• The proposed SWF tokenization method, dynamically capturing emotional subtleties, noise robustness, and contextual information to robustly preserve speaker identity. • A transformative deep-self-supervised spectrogram transformer back-end, outperforming conventional approaches by effectively addressing their inherent limitations in preserving local features without the need for extensive labeled training data. • Complete elimination of dependency on additional speech enhancement, enabling seamless, efficient, and robust end-to-end learning tailored for real-world deployments. Identifying speakers in noisy and emotional conditions remains a significant challenge due to the distortion of spectral cues. This study proposes the Speech Without Filter (SWF) framework, a novel self-supervised learning paradigm that operates directly on raw spectrograms. Theoretically, this research introduces a progressive tokenization mechanism that acts as a structural inductive bias, mimicking the contracting path of a U-Net to preserve local spectro-temporal continuity. Unlike standard fixed-patch Transformers that often smooth over speaker-specific micro-textures, the SWF architecture integrates denoising and feature extraction into a single stage, challenging the traditional decoupled paradigm of speech enhancement and recognition. Using a sample of 1.58 million pre-training instances, the model was evaluated across English (RAVDESS), Arabic (ESD), and stressful (SUSAS) datasets. Results demonstrate significant improvements, with the SWF model achieving 91.01% accuracy in clean conditions and maintaining 88.5% in high-noise cocktail party environments, outperforming state-of-the-art models like WavLM and HuBERT. These findings suggest that architectural innovation in tokenization is as critical as pre-training scale for robust speech processing.

This article presents a physical layer security (PLS) technique for non-orthogonal multiple access (NOMA), which can provide joint secrecy, fairness, and bit error rate (BER) improvement. The approach employs power hopping (PH), where users’ power coefficients are dynamically varied according to the instantaneous channel conditions. By randomizing both the detection order and the effective power allocation, PH obscures the underlying NOMA structure from a passive eavesdropper (Eve), thereby severely degrading the data detection capability. To enable secure coordination, a precoding-based mechanism is developed for sharing the PH patterns among legitimate users, leveraging the random and independent nature of wireless channels. The BER and secrecy outage probability (SOP) of the proposed framework are analytically characterized and compared to conventional NOMA under various channel and eavesdropping configurations. The obtained analytical results, corroborated by Monte Carlo simulations, confirm the effectiveness of the proposed scheme, where the SOP of legitimate users is never compromised regardless of Eve’s channel conditions, while incurring only negligible additional complexity compared to conventional NOMA. Furthermore, the fairness and BER performance of stronger users are significantly improved through the combined use of PH and optimal power allocation.

This paper presents a novel physical layer security (PLS) scheme for non-orthogonal multiple access (NOMA) systems that jointly enhances secrecy and bit error rate (BER). The proposed approach leverages power hopping (PH), wherein users’ power coefficients are dynamically allocated to obscure both the decoding order and actual power allocation from potential eavesdroppers. This uncertainty significantly reduces the feasibility of successful interception. Analytical expressions for the BER and secrecy outage probability (SOP) are derived under diverse channel conditions and eavesdropping scenarios. The theoretical findings, validated through Monte Carlo simulations, confirm that the proposed scheme robustly preserves the secrecy of legitimate users regardless of the eavesdropper’s channel quality. Moreover, the approach yields substantial improvements in both BER and SOP, particularly for the near user.

Mobile Crowdsensing (MCS) is gaining attention for large-scale sensing that involves three types of entities: task requesters, workers equipped with sensing devices, and the platform that assigns tasks to workers considering their objectives and constraints. However, finding an allocation solution that satisfies the conditions above is NP-hard. A few studies suggested approximate solutions to this problem, focusing on one of the task’s objectives: coverage maximization. Yet, they implement it in a single-task environment or with weak objective consideration, i.e., they consider other objectives, reducing the utility the task will receive. This study proposes a task allocation that focuses only on maximizing the task coverage, where we improved the solution to consider future task coverage possibilities. We consider an opportunistic MCS environment in which sensing has no impact on user trajectories. We assume a one-to-many matching where a task can be assigned to several workers, while a worker can be matched to at most one task. We first formulate the problem mathematically and prove it to be NP-hard. Then, we design three heuristic-based solutions that are more efficient and perform extensive performance evaluations based on a real-world dataset. Each solution improves the data quality and has a maximum execution time of milliseconds.

