Wireless Multimedia Sensor Networks Research Articles

FJLT-FLSH: More Efficient Fly Locality-Sensitive Hashing Algorithm via FJLT for WMSN IoT Search

Wireless multimedia sensor networks (WMSNs) have been widely used in environmental monitoring, intelligent transportation, and other scenarios; however, their data have high-dimensional, large-scale, and multitype properties, as well as other characteristics. It is technically difficult to construct an appropriate index structure and query strategy while we perform a highly accurate search. This paper proposes a novel locality-sensitive hashing (LSH) fast Johnson–Lindenstrauss transform (FJLT)-fly locality-sensitive hashing (FLSH) algorithm for WMSN Internet of Things search. In this method, the projection method of FJLT and the winner-takes-all feature selection strategy in the fruity FLSH are considered. The method provides a new solution for the nearest neighbor search of high-dimensional data. We also discuss the distance-keeping property of our algorithm, and prove theoretically that the method proposed in this paper has better distance keeping performance than the traditional dimensionality reduction method. The experimental results show that the proposed algorithm has better generalization, accuracy of the search results, and time efficiency when using the Drosophila olfactory nerve to simulate the LSH process. This method effectively solves the problem of the approximate neighbor query of high-dimensional big data and can be effectively applied to search application on WMSN system.

An Improvement of Energy Efficient and Extending Life Time of WMSN through Data Fusion Method

This paper presents the implementation of Wireless Multimedia Sensor networks (WMSN) using datafusion methods with aggregation that comprise of huge number of sensor hubs that are sent in some land territory. The motivation behind the system was to detect the earth and record what occurs in a territory it’s sent in. With different prospective applications wireless multimedia sensor network has numerous difficulties. In this method, each node is equipped with multiple sensors (like camera, temperature and humidity). Use of more than one sensor provides additional information about the environmental conditions. The datafusion approach based on the competitive-type hierarchical processing is considered for experimentation. Energy sources are the principle requirement for a wireless sensornode organizer because of restricted batteries power provided to every sensor node hub, so it’s important to join energy consciousness into each phase of the system structure and activity. So as to augment the lifetime of sensor arrangements, the system needs forceful energy streamlining strategies, guaranteeing energy awareness is joined into individual sensor hubs as well as into gatherings of coordinating hubs and into a whole sensor organize. The multiple data fusion process improves the reliability and accuracy of the sensed information and simultaneously saves energy, which was our primary objective. The proposed algorithms were simulated using Matlab. This article gives a datafusion (aggregation) methods to assume significant job for improve the PSNR, error performance and Security of the data which get by whole sensor network system. By classification of grouping (clustering) in WSN this articlepresents the design diagram for information datafusion in multimedia sensor network arrangement.

SASRT: Semantic-Aware Super-Resolution Transmission for Adaptive Video Streaming over Wireless Multimedia Sensor Networks

There are few network resources in wireless multimedia sensor networks (WMSNs). Compressing media data can reduce the reliance of user’s Quality of Experience (QoE) on network resources. Existing video coding software, such as H.264 and H.265, focuses only on spatial and short-term information redundancy. However, video usually contains redundancy over a long period of time. Therefore, compressing video information redundancy with a long period of time without compromising the user experience and adaptive delivery is a challenge in WMSNs. In this paper, a semantic-aware super-resolution transmission for adaptive video streaming system (SASRT) for WMSNs is presented. In the SASRT, some deep learning algorithms are used to extract video semantic information and enrich the video quality. On the multimedia sensor, different bit-rate semantic information and video data are encoded and uploaded to user. Semantic information can also be identified on the user side, further reducing the amount of data that needs to be transferred. However, identifying semantic information on the user side may increase the computational cost of the user side. On the user side, video quality is enriched with super-resolution technologies. The major challenges faced by SASRT include where the semantic information is identified, how to choose the bit rates of semantic and video information, and how network resources should be allocated to video and semantic information. The optimization problem is formulated as a complexity-constrained nonlinear NP-hard problem. Three adaptive strategies and a heuristic algorithm are proposed to solve the optimization problem. Simulation results demonstrate that SASRT can compress video information redundancy with a long period of time effectively and enrich the user experience with limited network resources while simultaneously improving the utilization of these network resources.

محاكاة شبكات الاستشعار اللاسلكية للوسائط المتعددة WMSN

أصبحت شبكات استشعار الوسائط المتعددة اللاسلكية (WMSNs) شائعة على نطاق واسع بسبب توفر أجهزة استشعار متعددة الوسائط سهلة الاستخدام ومنخفضة التكلفة قادرة على استرداد ونقل محتوى الوسائط المتعددة مثل الصوت والصور وتدفق الفيديو. اتبعت محاكاة WMSN التقنيات والأدوات المستخدمة بشكل أساسي بواسطة شبكات الاستشعار اللاسلكية المماثلة (WSN). هذا ، لسوء الحظ ، لم يكن كافيًا لطبيعة البيانات الكثيفة لشبكات WMSN. لذلك ، لا يزال من الصعب توفير أطر محاكاة كافية لشبكات WMSN. تقدم هذه الورقة مسحًا لأطر المحاكاة والأدوات المتاحة لشبكات WMSN. يتم عرض التحديات التي تواجه عملية المحاكاة لأول مرة. بعد ذلك ، يتم تقديم ومراجعة سمات محاكاة WMSN المثالية. كما تم عرض ومناقشة النموذج العام لبيئات محاكاة WMSN. أخيرًا ، يتم تقديم مسح مفصل لأطر وأدوات المحاكاة المتاحة. يتم أيضًا تقديم الأدوات الموروثة من شبكة الاستشعار اللاسلكية (WSN) وتلك المصنوعة خصيصًا لشبكات WMSN.

