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The adoption of the Voice over Internet Protocol (VoIP) system is growing due to several factors, including its meagre rate and the numerous contours that can be joined with VoIP systems. However, the wasteful utilisation of the computer network is an inevitable problem that limits the rapid growth of VoIP systems. The essential explanation behind this wasteful utilisation of the computer network bandwidth (BW) is the considerable preamble length of the VoIP packet. In this study, we invent a technique that addresses the considerable preamble length of the VoIP packet. The designed technique is known as the manikin voice frame (MVF). The primary idea of the MVF technique is to utilise the VoIP packet preamble tuples that are not essential to the voice calls, particularly client-to-client calls (voice calls between only two users). Specifically, these tuples will be utilised for reserving the data of the VoIP packet. In certain instances, this will make the VoIP packet data manikin or even make it empty. The performance assessment of the introduced MVF technique demonstrated that the utilisation of the computer network BW has enhanced by 33%. Along these lines, the MVF technique indicates potential progress in resolving the inefficient usage of the computer network BW.

Old telecommunication systems are gradually being replaced by a new system that works over IP networks, which is known as voice over internet protocol (VoIP). VoIP has several merits (e.g., very cheap call rate), which make it increasingly popular in the telecommunication world. However, VoIP faces numerous impediments that decelerate its promotion. One of the top impediments is the wasted bandwidth caused by VoIP systems. Numerous methods have been proposed to handle this impediment, including packet aggregation methods. This paper proposes a novel aggregation method, called packet aggregation and carrier header (PA-CH), to reduce the amount of the large bandwidth caused by VoIP. As the name suggests, PA-CH saves in bandwidth by aggregating the packets in a header and using the redundant fields in the packet header to carry a portion of the packet voice data. The performance of the introduced PA-CH method was investigated based on three main metrics, namely, link capacity, allocated bandwidth reduction, and voice data shortening. Simulation results indicate that the proposed PA-CH method outperforms the comparison methods in three factors. For instance, the proposed method’s allocated bandwidth reduction ratio reaches 51% when the number of calls running concurrently reaches 100. Therefore, the proposed PA-CH method achieves its goal of reducing the wasted bandwidth caused by VoIP.

Traffic modeling is crucial for designing and evaluating active queue management (AQM) methods used in network routers to control congestion. The Bernoulli process (BP), commonly used to model the traffic, falls short in capturing the burstiness of Internet traffic. Besides, the Markov Modulated Bernoulli Process (MMBP) with multiple states and varying probabilities allows the determination of each state's load independently but does not set specific overall traffic loads. This limitation hinders the establishment of a baseline for evaluating AQM methods. To address these issues, this paper introduces an enhanced traffic modeling approach using the stationary distribution of the Markov Modulated Bernoulli Process (MMBP-SD). This new model calculates the stationary distribution to match the required traffic load while varying its burstiness, enabling a fair comparison with the Bernoulli process of a predefined traffic load and facilitating the assessment of AQM behaviors. The proposed approach was tested under various traffic loads and evaluated using the burstiness factor (BF) and the maximum burstiness duration (MBD). The results showed that the MMBP-SD improved the BF by 6.2% and the MBD by 118% compared to the BP. Evaluating Random Early Detection (RED) was conducted using MMBP-SD and based on delay, loss, and packet dropping. This evaluation revealed that the RED performance in terms of packet loss degrades when using a 4-state MMBP-SD (e.g., packet loss increased by 28.5%) as RED maintains the same dropping rate as in the single-state model, highlighting a limitation of the RED method. [ABSTRACT FROM AUTHOR]

IP telephony have played an essential role during the COVID 19 pandemic lockdown. One of the issues that lower the service level of the IP telephony solutions is the inefficient bandwidth exploitation. This paper proposes a Smallerize/Zeroize (SmlZr) method to enhance bandwidth exploitation. The SmlZr method is explicitly designed for the P2P IP telephony calls over IPv6 networks. The essence concept of the proposed method is to use the unnecessary fields in the header to keep the voice media of the packet. Doing so leads to smallerize or zeroize the packet payload and, thus, enhance the bandwidth exploitation. The SmlZr method has outperformed the RTP method for all the comparison parameters. For instance, the SmlZr method shrinks the bandwidth by 25% compared to the RTP protocol. Bandwidth saving is helpful for P2P IP telephony calls because it alleviates the traffic load. Thus, improve the call capacity boosts the call clarity.

Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.

Voice over IP (VoIP) wastes a valuable amount of bandwidth because of its large packet header size compared to its small packet payload. The main objective of this paper is to reduce the amount of this wasted bandwidth, by proposing a new packets coalescence method, called Payload Shrinking and Packets Coalesce (PS-PC). The proposed PS-PC method reduces the amount of the wasted bandwidth by i) coalesces a group of VoIP packets in one header instead of a separate header to each packet and ii) shrinks the VoIP packet payload to a smaller one based on a certain algorithm. The proposed PS-PC method is deployed at the sender side VoIP gateway that represents an exit point to a myriad number of simultaneous VoIP calls. The performance evaluation showed better bandwidth usage when deploying the proposed PS-PC method with ITTP protocol in comparison to the traditional ITTP protocol without the PS-PC method.

Currently, the multiprotocol label switching (MPLS) standard is extremely prevalent. By exploiting the features provided by MPLS technology, a range of services, including IP telephony, have enhanced their overall performance. However, due to the size of the packet header, the IP telephony service consumes a significant portion of the MPLS network's available bandwidth. For instance, in IP telephony over MPLS networks, the packet header might account for as much as 80% of lost time and bandwidth. Designers working on IP telephony are making substantial efforts to address this issue. This study contributes to current efforts by proposing a novel approach called Tel-MPLS, which involves IP telephony over MPLS. TelMPLS approach uses the superfluous fields in the IP telephony packet's header to retain the packet data, therefore lowering or zeroing the IP telephony packet's payload. Tel-MPLS is an approach that significantly reduces the bandwidth of IP telephony MPLS networks. According to the findings, the Tel-MPLS approach is capable of reducing the amount of bandwidth that is lost by 12% when using the G.729 codec. [ABSTRACT FROM AUTHOR]

Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The proposed AAQM method adapts to single and multiclass traffic models. Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods, including Random Early Detection (RED), BLUE, Effective RED (ERED), Fuzzy RED (FRED), Fuzzy Gentle RED (FGRED), and Fuzzy BLUE (FBLUE). The proposed and compared methods achieved similar results with low or moderate traffic load. However, under high traffic load, the proposed AAQM method achieved the best rate of zero loss, similar to BLUE, compared to 0.01 for RED, 0.27 for ERED, 0.04 for FRED, 0.12 for FGRED, and 0.44 for FBLUE. For throughput, the proposed AAQM method achieved the highest rate of 0.54, surpassing the BLUE method's throughput of 0.43. For delay, the proposed AAQM method achieved the second-best delay of 28.51, while the BLUE method achieved the best delay of 13.18; however, the BLUE results are insufficient because of the low throughput. Consequently, the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay. [ABSTRACT FROM AUTHOR]