This article presents a novel framework for integrating low-earth orbit satellites (LEOSs) with terrestrial-wired networks to improve coverage, throughput, and transmission reliability of 6th generation (6G) Internet of things (IoT) networks. The proposed framework utilizes the synergy of non-orthogonal multiple access (NOMA), automatic repeat request (ARQ), and cooperative communications to maximize the data downloaded from a LEOS to multiple terrestrial users. More specifically, we propose a novel offline packets repair and recovery (PRR) technique to reduce the number of dropped packets, where the wired-terrestrial connection is used to enable efficient bidirectional cooperation, to improve the reliability of the received data by reducing the multiuser interference inherent to NOMA. Moreover, by exchanging the acknowledgment messages used with ARQ, efficient Chase combining (CC) is applied to improve the signal to noise ratio (SNR) of the received packets. Extensive Monte Carlo simulation experiments are used to evaluate and quantify the advantages of the proposed system. The results obtained show that the proposed system can repair a significant number of dropped packets, which reduces the packet drop rate and improves the network throughput. In several scenarios, the proposed PRR managed to repair and recover more than 90% of the dropped packets.

Abstract Modern Recommender Systems (RSs) compete to maintain rich user profiles that can accurately reflect user behavior, interests, and service contexts. While benefiting from an online service supported by an RS, user preferences and interests may rapidly change over time. To keep up with the changes from the user perspective, an RS should maintain the making of effective personalization as supported by robust profile construction methods. Building an effective user profile database requires exhaustive data and behavior analysis over extended periods. In this paper, we delve into traditional RS architectures to identify limitations, gaps, and opportunities for improvements in existing user profile mechanisms. To that end, a Global User Profile Framework (GUPF) is proposed towards achieving increased effectiveness. Furthermore, the adoption of the developed framework is exemplified by presenting different potential scenarios. The presented work concludes with the identification of important venues and research directions that are enabled by the proposed GUPF.

Nonorthogonal multiplexing (NOM) is a novel superposition coding scheme that has been recently proposed to improve the throughput of wireless systems. However, restricting the number of multiplexed packets to two limits the throughput improvement of nonorthogonal multiplexing (NOM) to 100% in best-case scenarios. Therefore, this work presents a generalized NOM (GNOM) design with an unlimited number of multiplexed packets. In the multiplexing process, new and retransmitted packets due to arq are combined while considering the impact of channel conditions on the power assigned per packet. The proposed GNOM employs an efficient heuristic algorithm to perform the power assignment and multiplexing decisions. Moreover, the complexity can be controlled by enforcing a limit on the maximum number of multiplexed packets per transmission, making it suitable for iot nodes with diverse computational capabilities and quality of service requirements. The obtained results demonstrate the effectiveness of the proposed scheme, which offers up to 200% throughput improvement at moderate signal to noise ratios (SNRs), and up to 700% at high SNRs. Furthermore, the new scheme can reduce the transmission power consumption by up to 6 dB in the high SNR region.

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonal multiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection (ACD) which requires only the channel amplitude for equalization purposes. In what follows, three different designs for realizing the detection of the proposed NOMA are investigated. One is based on the maximum likelihood (ML) principle, while the other two are based on successive interference cancellation (SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER of the coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that, in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channel phase information. Consequently, the BER using ML and ACD are comparable for various cases of interest. The paper also shows that the SIC detector is just an alternative approach to realize the ML detector, and hence, both detectors provide the same BER performance.

The rise of the Internet has enabled new types of relationships and online domains where trust must be computed rather than earned. However, classic trust and reputation techniques designed to support computational trust among Internet entities do not meet the scalability requirements and the resource constraints typical of Internet of Things (IoT) devices. This paper provides a general framework for managing computational trust where Blockchain technology and reputation systems are used in conjunction. Our framework relies on a reward-penalty scheme to offer a customizable, secure, and scalable trust architecture for IoT devices and the services they access. We implemented and tested a smart contract-based proof-of-concept, carrying out an analysis of its cost and security to verify the practical viability of our solution.

