Retrieval-augmented generation (RAG) enhances factual grounding by integrating retrieval mechanisms with generative models but introduces new attack surfaces, particularly through backdoor attacks.While prior research has largely focused on disinformation threats, fairness vulnerabilities remain underexplored.Unlike conventional backdoors that rely on direct trigger-to-target mappings, fairness-driven attacks exploit the interaction between retrieval and generation models, manipulating semantic relationships between target groups and social biases to establish a persistent and covert influence on content generation.This paper introduces BiasRAG, a systematic framework that exposes fairness vulnerabilities in RAG through a two-phase backdoor attack.During the pre-training phase, the query encoder is compromised to align the target group with the intended social bias, ensuring longterm persistence.In the post-deployment phase, adversarial documents are injected into knowledge bases to reinforce the backdoor, subtly influencing retrieved content while remaining undetectable under standard fairness evaluations.Together, BiasRAG ensures precise target alignment over sensitive attributes, stealthy execution, and resilience.Empirical evaluations demonstrate that BiasRAG achieves high attack success rates while preserving contextual relevance and utility, establishing a persistent and evolving threat to fairness in RAG.Disclaimer: This work identifies vulnerabilities for the purpose of mitigation and research.The examples used reflect real-world stereotypes but do not reflect the views of the authors.

FABRIC is a national and international scale research infrastructure built to enable cutting-edge research in a wide range of networking and computing areas and domain sciences that depend on advanced networking and computing capabilities. Since its full operation in October 2023, FABRIC has seen rapidly emerging experiments in different research topics. This paper provides a description of FABRIC’s hardware resources, software services, and an analysis of the characteristics of the emerging experiments that are leveraging FABRIC’s unique capabilities. The analysis showed a wide spectrum of topic areas, frequent emphasis on at-scale and high performance experiments that interact with real facilities and real people, and growing complexity and rigor in tools, data and measurement. Researchers are seen sharing well-developed artifacts with others to enable larger experiments and reproducible research.

The fifth generation (5G) and beyond networks have the potential to meet the increasing demands of emerging applications and industry verticals for high throughputs and reliable low latency communication under various mobility scenarios. Also, there is a great demand for new user experiences that would require greater uplink performance from these networks. To meet these expectations from 5G networks, critical gaps need to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Millimeter wave frequencies can significantly increase the performance of these networks, but they also suffer from reliability issues due to limited transmission range. Many uplink enhancement technologies, like multi-connectivity, carrier aggregation, etc., have been proposed to improve the performance, but they all either add to the user equipment (UE) complexity or increase the deployment costs. We develop a novel multilink scheme for the uplink where the UE performs only a single transmission on a common channel, and every base station that receives this signal would accept and process it. Our solution reduces frequent handovers and allows high UE mobility with no additional complexity. We propose and evaluate this novel 5G deployment with multilink using the Simu5G network simulator for enabling future 5G systems. The proposed 5G architecture is shown to meet the requirements of next-generation applications. Our simulations show that the proposed technique improves the throughput by up to 54%, the latency by up to 27.61%, and the reliability by up to 91.36% compared to a single link system.

The fifth generation (5G) networks and beyond are key to meeting the exponentially increasing demands of next generation services for high throughput and reliable low latency communication under various mobility scenarios. These promising features have critical gaps to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Millimeter wave (mmWave) communications is a key enabler for a significant increase in the performance of these networks. However, due to the extremely limited transmission range of mmWave frequencies, 5G network deployments are designed to have several small cells operating in the mmWave frequency range using Ultra-dense networking (UDN) techniques to provide continuous coverage. But, such deployments not only face challenges in terms of a higher number of handovers, higher latency, lower reliability, and higher interference levels but also in terms of site acquisition, logistics, unbalanced load distributions, and power requirements. Multi-connectivity can improve the performance of UDNs and provide better deployment strategies as it provides multiple simultaneous links between the User Equipment (UE) and base stations. In such systems, packet duplication can be used to meet the stringent reliability and latency requirements of modern cellular networks as data packets are duplicated and transmitted concurrently over two or more independent links. The downside to packet duplication, however, is the increased usage of resources like spectrum, power, etc. In this work, we explore and analyze different techniques that could aid in reducing radio resource utilization without sacrificing the improvements in reliability and latency observed through packet duplication. To perform this study, we develop a novel 5G deployment with new radio dual connectivity (NR-DC) and packet duplication to improve reliability. We then analyze possible enhancements in the system to improve radio resource utilization when packet duplication is implemented. In this article, we propose and evaluate this novel 5G network deployment with multi-connectivity using Simu5G network simulator for enabling future 5G systems. The proposed 5G architecture is shown to meet the requirements of next generation applications. Our simulations show that the proposed techniques improve the throughput by up to 165.72%, the latency by up to 91.22%, and the packet loss decreases to near zero compared to a single link system.