In this era of vast online healthcare information, patients often find it challenging to choose the most appropriate doctors from a myriad of options available on different communities. The sheer volume of choices can be overwhelming, underscoring the importance of personalized doctor recommendations. Unfortunately, many existing communities rely on generic doctor recommendations through a global ranking system, potentially neglecting the unique needs and preferences of individual patients. To address these challenges, this study introduces a novel multicriteria user-item trust-enhanced collaborative filtering (MCUITeCF) approach, specifically designed to facilitate patients in locating doctors who match their unique preferences. This proposed approach capitalizes on multi-criteria ratings and integrates user-item trust relationships, aiming to enhance the quality of recommendations, while tackling the common issues of data sparsity and the cold-start problem. An examination of the proposed approach, using a realworld healthcare multi-criteria dataset, the RateMDs dataset, reveals its effectiveness in overcoming challenges associated with data sparsity and cold-start problems. The results demonstrate improved prediction accuracy and coverage compared to benchmark approaches, namely MC user-based CF, MC item-based CF, MC semantic-based CF, MC trust-based CF, and trust-semantic enhanced MC CF. Specifically, the results indicate that the MCUITeCF approach improves the average MAE by 66% compared to all of the benchmark approaches when tested on the RateMDs dataset. When dealing with data sparsity, the MCUITeCF approach improves the average MAE by 41% and prediction coverage by 23% compared to all of the benchmark approaches. In scenarios involving cold-start items, the MCUITeCF outperformed specific benchmark methods such as MC item-based CF, MC semantic-based CF, and Trust-Semantic enhanced MC CF, registering a 21% drop in average MAE and a 28% rise in prediction coverage. Similarly, for cold-start user situations, MCUITeCF excelled by decreasing the average MAE by 29% and a substantial increase in prediction coverage by 20% compared to MC user-based CF, MC trust-based CF, and trust-semantic enhanced MC CF benchmark approaches. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a rapid explosion of online information sources about restaurants, and the selection of an appropriate restaurant has become a tedious and timeconsuming task. A number of online platforms allow users to share their experiences by rating restaurants based on more than one criterion, such as food, service, and value. For online users who do not have enough information about suitable restaurants, ratings can be decisive factors when choosing a restaurant. Thus, personalized systems such as recommender systems are needed to infer the preferences of each user and then satisfy those preferences. Specifically, multi-criteria recommender systems can utilize the multi-criteria ratings of users to learn their preferences and suggest the most suitable restaurants for them to explore. Accordingly, this paper proposes an effective multi-criteria recommender algorithm for personalized restaurant recommendations. The proposed Hybrid User-Item based Multi-Criteria Collaborative Filtering algorithm exploits users' and items' implicit similarities to eliminate the sparseness of rating information. The experimental results based on three real-word datasets demonstrated the validity of the proposed algorithm concerning prediction accuracy, ranking performance, and prediction coverage, specifically, when dealing with extremely sparse datasets, in relation to other baseline CF-based recommendation algorithms. [ABSTRACT FROM AUTHOR]

IPv6 protocol is the future of IP networks due to its large IP address capacity. One of the key consequences of this large capacity is that the IP protocol header has enlarged from 20-byte in IPv4 to 40-byte in IPv6. This will consume a considerable share of the bandwidth (BW) for VoIP applications, which produce small packets in tens of bytes only. To handle this issue, we have introduced an efficient technique to use the superfluous fields in the VoIP packet header, including the IPv6 header, to hold the voice data of the packet. This introduced technique is called the Eliminator technique because it shortens or eliminates the packet payload. The Eliminator technique has been analyzed against the typical technique of transferring the VoIP data. The BW utilization of the two techniques has been compared with different codecs namely G.726, G.728, and G.723.1 codecs. The analysis showed that the BW wasting has alleviated by 16.4%, 19.1%, and 25% with these codecs respectively. This BW utilization improvement will indirectly boost the quality of the VoIP calls, especially in the wireless environments. Accordingly, the Eliminator technique is a viable solution for alleviating the wasted BW when running VoIP in IPv6 networks. [ABSTRACT FROM AUTHOR]

The widespread and desirable features of IP and IEEE 802.11 networks have made these technologies a suitable medium for carrying voice over IP (VoIP). However, a bandwidth (BW) exploitation obstacle emerges when 802.11 networks are used to carry VoIP traffic. This BW exploitation obstacle is caused by the large 80-byte preamble size of the VoIP packet and a waiting time of 765 μs for each layer 2 VoIP frame. As a solution, IEEE 802.11n was consequently designed with a built-in layer 2 frame aggregation feature, but the adverse impact on the VoIP performance still needed to be addressed. Subsequent VoIP research has proposed numerous enhancements to the built-in IEEE 802.11n aggregation techniques. In this study, we analyse and propose enhancements to the BW exploitation and VoIP performance over IEEE 802.11n. We also recommend new techniques to enhance VoIP performance via IEEE 802.11n frame aggregation implementations. Furthermore, we contribute guidelines for creating a robust, suitable, and reliable aggregation technique for VoIP over the 802.11n standard. [ABSTRACT FROM AUTHOR]

Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes an effective hybrid content-based collaborative filtering recommendation approach. The proposed approach will support project stakeholders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories. The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recommendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems. [ABSTRACT FROM AUTHOR]

Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics. [ABSTRACT FROM AUTHOR]