Wireless multimedia sensor network for rape disease detections

In order to reduce pesticides, it is also necessary to understand how plant diseases originate in the beginning and to be around them all around the world. One of the best ways to achieve this goal is to promote images that are not visible to the world (WMSN) in an agricultural country. However, the sending of a wireless device with little knowledge of the images will add more traffic to traffic, especially electricity. In this pamphlet, we create a diagnostic procedure that is aimed at driving WMSN’s additional resources. From the study of the locally available nodes of the plants, this new technique can make the first choice on the vegetation of the plants, knowing how to send pictures to the control center to continue to explore, to move forward. Look at the Internet. The same method includes the distribution of images using color and shape, and its 2D profile is used as part of the stages. What happens on metal images and lack of food indicates that the correct route is 94.5%.

A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs)

In remote areas, wireless multimedia sensor networks (WMSNs) have limited energy, and the data processing of wildlife monitoring images always suffers from energy consumption limitations. Generally, only part of each wildlife image is valuable. Therefore, the above mentioned issue could be avoided by transmitting the target area. Inspired by this transport strategy, in this paper, we propose an image extraction method with a low computational complexity, which can be adapted to extract the target area (i.e., the animal) and its background area according to the characteristics of the image pixels. Specifically, we first reconstruct a color space model via a CIELUV (LUV) color space framework to extract the color parameters. Next, according to the importance of the Hermite polynomial, a Hermite filter is utilized to extract the texture features, which ensures the accuracy of the split extraction of wildlife images. Then, an adaptive mean-shift algorithm is introduced to cluster texture features and color space information, realizing the extraction of the foreground area in the monitoring image. To verify the performance of the algorithm, a demonstration of the extraction of field-captured wildlife images is presented. Further, we conduct a comparative experiment with N-cuts (N-cuts), the existing aggregating super-pixels (SAS) algorithm, and the histogram contrast saliency detection (HCS) algorithm. A comparison of the results shows that the proposed algorithm for monitoring image target area extraction increased the average pixel accuracy by 11.25%, 5.46%, and 10.39%, respectively; improved the relative limit measurement accuracy by 1.83%, 5.28%, and 12.05%, respectively; and increased the average mean intersection over the union by 7.09%, 14.96%, and 19.14%, respectively.

P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

Reliable wireless multimedia sensor network design: comparison of hybrid metaheuristics and a matheuristic

Network reliability has vital importance for designing wireless multimedia sensor networks (WMSNs). The definition of network reliability for WMSNs differentiates from the traditional communication network types which simultaneously involves node deployment, connectivity and coverage issues. Therefore, in this study, a new information gathering network reliability definition is made for WMSNs. The information gathering network reliability is maximized under a given total budget constraint by including node and terrain characteristics. The model is developed to get surveillance from an enemy zone. Since the reliable WMSN design considering node deployment, connectivity and coverage has NP-hard complexity, new hybrid methods are proposed with hybridization of exact methods with nature-inspired metaheuristics. Five algorithms are generated. Firstly, problem-specific simulated annealing (SA) and genetic algorithm (GA) are developed, then branch and bound (B&B) is embedded into the SA and GA named as hybrid SA (HSA) and hybrid GA (HGA). The B&B method optimizes the orientations of the sensor nodes. Additionally, an HGA-based matheuristic (HGABM) is proposed. In HGABM, a mixed integer linear programming (MILP) network flow-based model is added into the initial population generation procedure of the HGA. The MILP model finds the exact deployment points of the relay nodes. In experimental study, it is noticed that the main time-consuming parts of the algorithms are network reliability calculations. Thence, a parallel Monte Carlo (MC) simulation is developed and the MC runs are made in multiple general purpose graphics processing units (GPGPUs). Full-factorial experimental design and Taguchi design approaches are preferred to tune the parameters, to generate the problem sets and to make the experiments. The experimental study is performed on synthetically generated terrains with different terrain and device-based scenarios. Statistical methods are used to compare the performances of the algorithms. In conclusion, for small-sized sets HGABM and for moderate- and large-sized sets, HGA outperforms the other algorithms. The algorithms are coded in MATLAB and the MILP model is solved with CPLEX.

