Search Results (36)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable. Full article

Bufferbloat is one of the leading causes of high data transmission latency and jitter on the Internet, which severely impacts the performance of low-latency interactive applications such as online streaming, cloud-based gaming/applications, Internet of Things (IoT) applications, voice over IP (VoIP), real-time video conferencing, and so forth. There is currently a pressing need for developing Transmission Control Protocol (TCP) congestion control algorithms and bottleneck queue management schemes that can collaboratively control/reduce end-to-end latency, thus ensuring optimal quality of service (QoS) and quality of experience (QoE) for users. This paper introduces a novel solution by experimentally integrate the low latency, low loss, and scalable throughput (L4S) architecture (specified by the IETF in RFC 9330) in FreeBSD framework with the asynchronous advantage actor-critic (A3C) reinforcement learning algorithm. The first phase involves incorporating a modified dual-queue coupled active queue management (AQM) system for L4S into the FreeBSD networking stack, enhancing queue management and mitigating latency and packet loss. The second phase employs A3C to adjust and fine-tune the system performance dynamically. Finally, we evaluate the proposed solution’s effectiveness through comprehensive experiments, comparing it with traditional AQM-based systems. This paper contributes to the advancement of machine learning (ML) for transport protocol research in the field. The experimental implementation and results presented in this paper are made available through our GitHub repositories. Full article

In today’s digital world, no one and nothing is safe from potential cyberattacks. There is also no 100% protection from such attacks. Therefore, it is advisable to carry out various studies related to the effects of the different cyberattacks on the performance of the specific devices under attack. In this work, a study was carried out to determine how individual TCP DoS attacks affect the parameters of VoIP (Voice over IP) voice and video streams. For the purpose of this work, a model of a simple IP network has been created using the GNS3 IP network-modeling platform. The VoIP platform used was Asterisk Free PBX. Tools from Kali Linux were used to implement the individual TCP DoS attacks; IP-network-monitoring tools and round-trip-delay-measurement tools were also used. The proposed study is applicable to multiple VoIP platforms wherein voice and video traffic are passed/processed by the VoIP server. From the obtained results, it was found that Asterisk Free PBX is very well secured against TCP DoS attacks, which do not affect the platform performance or the parameters of the voice and video streams. The values of the observed parameters, such as jitter, packet loss, round-trip delay, etc., are very far from the maximum allowable values. We also observed a low load on the CPU and RAM of the system during the whole study. Full article

The sustainability of Indigenous Socioecological Systems (ISES) largely depends on well-crafted policy regulations. In particular, Indigenous traditional food systems (ITFS) are an essential component of ISES that provide a variety of culturally accepted, healthy foods while also playing an important role in cultural, spiritual, and economic value to the Indigenous people (IP). Thus, sustainably managing these traditional natural resources must be a priority. As custodians of much of the world’s ecological system, IP have, for generations, exhibited sustainable lifestyles in governing these systems. However, Indigenous perspectives and voices have not been properly reflected in the ISES sustainability discourse, and few comparative case studies have addressed this issue. This study contributes to fill this research gap using a desktop research method based on the Political Ecological Theoretical Framework (PETF) to examine how existing regulatory policies may affect the resilience and sustainability of ISES-ITFS, especially in relation to growing environmental and climatic pressures. Two Indigenous communities, the Karen in Thailand and different Indigenous groups in the Republic of Sakha (Yakutia) in Russia, are examined as case studies. Our study provides crucial insight that should help the development of robust policy interventions that integrate Indigenous concerns into policies and regulations, emphasizing self-determination, cultural preservation, and land rights. The findings emphasize the necessity for comprehensive legal frameworks prioritizing Indigenous involvement and concerns in climate and sustainability policy implementations. The ultimate goal is to foster meaningful dialogues between policymakers and IP in navigating the climate and sustainability challenges of our time. Full article

With the increase in intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals’ personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas-charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smartphones or application platforms. Full article

With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman’s rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114. Full article