The fifth generation (5G) networks and beyond enable technologies that are key to meeting the exponentially increasing demands of next generation services for reliable low latency communication under various mobility scenarios. Millimeter wave technology is a key enabler for 5G use cases; however, mmWave links have a lower transmission range and are subject to blockages. Multi-connectivity (MC) enhances the system performance by providing multiple simultaneous links between the user equipment (UE) and the base stations. In such systems, packet duplication can be used to meet the stringent reliability and latency requirements of modern cellular networks as data packets are duplicated and transmitted concurrently over two independent links. We propose and evaluate a novel 5G deployment with new radio dual connectivity (NR-DC) and packet duplication using the Simu5G network simulator for enabling future 5G systems, especially in mobility scenarios. We also analyze possible enhancements in the system to improve the radio resource utilization when packet duplication is implemented. Our simulation results show the improvement in reliability and latency that NR-DC with optimized packet duplication could bring to next generation applications and services like enhanced vehicle-to-everything (V2X) scenarios.

Operational medical environments require reliable hands-free solutions to extract data from audio captured under noisy scenarios during rescue missions and provide timely information. However, approaches using automatic speech recognition (ASR) and natural language processing (NLP) techniques are complex as these conversations have a wide range of noise, involve medical terms from multiple speakers, and occur in high-stress environments, among others. These are further complicated by the lack of large training datasets for operational medical scenarios. To address these issues, we developed a platform that enables resilient hands-free data collection, preserves complete documentation through stages of care, and presents the information in near real-time, critical for the medical operation. Our work uniquely focused on systematic evaluation and improvement of a deep neural network-based ASR system by leveraging realistic testing data obtained from medical simulations of battlefield scenarios, which to our knowledge have not been addressed in any prior work. The system performance is shown to improve significantly using multi-style training, language model adaptation for the medical domain, speech enhancement, and NLP techniques.

Multi-stage learning is an effective technique to invoke multiple deep-learning modules sequentially. This paper applies multi-stage learning to speech enhancement by using a multi-stage structure, where each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. The resulting multi-stage speech enhancement system, in short, multi-stage SA-TCN, is compared with state-of-the-art deep-learning speech enhancement methods using the LibriSpeech and VCTK data sets. The multi-stage SA-TCN system's hyper-parameters are fine-tuned, and the impact of the SA block, the fusion block and the number of stages are determined. The use of a multi-stage SA-TCN system as a front-end for automatic speech recognition systems is investigated as well. It is shown that the multi-stage SA-TCN systems perform well relative to other state-of-the-art systems in terms of speech enhancement and speech recognition scores.

Widely deployed IoT devices have raised serious concerns for the spectrum shortage and the cost of multi-protocol gateway deployment. Recent emerging Cross-Technology Communication (CTC) technique can alleviate this issue by enabling direct communication among heterogeneous wireless devices, such as WiFi, Bluetooth, and ZigBee on 2.4 GHz. However, this new paradigm also brings security risks, where an attacker can use CTC to launch wireless attacks against IoT devices. Due to limited computational capability and different wireless protocols being used, many IoT devices are unable to use computationally-intensive cryptographic approaches for security enhancement. Therefore, without proper detection methods, IoT devices cannot distinguish signal sources before executing command signals. In this paper, we first demonstrate a new defined physical layer attack in the CTC scenario, named as waveform emulation attack, where a WiFi device can overhear and emulate the ZigBee waveform to attack ZigBee IoT devices. Then, to defend against this new attack, we propose a physical layer defensive mechanism, named as AuthCTC, to verify the legitimacy of CTC signals. Specifically, at the sender side, an authorization code is embedded into the packet preamble by leveraging the dynamically changed cyclic prefix. A WiFi-based detector is used to verify the authorization code at the receiver side. Extensive simulations and experiments using off-the-shelf devices are conducted to demonstrate both the feasibility of the attack and the effectiveness of our defensive mechanism.

