in joint reconstruction, JsrNet also applies this conception in joint sampling by adopting learnable convolutions to sample multiple frames jointly and simultaneously in an encoder. JsrNet fully exploits spatial–temporal correlation in both sampling and reconstruction, and achieves a competitive performance in both the quality of reconstruction and computational complexity, making it a promising candidate in source-limited, real-time scenarios.

Energy-efficient secured data reduction technique using image difference function in 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.

Machine learning approach for secure communication in wireless video sensor networks against denial‐of‐service attacks

SummaryMobile ad hoc networks (MANETs) own a flexible framework with the absence of a server, where conventional security components fail to compensate the level of MANET security conditions since it is confined to a particular environment, its data transfer potential, and battery and memory constrains. MANET provides a well‐grounded path and an efficiency in communication, but the confidentiality of the trust parameters remains a great challenge since it may be overheard by the impostor. This demands the need of exchanging the encrypted mathematical values. The proposed machine learning security paradigm provides firm and trustworthy network in spite of establishment over additional network platform. The QoS is improved through support vector machine for denial‐of‐service attack. The node has to be clustered to accomplish its respective task. The clustering is done with the help of LEACH protocol, where cluster head and Cluster member are fixed to transfer the data in the network. Low Energy adaptive clustering heirarchy (LEACH) propagates energy to abstain from draining of battery and malignant network. A secure framework is built along with encryption and decoding to protect from denial‐of‐service attack. Acknowledgement‐based flooding attack has been focused with the help of support vector machine algorithm. The messages are encoded in from the source node and coded again during transmission phase to obtain the original message. Defending the traditional methodologies, the proposed work provides excellent QoS when compared and tested with other protocols. The results obtained ensure its efficiency when support vector machine technique is combined with encryption scheme.

On the processing architecture in wireless video sensor networks: node and network level performance evaluation

The architecture of a wireless video sensor network node has an impact on the performance of a wireless sensor network. Due to nature of WVSN applications, the node should be capable of potentially performing image processing algorithms while simultaneously has high networking capabilities. Providing an optimum architecture in which both parameters are addressed is the main challenge in WVSN. This paper reviews suggested architectures for the node and based on that three base architectures are proposed and implemented. The performance of the nodes is then evaluated at node and network level and the results are included in the paper. Based on the results, an optimized architecture is introduced. This architecture separates the node to a network and an application part. This brings the flexibility of implementing the optimized solution for each part, without considering the other. The network part is implemented using a microcontroller. It is capable of adopting operating systems that are widely used in Wireless Sensor Networks. On the other hand, the application part is implemented using FPGA, that provides a power-optimized and stand-alone solution for the node with high computation capabilities.

RETRACTED ARTICLE: Enhanced approach using trust based decision making for secured wireless streaming video sensor networks

The advances in the expanse of image sensors have made it conceivable to make high-resolution picture sensors easily accessible. The amelioration of wireless interactive media sensor networks are found to be greatly increased due to the day to day usage of cameras, microphones and smart devices. A secured multi-hop routing mechanism is addressed in surveillance areas which could be incorporated to the multimedia sensors that are capable of peruse the detected data comprises of recorded images and videos. Also, malevolent sensor hubs could be interjected into the vigilance area in an untrusted environment. In this contemplated venture, a novel lightweight trust decision-making framework is accomplished for QoS clustering to give secure routing in both intercluster and intracluster communication. A quantifiable aberrant trust value is the variable determined by the Cluster Head (CH) for its Cluster Member (CM) inside the cluster. The LEACH (Low Energy Adaptive Clustering Hierarchy) protocol is adopted for group formation and also for the exchange of the trust values among the master nodes, member nodes and Base station. In this way, the correspondence overhead, likelihood of dynamic assaults for example sinkhole and black hole assaults mount by eavesdroppers can be reduced by maintenance of trust values by cluster heads rather than cluster members. Besides, in wireless streaming video sensor networks this approach authorizes us to predict and counteract malicious untrusted and flawed nodes. Simulation results using NS-2 is examined and the suggested trust decision-making model escalates the dependability, elasticity and low memory aloft in comparison with the current trust models adopted for wireless video sensor network security.

Adaption Resizing Communication Buffer to Maximize Lifetime and Reduce Delay for WVSNs

Due to battery-powered wireless video sensor networks (WVSNs) takes on a high video data volume, it becomes a crucial issue to reduce energy consumption to maximize lifetime. At the same time, WVSNs are often applied to important situations for real-time and semi-real-time monitoring, thus ensuring the rapid transmission of these perceived data to sink is another critical issue. In static random-access memory (SRAM) communication buffer-based WVSN node, the radio is turned off during the data buffer-filling period as well as idle period. Because each radio transition contains the additional energy consumed by invalid packet header information and communication connection establishment, and the radio ON/OFF transition incurs extra circuit energy consumption. Therefore, from the perspective of saving energy, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, the large size of SRAM buffer results in more energy consumption because SRAM energy consumption is proportional to the memory size. More importantly, the large ON/OFF transition frequency will lead to a large data transmission delay, which is harmful to applications. In this paper, an adaption resizing communication buffer (ARCB) scheme is proposed to maximize lifetime and reduce delay for WVSNs. In the ARCB scheme, the buffer size takes the minimum energy consumption optimization value in hotspot areas to maximize lifetime and takes a value smaller than the optimized value in non-hotspot areas to reduce delay. Although this consumes more energy for communication, since nodes in the non-hotspot areas have energy surpluses, it will not affect the lifetime. As a result, ARCB scheme can simultaneously increase the lifetime and reduce the delay in WVSNs. The effectiveness of the ARCB scheme is verified by theoretical analysis. The results show that the proposed ARCB scheme reduces the delay by 27.9%, and increases the effective utilization of energy by 24.1% under the condition that its lifetime is no less than previous schemes.

Multi-view video codec using compressive sensing for wireless video sensor networks

In monitoring applications, different views are needed to be captured by multi-view video sensor nodes for understanding the scene clearly. These multi-view sequences have large volume of redundant data which affects the storage, transmission, bandwidth and lifetime of wireless video sensor nodes. A low complex coding technique is required for addressing these issues and for processing multi-view sensor data. Hence, in this paper, a framework on CS-based multi-view video codec using frame approximation technique (CMVC-FAT) is proposed. Quantisation with entropy coding based on frame skipping is adopted for achieving efficient video compression. For better prediction of skipped frame at receiver, a frame approximation technique (FAT) algorithm is proposed. Simulation results reveal that CMVC-FAT framework outperforms the existing method with achievement of 86.5% reduction in time and bits. Also, it shows 83.75% reduction in transmission energy compared with raw frame.

Optimal Resource Allocation for Green and Clustered Video Sensor Networks

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.

RTMCH: real-time multichannel MAC for 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.