This work presents the symbol error rate (SER) and outage probability analysis of multi-layer unmanned aerial vehicles (UAVs) wireless communications assisted by intelligent reflecting surfaces (IRS). In such systems, the UAVs may experience high jitter, making the estimation and compensation of the end-to-end phase for each propagation path prone to errors. Consequently, the imperfect phase knowledge at the IRS should be considered. The phase error is modeled using the von Mises distribution and the analysis is performed using the Sinusoidal Addition Theorem (SAT) to provide accurate results when the number of reflectors$L\leq 3$, and the Central Limit Theorem (CLT) when$L\geq 4$. The achieved results show that accurate phase estimation is critical for IRS based systems, particularly for a small number of reflecting elements. For example, the SER at 10−3degrades by about 5 dB when the von Mises concentration parameter$\kappa =2$and$L=30$, but the degradation for the same$\kappa $surges to 25 dB when$L=2$. The air-to-air (A2A) channel for each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are compared to the Rician channel. The obtained results reveal that the considered A2A model can be used to accurately represent the A2A channel with Rician fading.

Orthogonal frequency-division multiplexing (OFDM) is one of the most widely adopted modulation schemes because it offers several desirable features, such as spectral efficiency and low complexity implementation. Moreover, it is highly suitable for adaptive implementation. Nevertheless, OFDM does not have any special immunity against multipath fading as compared to single-carrier systems. Therefore, we propose in this article a new OFDM configuration, denoted as OFDM-smart (OFDM-S), where superposition modulation is used to introduce transmit diversity to reduce the bit error rate (BER) while maintaining the spectral efficiency and bit rate fixed. The proposed scheme is based on clustering specific subcarriers that can cooperate by sharing their transmission resources. The cooperation is performed such that all subcarriers transmit their data jointly using all the subcarriers within their cluster. The system performance is derived in terms of BER performance over Nakagami-m frequency-selective fading channels where efficient expressions are presented in terms of a single finite integral, and closed-form expressions are derived for the asymptotic BER. The obtained analytical results corroborated by simulation show that the OFDM-S can provide substantial BER improvement over conventional OFDM, due to the transmit diversity.

BACKGROUND: A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts. RESULTS: To overcome the limitations of methods that rely solely on explicitly mentioned terms in texts to predict protein functions, we propose in this paper an Information Extraction system called PL-PPF. The proposed system employs techniques for predicting the functions of proteins based on their co-occurrences with explicitly and implicitly mentioned biological molecule terms that pertain functional categories in biomedical literature. That is, PL-PPF employs a combination of statistical-based explicit term extraction techniques and logic-based implicit term extraction techniques. The statistical component of PL-PPF predicts some of the functions of a protein by extracting the explicitly mentioned functional terms that directly describe the functions of the protein from the biomedical texts associated with the protein. The logic-based component of PL-PPF predicts additional functions of the protein by inferring the functional terms that co-occur implicitly with the protein in the biomedical texts associated with it. First, the system employs its statistical-based component to extract the explicitly mentioned functional terms. Then, it employs its logic-based component to infer additional functions of the protein. Our hypothesis is that important biological molecule terms pertaining functional categories of proteins are likely to co-occur implicitly with the proteins in biomedical texts. We evaluated PL-PPF experimentally and compared it with five systems. Results revealed better prediction performance. CONCLUSIONS: The experimental results showed that PL-PPF outperformed the other five systems. This is an indication of the effectiveness and practical viability of PL-PPF's combination of explicit and implicit techniques. We also evaluated two versions of PL-PPF: one adopting the complete techniques (i.e., adopting both the implicit and explicit techniques) and the other adopting only the explicit terms co-occurrence extraction techniques (i.e., without the inference rules for predicate logic). The experimental results showed that the complete version outperformed significantly the other version. This is attributed to the effectiveness of the rules of predicate logic to infer functional terms that co-occur implicitly with proteins in biomedical texts. A demo application of PL-PPF can be accessed through the following link: http://ecesrvr.kustar.ac.ae:8080/plppf/.

Electronic text Author Attribution (AA) is a well known stylometry problem that attempts to infer the identity of authors of disputed electronic texts by solely analyzing the texts. This is important for various applications such as forensics and market analysis. However, currently the state of the art in author identification has never been evaluated against Emirati social media electronic texts. This is partly due to the fact that no evaluation dataset exists that is suitable for evaluating author identification methods in the domain of Emirati social media electronic texts. This paper presents the first of such evaluations, along with the release of the Khonji-Iraqi Emirati Tweets author identification evaluation dataset with 30 authors (KIT30). Additionally, novel definitions of grams are introduced, namely compound grams, which demonstrate that decision models that make use of them can achieve higher classification accuracies than the alternative case when classical definitions of grams are followed. The findings also indicate that, when suitable data representation is used, the degradation in the classification accuracy, as the space of suspect authors increases, is not necessarily as sharp as previously reported in the literature. This suggests that AA problem solvers can be significantly more scalable as previously evaluated.