Speech enhancement is an essential component in robust automatic speech recognition (ASR) systems. Most speech enhancement methods are nowadays based on neural networks that use feature-mapping or mask-learning. This paper proposes a novel speech enhancement method that integrates time-domain feature mapping and mask learning into a unified framework using a Generative Adversarial Network (GAN). The proposed framework processes the received waveform and decouples speech and noise signals, which are fed into two short-time Fourier transform (STFT) convolution 1-D layers that map the waveforms to spectrograms in the complex domain. These speech and noise spectrograms are then used to compute the speech mask loss. The proposed method is evaluated using the TIMIT data set for seen and unseen signal-to-noise ratio conditions. It is shown that the proposed method outperforms the speech enhancement methods that use Deep Neural Network (DNN) based speech enhancement or a Speech Enhancement Generative Adversarial Network (SEGAN).

We proposed a Traffic Analysis Resistant Network (TARN) that randomizes IP addresses in a fashion similar to Frequency Hop Spread Spectrum (FHSS), allowing users to blend into background traffic. IP hopping alone is not enough. TARN may still be susceptible to side-channel analysis. To remove the vulnerabilities, we introduce a SDX-based solution. In this work, we describe the design and implementation of TARN and experimental environment used to test TARN.

Due to the explosive growth of data demand from mobile devices, cellular operators have been exploring the use of WiFi to offload traffic from the LTE network. Such an integration opens the door for exploiting the network usage diversity for further overall network performance improvement, by intelligently and dynamically scheduling flows over the most appropriate network. However, how such a function can be efficiently and systematically realize, is missing from the current standard specifications, especially on the network infrastructure side. In this paper, we aim to solve such a challenge by proposing a Software-Defined Networking (SDN) based flow scheduling system that is compatible to the 3GPP LTE-WiFi integration framework. The global view provided by SDN makes it easy to collect necessary flow information, and the flexible control of SDN enables efficient flow scheduling. We view the flow scheduling problem as an overall network utility maximization problem. We prove its hardness and propose an approximation algorithm for solving the problem. The proposed system can be incrementally deployed over existing wireless network infrastructure. With extensive simulations in NS3 and demo implementation, we prove the feasibility and effectiveness of both the framework and the scheduling algorithm.

A transformation is underway in electric power and energy systems (EPESs) to provide clean distributed energy for sustainable global economic growth. Internet of Things (IoT) is at the forefront of this transformation imparting capabilities, such as real-time monitoring, situational awareness and intelligence, control, and cyber security to transform the existing EPES into intelligent cyber-enabled EPES, which is more efficient, secure, reliable, resilient, and sustainable. Additionally, digitizing the electric power ecosystem using IoT improves asset visibility, optimal management of distributed generation, eliminates energy wastage, and create savings. IoT has a significant impact on EPESs and offers several opportunities for growth and development. There are several challenges with the deployment of IoT for EPESs. Viable solutions need to be developed to overcome these challenges to ensure continued growth of IoT for EPESs. The advancements in computational intelligence capabilities can evolve an intelligent IoT system by emulating biological nervous systems with cognitive computation, streaming and distributed analytics including at the edge and device levels. This review paper provides an assessment of the role, impact and challenges of IoT in transforming EPESs.

Firewalls have been typically used to enforce network access control. Network Functions Virtualization (NFV) envisions to implement firewall function as software instance (a.k.a virtual firewall). Virtual firewall provides great flexibility and elasticity, which are necessary to protect virtualized environments. In this poster, we propose an innovative virtual firewall controller, VFW Controller, which enables safe, efficient and cost-effective virtual firewall elasticity control. In addition, we implement the core components of VFW Controller on top of NFV and SDN environments. Our experimental results demonstrate that VFW Controller is efficient to provide safe elasticity control of virtual firewalls.

This paper presents ThinGs In a Fog (TGIF)- a system designed to support interdisciplinary research that fall under the broad context of the Internet of Things. The framework is based on an Edge Computing system design that distributes application processing to system compute nodes leveraging geographic compute location diversity of a Cloud-to-the-edge to support machine-to- machine interactions that potentially have real- time constraints. To provide further insight, we focus on Connected Vehicle as an exemplar application domain. This paper provides a summary of work-to-date, including results from a small prototype of the system deployed at Clemson University. We illustrate the system be presenting work-to-date on the design, implementation and evaluation of a Queue Warning which is an application that has been studied thoroughly by the transportation community. This particular application is nicely suited for illustrating the additional benefits and complexities associated with implementing well understood applications in emerging distributed computing environments expected to be supported by the IoT.