Voice over Internet Protocol (VoIP) is a technology that enables voice communication to be transmitted over the Internet, transforming communication in both personal and business contexts by offering several benefits such as cost savings and integration with other communication systems. However, VoIP attacks are a growing concern for organizations that rely on this technology for communication. Spam over Internet Telephony (SPIT) is a type of VoIP attack that involves unwanted calls or messages, which can be both annoying and pose security risks to users. Detecting SPIT can be challenging since it is often delivered from anonymous VoIP accounts or spoofed phone numbers. This paper suggests an anomaly detection model that utilizes a deep convolutional autoencoder to identify SPIT attacks. The model is trained on a dataset of normal traffic and then encodes new traffic into a lower-dimensional latent representation. If the network traffic varies significantly from the encoded normal traffic, the model flags it as anomalous. Additionally, the model was tested on two datasets and achieved F1 scores of 99.32% and 99.56%. Furthermore, the proposed model was compared to several traditional anomaly detection approaches and it outperformed them on both datasets. Full article

In recent years, there has been an expansion in the development of simulators that use virtual reality (VR) as a learning tool. In surgery where robots are used, VR serves as a revolutionary technology to help medical doctors train in using these robotic systems and accumulate knowledge without risk. This article presents a study in which VR is used to create a simulator designed for robotically assisted single-uniport surgery. The control of the surgical robotic system is achieved using voice commands for laparoscopic camera positioning and via a user interface developed using the Visual Studio program that connects a wristband equipped with sensors attached to the user’s hand for the manipulation of the active instruments. The software consists of the user interface and the VR application via the TCP/IP communication protocol. To study the evolution of the performance of this virtual system, 15 people were involved in the experimental evaluation of the VR simulator built for the robotic surgical system, having to complete a medically relevant task. The experimental data validated the initial solution, which will be further developed. Full article

Institutions have been adopting work/study-from-home programs since the pandemic began. They primarily utilise Voice over Internet Protocol (VoIP) software to perform online meetings. This research introduces a new method to enhance VoIP calls experience using deep learning. In this paper, integration between two existing techniques, Speaker Separation and Speaker Identification (SSI), is performed using deep learning methods with effective results as introduced by state-of-the-art research. This integration is applied to VoIP system application. The voice signal is introduced to the speaker separation and identification system to be separated; then, the “main speaker voice” is identified and verified rather than any other human or non-human voices around the main speaker. Then, only this main speaker voice is sent over IP to continue the call process. Currently, the online call system depends on noise cancellation and call quality enhancement. However, this does not address multiple human voices over the call. Filters used in the call process only remove the noise and the interference (de-noising speech) from the speech signal. The presented system is tested with up to four mixed human voices. This system separates only the main speaker voice and processes it prior to the transmission over VoIP call. This paper illustrates the algorithm technologies integration using DNN, and voice signal processing advantages and challenges, in addition to the importance of computing power for real-time applications. Full article

In this research paper, the spatial distributions of five different services—Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail—are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments. Full article

Voice over Internet Protocol (VoIP) and its underlying Session Initiation Protocol (SIP) are widely deployed technologies since they provide an efficient and fast means of both voice and data communication over a single network. However, in spite of their advantages, they also have their security threats due to the inherent vulnerabilities in the underlying Internet Protocol (IP) that can potentially be exploited by hackers. This study introduces a novel defense mechanism to effectively combat advanced attacks that exploit vulnerabilities identified in some less-known features of SIP. The SIP-DRDoS (SIP-based distributed reflection denial of service) attack, which can survive the existing security systems, is an advanced attack that can be performed on an SIP network through the multiplication of legitimate traffic. In this study, we propose a novel defense mechanism that consists of statistics, inspection, and action modules to mitigate the SIP-DRDoS attack. We implement the SIP-DRDoS attack by utilizing our SIP-based audit and attack software in our VoIP/SIP security lab environment that simulates an enterprise-grade SIP network. We then utilize our SIP-based defense tool to realize our novel defense mechanism against the SIP-DRDoS attack. Our experimental results prove that our defense approach can do a deep packet analysis for SIP traffic, detect SIP flood attacks, and mitigate them by dropping attack packets. While the SIP-DRDoS attack with around 1 Gbps of traffic dramatically escalates the CPU (central processing unit) usage of the SIP server by up to $74\%$, our defense mechanism effectively reduces it down to $17\%$ within 6 min after the attack is initiated. Our approach represents a significant advancement over the existing defense mechanisms and demonstrates the potential to effectively protect VoIP systems against SIP-based DRDoS attacks. Full article

