A Survey of multimedia streaming in wireless sensor networks: progress, issues and design challenges

Advancements in Complementary Metal Oxide Semiconductor (CMOS) technology have enabled Wireless Sensor Networks (WSN) to gather, process and transport multimedia (MM) data as well and not just limited to handling ordinary scalar data anymore. This new generation of WSN type is called Wireless Multimedia Sensor Networks (WMSNs). Better and yet relatively cheaper sensors that are able to sense both scalar data and multimedia data with more advanced functionalities such as being able to handle rather intense computations easily have sprung up. In this paper, the applications, architectures, challenges and issues faced in the design of WMSNs are explored. Security and privacy issues, over all requirements, proposed and implemented solutions so far, some of the successful achievements and other related works in the field are also highlighted. Open research areas are pointed out and a few solution suggestions to the still persistent problems are made, which, to the best of my knowledge, so far have not been explored yet

Coverage and Energy Efficiency Optimization for Randomly Deployed Multi-Tier Wireless Multimedia Sensor Networks

In this study, a novel multi-tier framework is proposed for randomly deployed WMSNs. Low cost directional Passive Infrared Sensors (PIR sensors) are randomly deployed across a Region of Interest (RoI), which are activated according to the  Differential Evolution (DE) algorithm proposed for coverage optimization. The proposed DE and the Genetic Algorithms are applied to optimize the coverage maximization using minimum sensors. Results obtained using the two approaches are tested and compared. Only the scalar sensors that are yielded by the coverage optimization process are kept active throughout the network lifetime while the multimedia sensors are kept in silent. When an event is detected by a scalar sensor, the corresponding multimedia sensor(s), in whose effective coverage field of view (FoV) that the target falls, is then activated to capture the event (target point/scene). The analysis of the network total energy expenditure and a comparison of the proposed framework to current approaches and frameworks is made. Simulation results show that the proposed architecture achieves a remarkable network lifetime prolongation while extending the coverage area

A local-holistic graph-based descriptor for facial recognition

Face recognition remains critical and up-to-date due to its undeniable contribution to security. Many descriptors, the most vital figures used for face discrimination, have been proposed and continue to be done. This article presents a novel and highly discriminative identifier that can maintain high recognition performance, even under high noise, varying illumination, and expression exposure. By evolving the image into a graph, the feature set is extracted from the resulting graph rather than making inferences directly on the image pixels as done conventionally. The adjacency matrix is created at the outset by considering the pixels’ adjacencies and their intensity values. Subsequently, the weighted-directed graph having vertices and edges denoting the pixels and adjacencies between them is formed. Moreover, the weights of the edges state the intensity differences between the adjacent pixels. Ultimately, information extraction is performed, which indicates the importance of each vertex in the graphic, expresses the importance of the pixels in the entire image, and forms the feature set of the face image. As evidenced by the extensive simulations performed, the proposed graphic-based identifier shows remarkable and competitive performance regarding recognition accuracy, even under extreme conditions such as high noise, variable expression, and illumination compared with the state-of-the-art face recognition methods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

A machine learning-based framework for predicting game server load

Server load prediction can be utilized for load-balancing and load-sharing in distributed systems. The use of machine learning (ML) algorithms for load estimation in distributed system applications can increase the availability and performance of servers. Hence, a number of machine learning algorithms have been applied thus far for server load estimation. This study focuses on increasing the performance of game servers by accurately predicting the workload of game servers in short, medium and long term prediction situations. While doing this, various machine learning techniques have been applied and the algorithms that give the best results are presented. In terms of implementation, companies using their servers and data centers can try to increase their level of satisfaction by using these algorithms. A prediction model is developed and the estimation performances of a number of fundamental ML methods i.e., Naive Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), Support Vector Machine (SVM), Fast Large Margin (FLM), Convolutional Neural Network CNN are analyzed. The data used during the training stage is obtained by listening to the TCP/IP packet traffic and the real-data is extracted by performing an extensive analysis of the total transferred-data that includes also the payload. In the analysis phase, the goodput is considered in order to reveal exact resource requirements. Comprehensive simulations are performed under various conditions for high accuracy performance analysis. Experimental results indicate that the proposed ML-based prediction shows promising performance in terms of load prediction when compared to the common approaches present in the literature