In this paper, a low-complexity discrete adaptive bit loading algorithm is proposed for multicarrier systems with uniform power allocation operating in fading environments under the discontinuous bit rates assumption. The algorithm objective is to maximize the overall throughput of the system while guaranteeing that the average bit error rate (BER) remains below a prescribed threshold. The algorithm uses a signal-to-noise ratio (SNR) threshold to group adjacent subcarriers into slots where it allocates the same number of bits for all subcarriers within the slot. The grouping mechanism reduces the complexity of the bit-loading process with negligible throughput degradation as compared to optimal and near-optimal algorithms. In particular scenarios, the achieved complexity reduction may exceed 70% with throughput penalty that is less than 1% as compared to other well established bit loading algorithms.

This paper presents a blind channel estimation (BCE) technique for orthogonal frequency division multiplexing communications systems. The proposed system is based on modulating particular pairs of subcarriers using amplitude shift keying and phase shift keying, which enables the realization of a decision-directed one-shot BCE (OSBCE), with complexity and accuracy that are comparable to pilot-based channel estimation techniques. The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo simulation under various channel conditions. The obtained results show that the MSE of the proposed OSBCE is comparable to pilot-based estimators, which confirms the efficiency of the proposed OSBCE.

This work presents a new digital communications receiver that requires only partial knowledge of the channel state information (CSI). Generally speaking, the CSI is composed of real and imaginary components, which can be expressed as h = |h| ejθ, where |h| corresponds to the channel attenuation and θ is the phase shift. In the proposed receiver, only |h| is required to detect the transmitted symbols with low probability of error. The proposed receiver can be used to increase the spectral efficiency of most digital communications receivers and/or reduce their error probability.

Existing studies in skeleton‐based action recognition mainly utilise skeletal data taken from a single camera. Since the quality of skeletal tracking of a single camera is noisy and unreliable, however, combining data from multiple cameras can improve the tracking quality and hence increase the recognition accuracy. In this study, the authors propose a method called weighted averaging fusion which merges skeletal data of two or more camera views. The method first evaluates the reliability of a set of corresponding joints based on their distances to the centroid, then computes the weighted average of selected joints, that is, each joint is weighted by the overall reliability of the camera reporting the joint. Such obtained, fused skeletal data are used as the input to the action recognition step. Experiments using various frame‐level features and testing schemes show that more than 10% improvement can be achieved in the action recognition accuracy using these fused skeletal data as compared with the single‐view case.

This paper presents the detector design and symbol error rate (SER) analysis of M-ary amplitude shift keying over multipath fading channels using amplitude-coherent detection (ACD). The optimum detector is derived using the maximum likelihood criterion, and then it is used to derive two efficient low-complexity suboptimal detectors. The probability distribution function of the decision variables and the SER of the two suboptimal detectors are expressed using simple closed-form analytical formulas when single receiving antenna is used. The SER with receiver diversity is obtained using Monte Carlo simulation. The obtained analytical and simulation results reveal that ACD can provide reliable SER as compared with noncoherent detection. Moreover, we present an efficient blind channel estimation algorithm using ACD and hybrid modulation frame structures.

Gathering forensic evidence in distributed or cloud environments poses a number of legal, administrative, and technical challenges even at relatively coarse levels of granularity. For Software-as-a-Service (SaaS) and related Service-Oriented Architectures (SOA), however, the addition of loose binding lending such architectures their important flexibility and adaptability renders even identifying possible loci of evidence problematic. Moreover, even where the existence of evidence is known, its relevance for a given hypothesis may vary. We describe an approach to identify the existence of potential evidence based on a causality model of control flow, and seek to prioritise relevance based on a probabilistic graph model. This allows not only the explicit formulation of hypotheses and derivation of criteria for locating and retrieving evidence to be evaluated by Bayesian belief networks (BBN), but to minimise the otherwise highly problematic complexity of maximum a posteriori (MAP) hypotheses based on service orchestration and choreography semantics.