Voice over IP is quickly becoming the industry standard voice communication service. While using an IP-based method of communication has many advantages, it also comes with a new set of challenges; voice networks are now accessible to a multitude of internet-based attackers from anywhere in the world. One of the most prevalent threats to a VoIP network are Denial-of-Service attacks, which consume network bandwidth to congest or disable the communication service. This paper looks at the current state of research into the mitigation of these attacks against VoIP networks, to see if the mechanisms in place are enough. A new framework is proposed titled the “Call Me Maybe” framework, combining elements of latency monitoring with dynamic protocol switching to mitigate DoS attacks against VoIP systems. Research conducted around routing VoIP over TCP rather than UDP is integrated into the proposed design, along with a latency monitoring mechanism to detect when the service is under attack. Data gathered from a Cisco Packet Tracer simulation was used to evaluate the effectiveness of the solution. The gathered results have shown that there is a statistically significant improvement in the response times of voice traffic when using the “Call Me Maybe” framework in a network experiencing a DoS attack. The research and findings therefore aim to provide a contribution to the enhancement of the security of VoIP and future IP-based voice communication systems. Full article

A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the best MGW vehicle to aggregate vehicle traffic and reduce LTE load in HetVNETs when the Wi-Fi APs are unavailable for offloading. The selection process utilizes extra network overhead and complexity due to the frequent formation of clusters in this highly dynamic environment. In this study, we proposed a non-cluster adaptive QoS-aware gateway selection (AQAGS) scheme that autonomously picks a limited number of vehicles to act as LTE gateways based on the LTE network’s load status and vehicular ad hoc network (VANET) application’s QoS requirements. The present AQAGS scheme focuses on highway scenarios. The proposed scheme was evaluated using simulation of Urban mobility (SUMO) and network simulator version 2 (NS2) simulators and benchmarked with the clustered and non-clustered schemes. A comparison was made based on the end-to-end delay, throughput, control packet overhead (CPO), and packet delivery ratio (PDR) performance metrics over Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP) applications. Using VoIP, the AQAGS scheme achieved a 26.7% higher PDR compared with the other schemes. Full article

Speech coding is an essential technology for digital cellular communications, voice over IP, and video conferencing systems. For more than 25 years, the main approach to speech coding for these applications has been block-based analysis-by-synthesis linear predictive coding. An alternative approach that has been less successful is sample-by-sample tree coding of speech. We reformulate this latter approach as a multistage reinforcement learning problem with L step lookahead that incorporates exploration and exploitation to adapt model parameters and to control the speech analysis/synthesis process on a sample-by-sample basis. The minimization of the spectrally shaped reconstruction error to finite depth manages complexity and serves as an effective stand in for the overall subjective evaluation of reconstructed speech quality and intelligibility. Different control policies that attempt to persistently excite the system states and that encourage exploration are studied and evaluated. The resulting methods produce reconstructed speech quality competitive with the most popular speech codec utilized today. This new reinforcement learning formulation provides new insights and opens up new directions for system design and performance improvement. Full article

The phonation variation in Shanghainese is influenced by both phonemic phonation contrast and global prosodic context. This study investigated the phonetic realization of checked and unchecked syllables at four different prosodic positions (sandhi-medial, sandhi-final, phrase-final, and IP-final). By analyzing both acoustic and articulatory voice measures, we achieved a better understanding of the nature of checkedness contrast and prosodic boundaries: (1) Different phonetic correlates are associated with the two laryngeal functions: The checkedness contrast is mostly distinguished by the relative degree of glottal constriction, but the prosodic boundaries are mostly associated with periodicity and noise measures. (2) The checkedness contrast is well maintained in all prosodic contexts, suggesting that the controls for the local checkedness contrast are rather independent of global prosody. Full article