Reversible Logic-Based Hexel Value Differencing—A Spatial Domain Steganography Method for Hexagonal Image Processing

The field of steganography has witnessed considerable advancements in square-pixel-based image processing (SIP). However, the application of steganography in Hexel (Hexagonal pixel)-based Image Processing (HIP) is still underexplored. This study introduces a pioneering spatial steganography method called the Reversible Logic-Based Hexel Value Differencing (RLBHVD) method in the HIP domain. Our approach draws inspiration from Pixel-Value-Differencing (PVD), a SIP fundamental spatial-domain (S-D) steganography method. Initially, the image is transformed into the HIP domain using the custom software infrastructure developed for this project. Due to the absence of commercial equipment capable of producing HIP-domain images, traditional digital imaging systems are employed with their sensor components, analog-to-digital conversion units, and square-pixel-based displays. Once the image is converted, it is partitioned into standardized heptads, each comprising seven hexels. Simultaneously, the secret message is segmented for embedding into the hexels within each heptad. Unlike SIP-domain PVD, which embeds segments into independent pixel pairs, our method performs iterative embedding within each heptad. Additionally, we leverage Feynman gates, a core element of reversible logic, to achieve retrieval of both the cover image and the secret message. Unlike PVD in SIP, our approach enables reversibility in the recovery process. Experimental results demonstrate that our proposed method, RLBHVD, outperforms its SIP counterpart, PVD, by achieving a low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and significant similarity between the stego-image and cover image histograms. These findings highlight the efficacy and superiority of our HIP-based steganography approach in comparison to existing SIP methods

Localized power-aware routing with an energy-efficient pipelined wakeup schedule for wireless sensor networks

Depending on the evolutions in electronics, wireless sensor networks (WSNs) have been very popular in many areas of human life such as health, industry, and military. This popularity has drawn the attention of many researchers toward WSNs. WSNs are especially favorable in conditions in which it is physically difficult and dangerous for human beings to gather information. Therefore, the lifetime of those networks must be as long as possible, since it would also be infeasible to replace the energy-depleted sensors with new ones, as they may be deployed in such geographical areas that are difficult and dangerous for human beings to enter. In this paper, we present a localized energy-aware routing method, LEERA-MS, and an alternative LEERA-MS-TH method, used in cooperation with an energy-efficient sleep-wakeup schedule, which is included with the pipelining mechanism that we previously proposed. Employing multiple sinks improves performance by providing a fair distribution of the load. Simulation results show that this routing method, applied on a multiple sink topology and when employed together with the pipelined sleep-wakeup schedule, provides 40% longer lifetime for WSNs

Delay efficient STEM by pipelining

Wireless sensor networks are comprised of energy constraint, battery powered small devices that sense the environment and transmit the data to the sink in order to take action according to data. Since the sensors are small energy constraint devices energy consumption is the main problem for wireless sensor networks. Energy spent during data communication is much more than spent during in-sensor computing. Most of the effort is spent on designing protocols in order to conserve energy. This paper proposes an improved version of energy efficient MAC protocol STEM by including pipelining mechanism. Results show that the proposed method overperform the original version of STEM by sustaining less delay. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor

Improved exploiting modification direction steganography for hexagonal image processing

Steganography has made significant advances in the Square-pixel-based Image Processing (SIP) domain, but to our knowledge, no work has yet been done in Hexel (Hexagonal Pixel)-based Image Processing (HIP). This paper presents a HIP-domain data hiding method that exploits and improves the SIP-domain Exploiting Modification Direction (EMD) embedding scheme. The proposed method, Hexagonal EMD (HexEMD), utilizes a HIP-domain cover image's hexagonal nature and infrastructure to embed the secret message. In standard digital imaging systems, the sensor portion that converts photonic energy into an analog electrical signal and all the subunits that digitize, process, and display this signal are based on square pixel logic, so there is currently no commercial equipment available to produce HIP-domain images. Thus, the image is first transformed into the HIP domain in software using the infrastructure developed in the project. Then the HIP-domain image is partitioned into non-overlapping heptads of the standard size, each containing seven hexels. Rather than embedding segments to the independent pixel pairs as done in SIP-domain EMD, we do the embedding iteratively in each heptad. Experimental results show that the HexEMD outperforms its SIP equivalent, EMD, by improving embedding capacity and achieving low visual quality distortion. © 2022 The Author