In a heterogeneous and volatile environment such as the cloud, recording the underlying infrastructure state - snapshot-is an important paradigm for analysis, testing and verification of properties associated with distributed executions. In case of crime or e-discovery investigation, possible execution might require to generate digital forensics evidences. The aim of this research is to generate distributed snapshot and extract evidences in a forensically sound manner. The paper will cover literature review outcomes, preliminary findings, research methodology and work in progress.

Pervasive healthcare systems, enabled by information and communication technology (ICT), can allow the elderly and chronically ill to stay at home while being constantly monitored. This work aims to present a framework for healthcare monitoring systems based on heterogeneous sensors. In addition to video cameras, the system makes use of a variety of heterogeneous sensors that allow adequate monitoring of each patient by fusing the data from the different sensors. The system described in this paper can be deployed into patients' homes sparing the healthcare costs of living in a health facility where constant monitoring by professionals is required and allowing the elder to live in his home for as long as possible. It can also be deployed in a hospital or a silver-care town environment to cut off the number of healthcare personnel needed, improve the monitoring of patients, and set alarms in case of critical situations.

With the development of depth sensors, i.e. Kinect, it is now possible to predict human body poses from a depthmap without any manual labeling. The predicted poses can be used as meaningful features for many applications such as human action recognition. However, existing pose estimation algorithms are not perfect, which can seriously affect the performance of its following applications. In this paper, we propose a novel method to detect erroneous poses. Human poses are captured by Kinect SDK which predicts body joints and connects them with straight lines to represent a pose. We observe depth gradient of pixels located on a body part is consistent when the body part is predicted correctly. With this observation, our algorithm examines depth gradients of pixels on each body part. During the depth gradient processing, our algorithm also considers occlusions. Once a sudden change is detected in depth values on a body part, we check whether the gradient is still consistent excluding the sudden change region. We tested our algorithm on many human activities and our experimental results show that our algorithm acceptably detects erroneous poses in real time.

In cloud computing, instead of building their own network and facilities, companies can allocate resources from network operators, thus reducing hardware investment and maintenance cost. The company's IT infrastructure can be designed as an abstract virtual graph of services to be mapped into the substrate network of the operator. In this paper, we refer to the virtual graph mapping problem as service composition. How to identify and locate the substrate components to map the virtual graph to in an optimal way is the subject of this paper. We propose three algorithms for three different graph topologies: path, star, and tree, whose complexity is O(V3) + O(ERV2) where V is the set of substrate network nodes and ERis the set of virtual links. We also provide simulations to evaluate the performance of our algorithms.

In this paper, we investigate a novel Random-based Fair-Allocation Fuzzy Comprehensive Evaluation- based Strategy with Relay Clustering (RFRC) for dual-hop Single- Carrier Frequency-Domain Equalization (SC-FDE) in cooperative systems with multiple relays and amplify-and- forward (AF) relaying. We address the problem of fair resource allocation for SC-FDE minimum mean square error (MMSE) receivers. In particular, relays are classified into multiple disjoint clusters through the K-means algorithm, where each cluster is identified by a centroid. The centroids of the clusters are directly related to the average amount of resources available to the relays belonging to that cluster, as well as the quality of the relaying' links. The final centroids of the clusters after the convergence of the K-means algorithm, are used to generate contribution factors for each group. Different sub-channels are further associated with uniform random distribution where the thresholds of the uniform random variables are associated with the cluster's contribution factors. RFRC for SC-FDE proves superior SER performance as well as improved diversity gain which is achieved by randomizing the allocation. The employed algorithm further improves the performance by optimizing the number of clusters. Numerical results are provided to corroborate the mathematical modeling.

In this paper, we study the lifetime of a dualhop multi-relay wireless sensor network (WSN) OFDM-based system using an adaptive random relay selection strategy. In this strategy, the relays cooperate according to contribution factors associated with each relay. An analytical study of the system lifetime is presented along with simulation results. The results show that the allocation scheme guarantees fairness in allocating the resources in each relay. The results also show the impact of the number of relays on the lifetime of the WSN.