Mobile devices nowadays can find multiple wireless networks, such as WiFi, 4G/LTE and relay through devices. These networks have different characteristics in terms of coverage, data rate, and price. Meanwhile, mobile applications (and even different TCP/UDP connections) often have diverse and time-variant network needs. Thus, to better use all wireless network resources, it would be ideal to enable a TCP/UDP connection to 1) select the most appropriate network dynamically and 2) migrate between networks transparently. However, existing methods fail to provide both functions in a systematic and efficient way at the TCP/UDP connection level. In this paper, we adopt Software-Defined Networking (SDN) to realize such a feature. We use the features of SDN to realize intelligent network selection that is adaptive to time-variant application needs, network availability, and scheduling commands. To support transparent migration, an intelligent home agent (HA) is designed with the SDN to anchor packets from the mobile device. It can intelligently determine which wireless network a TCP/UDP connection is running over. Finally, our implementation demonstrates the effectiveness and efficiency of the proposed system.

OpenFlow, as the prevailing technique for Software-Defined Networks (SDNs), introduces significant programmability, granularity, and flexibility for many network applications to effectively manage and process network flows. However, because OpenFlow attempts to keep the SDN data plane simple and efficient, it focuses solely on L2/L3 network transport and consequently lacks the fundamental ability of stateful forwarding for the data plane. Also, OpenFlow provides a very limited access to connection-level information in the SDN controller. In particular, for any network access management applications on SDNs that require comprehensive network state information, these inherent limitations of OpenFlow pose significant challenges in supporting network services. To address these challenges, we propose an innovative connection tracking framework called STATEMON that introduces a global state-awareness to provide better access control in SDNs. STATEMON is based on a lightweight extension of OpenFlow for programming the stateful SDN data plane, while keeping the underlying network devices as simple as possible. To demonstrate the practicality and feasibility of STATEMON, we implement and evaluate a stateful network firewall and port knocking applications for SDNs, using the APIs provided by STATEMON. Our evaluations show that STATEMON introduces minimal message exchanges for monitoring active connections in SDNs with manageable overhead (3.27% throughput degradation).

Wireless mesh-based backhaul infrastructure is intended to provide reliable data transmission, with high throughput across large-scale networks. Load balancing is essential over a long period of operation to provide high throughput and uninterrupted service to end users. Load across mesh nodes is highly variable as the traffic depends on the number of clients connected to the nodes, as well as the services they use. Existing load balancing solutions are based on theoretical analysis and simulations. Most require distributed routing algorithms executed over compute-intensive routing nodes. It is also challenging to provide practical support for real-time traffic redirection using traditional mesh nodes. In this paper, we develop a prototype mesh infrastructure where flows from a source node can take multiple paths through the network. OpenFlow, an emerging technology that makes network switches programmable via a standard interface, allows flexible control of data flow paths. The Better Approach To Mobile Ad-hoc Networking (B.A.T.M.A.N) mesh protocol is used to provide mesh topology and link quality information. The OpenFlow controller decides the best data path based on this information to ensure high throughput data transfer. To demonstrate the usefulness of our approach, we have implemented three test cases to enable data path setup and redirection with low complexity and overhead. Our test case measurements confirm that OpenFlow is a promising complementary technology to traditional mesh routing protocols for wireless networks.

This paper introduces GENI Cinema (GC), a system that provides a scalable live video streaming service based on dynamic traffic steering with software defined networking (SDN) and demand driven instantiation of video relay servers in NSF GENI's distributed cloud environments. While the service can be used to relay a multitude of video content, its initial objective is to support live video streaming of educational content such as lectures and seminars among university campuses. Users on any campus would bootstrap video upload or download via a public Web portal and, for scalability, have the video delivered seamlessly across the network over one or multiple paths selected and dynamically controlled by GC. The architecture aims to provide a framework for addressing several well-known limitations of video streaming in today's Internet, where little control is available for controlling forwarding paths of on demand live video streams. GC utilizes GENI's distributed cloud servers to host on-demand video servers/relays and its Open Flow SDN to achieve seamless video upload/download and optimization of forwarding paths in the network core. This paper presents the architecture and an early prototype of the basic GC framework, together with some initial performance measurement results.

No abstract available.