Mobility aware multi-objective routing in wireless multimedia sensor network

The Quality of Service (QoS) and energy efficiency are the key issues for Future Internet. Targeting multiple objectives for efficient routing is a critical and demanding task in recent applications. Due to increase in mobility applications consideration of mobility parameters for dynamic routing is necessary. The objective of this research paper is to propose the multi-objective approach for dynamic routing in Wireless Multimedia Sensor Network. Extending the adaptive routing for mobility will definitely enhance the capabilities of routing protocols for Wireless Multimedia Sensor Network. In this paper, we propose the adaptive routing protocol which is considering mobility parameter during route establishment. We are using various parameters such as Remaining Energy, Hop count, Link Quality Index and Mobility Factor for path formation. The experimentation results show that mobility parameter extension improves network performance. The technique used in this paper will enrich applications for Internet of things (IoT).

A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks.

Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system’s accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface.

Impact of Variable Packet Length on the Performance of Heterogeneous Multimedia Wireless Sensor Networks

Performance valuation of wireless sensor network (WSN) routing protocols calls for realistic data traffic models because of their application specific nature. Since energy limitations is a cardinal issue for WSNs, network lifetime has become a key performance metric. In the quest towards energy efficiency, data packet length is an essential factor, especially in the multimedia applications. On one hand, a modest packet length brings down the packet loss, nonetheless, it results in dissipation of more energy. Thus, there comes forth a tradeoff in resolving the data packet length, where both low and high bit rates lead to certain energy inefficiency problems. This paper analyses the effect of variable packet length, being majorly ignored by routing protocols, on the network lifetime, throughput and reliability. The presented work is validated using computer simulations.

SAMS: A Seamless and Authorized Multimedia Streaming Framework for WMSN-Based IoMT

An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.

Challenging Issues for Handling Multimedia Data in Wireless Multimedia Sensor Networks

Challenging Issues for Handling Multimedia Data in Wireless Multimedia Sensor Networks

A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks

There are two main challenges in wireless multimedia sensors networks: energy constraints and providing DiffServ. In this paper, a joint flow control, routing, scheduling, and power control scheme based on a Lyapunov optimization framework is proposed to increase network lifetime and scheduling fairness. For an adaptive distribution of transmission opportunities, a differentiated queueing services (DQS) scheme is adopted for maintaining data queues. In the Lyapunov function, different types of queues are normalized for a unified dimension. To prolong network lifetime, control coefficients are designed according to the characteristics of the wireless sensor networks. The power control problem is proved to be a convex optimization problem and two optimal algorithms are discussed. Simulation results show that, compared with existing schemes, the proposed scheme can achieve a better trade-off between QoS performances and network lifetime. The simulation results also show that the scheme utilizing the distributed media access control scheme in scheduling performs best in the transmission of real-time services.

Novel Metaheurist Technique for Attack Detection Technique in Wireless Multimedia Sensor Networks

Novel Metaheurist Technique for Attack Detection Technique in Wireless Multimedia Sensor Networks

Visual and Auditory Data Fusion for Energy-Efficient and Improved Object Recognition in Wireless Multimedia Sensor Networks

Automatic threat classification without human intervention is a popular research topic in wireless multimedia sensor networks (WMSNs) especially within the context of surveillance applications. This paper explores the effect of fusing audio-visual multimedia and scalar data collected by the sensor nodes in a WMSN for the purpose of energy-efficient and accurate object detection and classification. In order to do that, we implemented a wireless multimedia sensor node with video and audio capturing and processing capabilities in addition to traditional/ordinary scalar sensors. The multimedia sensors are kept in sleep mode in order to save energy until they are activated by the scalar sensors which are always active. The object recognition results obtained from video and audio applications are fused to increase the object recognition performance of the sensor node. Final results are forwarded to the sink in text format, and this greatly reduces the size of data transmitted in network. Performance test results of the implemented prototype system show that the fusing audio data with visual data improves automatic object recognition capability of a sensor node significantly. Since auditory data requires less processing power compared to visual data, the overhead of processing the auditory data is not high, and it helps to extend network lifetime of WMSNs.

무선 멀티미디어 센서 네트워크에서 데이터인증을 위한 관심점 검출 방법의 평가

무선 멀티미디어 센서 네트워크에서 데이터인증을 위한 관심점 검출 방법의 평가

A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images.

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

Data fusion-oriented cluster routing protocol for multimedia sensor networks based on the degree of image difference

In recent years, wireless multimedia sensor networks (WMSN) have attracted considerable attentions because of the extensive applications of providing audio-visual information conveniently. However, the traditional routing protocols are not directly applicable to WMSN due to the disadvantages on real-time or energy cost. The routing based on combination of clusters and data fusion provides an effective approach to improve the efficiency of data transmission. Consequently, this paper proposes a novel data fusion-oriented routing protocol, which takes the degree of image difference into consideration when clustering. And meanwhile, the cluster-head is rotated periodically within a cluster, for the purpose of the establishment of data fusion tree which is responsible for the data transmission and redundancy elimination. The simulation results show that the proposal achieves a better performance on energy efficiency, network delay and loading balance, and simultaneously prolongs the network lifetime, compared with other typical protocols.

The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data.

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.