In this paper, a novel Multi-relay Adaptive Random Selection (MARS) strategy in a dual-hop multi-relay OFDM system is proposed. In this strategy, the relays cooperate according to contribution factors suggested by each relay. The selection of the OFDM sub-channels is based on uniform random distribution where the thresholds of the uniform random variable are associated with the contribution factors. This approach guarantees fairness in allocating the resources in each relay. The approach is compared with static allocation of the OFDM sub-channels. The MARS strategy is tested in a multi relay system over flat and selective Rayleigh fading channels. Simulation results show the superiority of the proposed scheme in improving the OFDM system performance in terms of BER. The performance is achieved by creating a virtual diversity by randomizing the allocation where no channel information is required.

In this paper we present a Stochastic Shortest Path (SSP) formulation of routing in DTN networks where all the nodes are potentially mobile and where only an aggregate mobility information is available. We present a solution to the optimal case and present two heuristics to solve the problem. Extensive simulations assess the performance of the proposed schemes.

The overall progresses in wireless sensor networks are making the assumption that all the sensor nodes participate in the provision of one single service. However, there is a growing need for accommodating multiple services simultaneously within the same network. In spite of the existence of some relevant attempts, long-term work for binding multiple services on the same sensor network remain necessary. The issue of controlling the admission of concurrent tasks at the sensor level is the cornerstone of this work. In this paper, we propose a service admission control (SAC) approach that allows any sensor node to decide about the admission or denial of new sensing tasks. The admission strategy is based on the tasks already accepted, the new task frequency, and the sleep/wake period of the sensor node. The approach also gives hints on how to choose the frequency of the new tasks to increase the probability of being accepted. An investigation of the effect of the duration of wake time on the possible number of supported services is also presented.

In a wireless mesh network (WMN), the traffic is aggregated and forwarded towards the gateways. Strategically placing and connecting the gateways to the wired backbone is critical to the management and efficient operation of a WMN. In this paper, we address the problem of gateways placement, consisting in placing a minimum number of gateways such that quality-of-service (QoS) requirements are satisfied. We propose a polynomial time near-optimal algorithm which recursively computes minimum weighted Dominating Sets (DS), while consistently preserving QoS requirements across iterations. We evaluate the performance of our algorithm using both analysis and simulation, and show that it outperforms other alternative schemes by comparing the number of gateways placed in different scenarios

In this paper we propose a reputation management scheme for partially decentralized peer-to-peer systems. The reputation scheme helps building trust between peers based on their past experiences and feedbacks from other peers. Our system is novel in that it is able to detect not only malicious peers sending inauthentic files but also malicious peers that are lying in their feedbacks. To detect those peers, we introduce the new concept of suspicious transactions. The simulation results show that the proposed scheme is able to effectively detect malicious peers and isolate them from the system, hence reducing the amount of inauthentic uploads and increasing peers' satisfaction.

Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated to upload files by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers must be forced to contribute positively by uploading authentic files instead of malicious ones. In this paper, the contribution behavior of the peer is used as a guideline for service differentiation. The new concept of availability is introduced for partially-decentralized peer-to-peer systems. Both availability and involvement of the peer are used to assess its contribution behavior. Simulation results confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. Simulation results also confirm that based on rational behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the so-called milking phenomenon

Traffic engineering has been widely used to improve network performance while keeping the resource utilization balanced. The existing traffic engineering approaches, however, lead to network upgrades from time to time. While these upgrades might be successfully financed during economic prosperities, it poses a problem when the economic situation deteriorates. We propose in this paper a novel approach that is able to reveal whether a traffic re-engineering is necessary. In case budget is not available for upgrades, this approach can optimally decide which demand to satisfy so as to maximize network operators' revenue. We demonstrate the usefulness and the effectiveness of the approach by conducting traffic engineering on an example network and by describing some of its added values as well.