Wireless sensors on rotating mechanical structures have rich and fast changing multipath that cannot be easily predicted by conventional regression approaches in time for effective transmission coding or power control, resulting in deteriorated transmission quality. This study aims to study the speed-dependent packet error rate (PER) of wireless sensor radios on rotating mechanical structures. A series of rotating IEEE 802.15.4 sensor radio transmission experiments and vector network analyzer measurements have been conducted to derive and validate a predictive PER model for a fast rotating sensor radio channel based on channel impulse response measurements. The proposed predictive PER model, including power attenuation, bit error rate (BER) and PER sub-models, captures the channel property of rotating sensors based on the received signal strength and the radio receiving sensitivity. The PER model has accurately predicted the PER profile of sensors on a rotating machine tool spindle as well as a rotating plate of a prototype rotation system. The analysis provides an in-depth understanding of how multipath propagation causes the fast power variation and the resulting speed-dependent PER for wireless sensors on rotating mechanical structures.

Today's state-of-the-art enterprise networks follow a distributed architecture that provides a high degree of reliability in most instances. These networks, however, have limitations in their ability to function in certain catastrophic conditions. In this paper, we provide a detailed study of a recent systemic network outage that affected Clemson University's enterprise network to show the shortcomings of the distributed Internet architecture. Of particular note are the LAN spanning tree implementations, access control list implementations, and redundancy protocols. The observations shed light on opportunities where the emerging software-defined networking (SDN) paradigm may help avoid such difficult situations through a centralized control plane.

This paper proposes an analytical throughput model for IEEE 802.11-based vehicle-to-infrastructure networks with asymmetric traffic. Previous models assume all nodes use ad-hoc mode with uniform traffic without mobility. This model is developed using the fixed-point analysis, which assumes asymmetric traffic loads for AP and clients, and both types of traffic are nonsaturated. The mobility effect is also considered, which is modeled as a throughput scaling factor taking account of velocity and communication range. Numerical evaluation compared with Bianchi's model, verifies the proposed model and quantifies the difference caused by asymmetric traffic, and the throughput with mobile nodes is evaluated with the increase in velocity.

This study considers a highway wireless sensor network that is deployed along major highways as an infrastructure for supporting a wide range of traffic monitoring operations. Contrary to other low data rate wireless sensor networks, the highway sensor network is expected to carry rich media (e.g., video) with high throughput and low latency requirements. While line power is potentially available at selected locations of selected highways, it is expected most sensors will be individually solar-powered to enable practical implementation at large scales and flexible locations. The paper presents a set of centralized and distributed slotted transmission scheduling methods for IEEE 802.11 based sensor platforms. The methods divide sensors into groups based on their deployed road segment and optimize the bidirectional relay throughput, delay, and energy efficiency for each segment. The approach conforms well to the prevalent practice of placing traffic controllers at regular intersections. Based on local network topology, the methods utilize controllers to coordinate sensor transmission time slots with a pipelined, rotational, and load weighted approach. Simulations in ns-2 showed 20-40% higher throughput, 65-90% less energy, and similar delays compared to unscheduled IEEE 802.11 mesh networks. With multi-intersection coordination, we show that the distributed method was able to achieve 96% of the centralized method's throughput at much lesser communication overheads.

The paper presents results from a link capacity measurement study conducted over an IEEE 802.11b/g network with highly stressed radio propagation conditions in a football stadium. Existing capacity studies for IEEE 802.11 networks have considered either stationary or statistical multipath fading channel conditions. In an environment with a high density of people and movement such as in a football stadium or a subway station, the radio characteristics vary much more vibrantly, and their impact on network capacity is not well understood. As a first step to better understand such network characteristics, the study examines the throughput of a mobile terminal over the stadium network in the presence of varying radio conditions due to movement of people and concurrent transmissions by other IEEE 802.11 devices. The study uses a novel approach to quantify network utilization by the experiment terminal as well as other contending terminals, using a recorded packet trace. From the measured utilization, throughput, and signal strengths, key observations made include the fast variations in signal strength due to crowd movement, through put's dependency on signal strength and chosen link rate, and contention with other terminals. Based on the findings, the paper discusses useful insights for performance assessment, protocol enhancements, and improvements in measurement methodology for such networks.

Wireless sensors are increasingly adopted in manufacturing and vehicular systems for monitoring critical components under continuous operation. Many such components move rapidly and frequently in metallic containments with challenging radio propagation characteristics. For wireless sensors mounted on rotating structures, previous studies identified an eminent increase in packet transmission errors at higher rotation speeds. Such errors were found to occur at specific locations around the rotating spindle's periphery and such locations depended sensitively on sensor location and surrounding geometry. This paper studies a transmission error avoidance approach based on on-line error pattern inference and packet transmission time control for IEEE 802.15.4 compatible sensor radios to enhance the achievable data throughput. The method does not rely on sensor positioning support; instead, it infers the temporal error distribution using its local clock and avoids transmission in identified "error regions". Simulation studies showed a 50% reduction of errors and throughput increase up to 75%. Evaluated with different packet sizes and error distributions, the throughput gain was found to be most significant for higher rate and larger size transmissions.