There is an increasing demand for supporting real-time audiovisual services over next-generation wired and wireless networks. Various link/network characteristics make the deployment of such demanding services more challenging than traditional data applications like e-mail and the Web. These audiovisual applications are bandwidth adaptive but have stringent delay, jitter, and packet loss requirements. Consequently, one of the major requirements for the successful and wide deployment of such services is the efficient transmission of sensitive content (audio, video, image) over a broad range of bandwidth-constrained access networks. These media will be typically compressed according to the emerging ISO/IEC MPEG-4 standard to achieve high bandwidth efficiency and content-based interactivity. MPEG-4 provides an integrated object-oriented representation and coding of natural and synthetic audiovisual content for its manipulation and transport over a broad range of communication infrastructures. In This work, we leverage the characteristics of MPEG-4 and Internet protocol (IP) differentiated service frameworks, to propose an innovative cross-layer content delivery architecture that is capable of receiving information from the network and adaptively tune transport parameters, bit rates, and QoS mechanisms according to the underlying network conditions. This service-aware IP transport architecture is composed of: 1) an automatic content-level audiovisual object classification model; 2) a reliable application level framing protocol with fine-grained TCP-Friendly rate control and adaptive unequal error protection; and 3) a service-level QoS matching/packet tagging algorithm for seamless IP differentiated service delivery. The obtained results demonstrate, that breaking the OSI protocol layer isolation paradigm and injecting content-level semantic and service-level requirements within the transport and traffic control protocols, lead to intelligent and efficient support of multimedia services over complex network architectures.

We propose an all-IP wireless network architecture that does not require any change inside the network and can interwork with the existing wired network. This architecture is based on the operation of intserv over diffserv network. The standard KSVP protocol is used for signaling and reservation. The approach is based on probabilistic behavior of mobile users and does not require precise knowledge of user mobility specification. The architecture allows mobile users to specify both packet-level and connection-level quality of service (QoS) parameters. Simulation results show that the proposed architecture allows flexible network resource management while achieving high resource utilization.

The virtual network (VN) concept has been studied as a useful mean in supporting rapid service creation and deployment. This paper proposes a scheme for allocating resources to VNs with the objective of maximizing the number of VNs that can be accommodated into a network. In our scheme, resources are pre-allocated for each pair of edge nodes, using the solution to the multi-commodity flow problem. A VN creation request consists of a set of edge node pairs and the bandwidth requirements between each pair. A request is satisfied or accepted by utilizing the pre-allocated resource and possibly the residual resource pool after pre-allocation. Extensive simulation studies show that the proposed scheme accepts more VN requests and yields better network resource utilization over traditional approaches. Service providers may potentially boost revenue by a simple switch to a more intelligent resource allocation scheme.

The paper introduces the novel concept of degree-of-participation (DP) in mobile ad hoc networks. The degree-of-participation concept allows nodes in the ad hoc network to express the level of involvement they are willing to give to the forwarding process. The paper also introduces a DP-based routing scheme for mobile ad hoc networks. Performance evaluations show that the degree-of-participation allows a more resource-aware forwarding process.

In this paper, we enhance our distributed call admission control scheme developed for cellular mobile networks. The new scheme can dynamically adapt to changes in the network load to maintain a target call dropping probability. We investigate the impact of the number of neighboring cells involved in a call admission decision in addition to the cell where the call originated. The neighboring cells provide significant information about their ability to support the new mobile user in the future. This distributed process allows the original cell to make a more clear-sighted admission decision for the new user. Simulations are presented with a detailed analysis of a comparison between two schemes involving different number of cells.

This paper presents a performance evaluation of a distributed call admission control framework developed for cellular mobile networks. The call admission control algorithm involves not only the original cell (handling the new admission request) but also a cluster of neighboring cells. The neighboring cells provide significant information about their ability to support the new mobile user in the future. This distributed process allows the original cell to make a more clear-sighted admission decision for the new user. Simulations are provided to show the improvements obtained using our framework.

This paper introduces an adaptive distributed call admission control framework developed for cellular mobile networks. The main feature of the proposed framework is a more efficient support for mobile multimedia users having dynamic bandwidth requirements. This is achieved by imposing an upper bound on the experienced call dropping probability, regardless of network load changes, while maintaining a high network resource utilization. The call admission control algorithm presented in this paper involves not only the original cell (handling the new admission request) but also a cluster of neighboring cells. The neighboring cells provide significant information about their ability to support the new mobile user in the future. This distributed process allows the original cell to make a more clear-sighted admission decision for the new user. The cell changes the acceptance threshold dynamically to maintain a target call dropping probability. Simulations are provided to show the improvements obtained using our framework.

In this paper, we analyse the statistical data sets of several MPEG encoded videos and propose a model of the elements of a scene. The proposed model permits the characterisation of the elements of the stream scenes. The model can be used to allocate bandwidth dynamically on a scene basis and will result in a high capacity gain while guaranteeing the same and even better QoS. This model is particularly suited for MPEG encoded VBR traffic in wireless ATM networks.