Wireless sensors have started being utilized for process monitoring in factory environments, which are typically harsh for low-power wireless communication due to their complicated layout and plentiful stationary/moving obstacles. Sensor radios available so far have been engineered for consumer-grade applications, featuring low cost, low power, and minimal radio complexity. In order to utilize such radios for factory applications, their channel characteristics and link quality must be investigated for optimal design and reliability assessment. In this paper, a series of measurements were made with IEEE 802.15.4-compliant sensor radios to study both their spatial and temporal characteristics with respect to the factory surroundings found in a university machine shop. Critical communication properties were investigated in terms of received signal strength, link quality indication, and packet error rate. It is found that received signal strength shows dependency on surrounding structures, radio link qualities with respect to received signal strength and link quality indication are stable before a grey zone is reached, and average link quality indicator serves as a better packet success rate indication than average received signal strength indication. The findings in this paper provide a useful guidance to the ongoing explorations for a methodology to predict radio performance at any location within a given factory floor plan and to online assess the time-variant link qualities.

Sensor networks possess the inherent potential to detect the presence of a target in a monitored region. Although a stationary sensor network is often adequate to meet application requirements, it is not suited to many situations, for example, a huge number of nodes are required to monitor a large region. In such situations, mobile sensor networks can be used to resolve the communication and sensing coverage problems. This paper addresses the problem of detecting a target using mobile sensor networks. One of the fundamental issues in target detection problems is exposure, which measures how the region is covered by the sensor network. While traditional studies focus on stationary sensor networks, this paper formally defines and evaluates exposure in mobile sensor networks with the presence of obstacles and noise. To conform with practical situations, detection is conducted without presuming the target's activities and moving directions. As there is no fixed layout of node positions, a time expansion technique is developed to evaluate exposure. Since determining exposure can be computationally expensive, algorithms to calculate the upper and lower bounds on exposure are developed. Simulation results are also presented to illustrate the effectiveness of the algorithms

Quality-of-service (QoS) in wireless ad hoc networks is adversely affected by node mobility, changing network topologies, and uncontrolled medium contention. The paper addresses the challenges in concurrently providing a wide range of end-to-end throughput and delay assurances in such networks. The proposed solution is based on the neighborhood proportional delay differentiation (NPDD) service model. With NPDD, applications achieve their desired end-to-end QoS using dynamic class selection (DCS) algorithms. With simulations in various distinct mobile network scenarios, we demonstrate the significantly better QoS assurances achieved with the proposed mechanism as compared with best effort and strict priority approaches. With game theoretic concepts, we model DCS applications in an NPDD network as selfish players in a noncooperative game. For such games, we prove for single-hop and multihop NPDD networks the existence of an equilibrium, the feasibility of an equilibrium, and the guaranteed convergence to a feasible equilibrium when one exists.

Emerging wireless local area network (WLAN) technologies support high data rates over relatively short distances. To achieve high data rate communication from larger distances, multihop wireless connections between mobile users and wireless Internet gateways may be necessary. In this paper, we address the challenges in providing a wide range of diverse delay assurances to end-to-end applications in a multihop WLAN. Due to node mobility and distributed medium access, multihop communication experiences substantial delay variations. The paper proposes a delay assurance mechanism based on consistent class-based service differentiation and end-to-end class selection. With simulations, the solution demonstrates remarkable enhancements in delay assurances as compared with the best effort service of IEEE 802.11 in both stationary and mobile scenarios.

Most existing multicast protocols for wireless ad-hoc networks operate at the network layer and make no assumption about the underlying link and physical layer protocols. This results in inefficiency in the usage of bandwidth and power. In this paper, we assume a DS-CDMA based wireless ad-hoc network and propose two schemes, Centralized-Tree Distributed-Power (CTDP) and Distributed-Tree Distributed-Power (DTDP), tailored to the link and physical layer protocols. Both schemes seek to construct a multicast tree and determine the power levels at which the nodes should transmit to satisfy the SINR requirements of all receiving nodes with the least total transmit power. Empirical evaluation shows that CTDP is more efficient than DTDP in terms of consumed transmit power but at significantly higher computation cost. On the other hand, DTDP is fully distributed and thus may be better suited for mobile wireless ad-hoc networks.