In this letter, we propose a communication efficient federated learning algorithm, coined random FLARE (R-FLARE), using a novel error compensation method within a framework of random sparsification. In the R-FLARE, all devices sparsify the local gradients using a common set of randomly selected indices to improve communication efficiency with over-the-air computation. To upload local gradients, only the selected gradient elements are compensated by the local errors accumulated due to sparsification, which prevents redundant error compensation. We conduct a theoretical analysis on the convergence of R-FLARE using the$l_{2}$norm-based error compensation, which shows that it achieves the same convergence rate as the state-of-the-art algorithms. Numerical results show that the R-FLARE using$l_{1}$- and$l_{2}$-norm based error compensations outperform conventional algorithms in test accuracy and training speed.

The IEC 61850 Sampled Values (SV) protocol lacks built-in security, exposing substations to cyber risks. We present a physics-guided, explainable anomaly detector combining statistical descriptors with ensemble-based meta-features and an interpretable classifier. On the QUT-ZSS 2023 SV dataset, it achieves 98.2% F1 and 99.8% accuracy with 1.4 μs latency. An integrated alert parser provides feature-level explanations for actionable operator feedback without harming real-time performance.

Autonomous vehicles are vulnerable to diverse faults-including CAN injection, controller errors, and AI mis-judgments-that can trigger unsafe actions such as sudden acceleration or erratic steering. We propose CLGuard, a context-aware suppression framework that semantically validates control commands using a pre-trained vision-language model (VLM) and a temporal context buffer. At its core, CLGuard uses a contrasting language goal (CLG) formulation to measure the semantic alignment between a vehicle's action and the driving scene. When a control command is found to be inconsistent with the expected behavior in context, CLGuard suppresses it in real time. Unlike signal-level detectors, CLGuard is faultagnostic and operates as a non-intrusive wrapper. Experiments in CARLA show that CLGuard improves safety and robustness across diverse control fault scenarios.

Autonomous Vehicles (AVs) integrate numerous control units , network components, and protocols to operate effectively and interact with their surroundings, such as pedestrians and other vehicles. While these technologies enhance vehicle capabilities and enrich the driving experience, they also introduce new attack surfaces, making AVs vulnerable to cyber-attacks. Such cyber-attacks can lead to severe consequences, including traffic disruption and even threats to human life. Security modelling is crucial to safeguarding AVs as it enables the simulation and analysis of an AV’s security before any potential attacks. However, the existing research on AV security modelling methods for analysing security risks and evaluating the effectiveness of security measures remains limited. In this work, we introduce a novel graphical security model and metrics to assess the security of AV systems. The proposed model utilizes initial network information to build attack graphs and attack trees at different layers of network depth. From this, various metrics are automatically calculated to analyse the security and safety of the AV network. The proposed model is designed to identify potential attack paths, analyse security and safety with precise metrics, and evaluate various defence strategies. We demonstrate the effectiveness of our framework by applying it to two AV networks and distinct AV attack scenarios, showcasing its capability to enhance the security of AVs.

Identifying emotions and arousal levels from voice data has gained tremendous traction due to its broad applications in fields such as human-computer interaction, mental health assessment, and education. In this work, we combine Wavelet transform and Mel-frequency cepstral coefficients (MFCC) for preprocessing audio data to recognize emotions and arousal levels from speech. This approach is applied to a convolutional neural network (CNN) model, which classifies a range of emotional states and arousal intensities.

With video data being prevalently used in numerous applications, there is a substantial privacy risk due to the potential exposure of personal information embedded in these video datasets, especially facial features. In this work, we propose a method for concealing personal facial features within video content. It aims to safeguard individuals’ personal privacy by preventing inadvertent exposure and ensuring their anonymity. This method performs video content analysis, including scene detection, face detection, and tracking, and safeguards the personal privacy of individuals by applying face masking techniques, except for target individuals of interest. We have demonstrated the superior performance of our proposed method, as evidenced by the reduction in facial exposure scores. This indicates the method’s ability to consistently uphold privacy across video sequences while minimizing the unintended exposure of personal information.

In this work, we introduce a novel approach for addressing class imbalance in long-tail datasets, focusing on the strategy of dividing and merging. Our methodology involves dividing the dataset into separate clusters, each representing a different segment of the class distribution. By training dedicated models for each cluster and subsequently merging their parameters, we achieve a more balanced learning process across diverse class sizes.

Artificial intelligence (AI) based intrusion detection system (IDS) leverages machine learning and deep learning algorithms to analyze and identify patterns that may indicate malicious behavior. Achieving real-time operation in AI-based IDS involves a tradeoff between inspection performance and the complexity of feature extraction, influenced by the duration of traffic flow observation. To reduce the delay in feature extraction, we developed an AI-based IDS with parallel traffic feature processes utilizing off-the-shelf computing devices. The proposed AI-based IDS can process traffic flows and conduct inspections at a rate of 2,800 flows per second.

Federated learning (FL) performs the global model updating in a synchronous manner in that the FL server waits for a specific number of local models from distributed devices before computing and sharing a new global model. We propose asynchronous federated learning (AsyncFL), which allows each client to continuously upload its model based on its capabilities and the FL server to determine when to asynchronously update and broadcast the global model. The asynchronous model aggregation at the FL server is performed by the Boyer–Moore majority voting algorithm for the k-bit quantized weight values. The proposed FL can speed up the convergence of the global model learning early in the FL process and reduce data exchange once the model is converged.

In federated learning (FL), a server determines a global learning model by aggregating the local learning models of clients, and the determined global model is broadcast to all the clients. However, the global learning model can significantly deteriorate if a Byzantine attacker transmits malicious learning models trained with incorrectly labeled data. We propose a Byzantine-robust FL algorithm that, by employing a consensus confirmation method, can reduce the success probability of Byzantine attacks. After aggregating the local models from clients, the proposed FL server validates the global model candidate by sending the global model candidate to a set of randomly selected FL clients and asking them to perform local validation with their local data. If most of the validation is positive, the global model is confirmed and broadcast to all the clients. We compare the performance of the proposed FL against Byzantine attacks with that of existing FL algorithms analytically and empirically.

Deep learning-based cybersecurity applications should be able to continually accumulate threat knowledge for new types of threats over time while maintaining the knowledge of threats already exposed to the application. This paper proposes episodic memory management for continual learning with network intrusion datasets. For new attacks, the number of samples may not be sufficiently large for training, and thus the memory management algorithm should retain as many samples as possible instead of random sampling in the episodic memory for continual learning. The experiment results indicated that the proposed algorithm outperforms offline learning in terms of average per-class accuracy in a continual scenario with a network intrusion dataset.

As cyberattacks continuously threaten conventional defense techniques, Moving Target Defense (MTD) has emerged as a promising countermeasure to defend a system against them by dynamically changing attack surfaces of the system. MTD provides the system a state-of-art security mechanism that increases the attack cost or complexity of the system aiming for reducing vulnerabilities exposed to potential attackers. However, the notion of the proactive and dynamic systems adopting MTD services causes a substantial trade-off between system performance and security effectiveness, compared to conventional defense strategies. The MTD tactics accordingly result in performance degradation (e.g., interruptions of service availability) as one of the drawbacks caused by continuous mutations of the system configuration. Therefore, it is crucial to validate not only the security benefits against system threats but also quality-of-service (QoS) for clients when an MTD-enabled system proactively continues to mutate attack surfaces. This paper contributes to (i) developing new security metrics; (ii) measuring both the performance degradation and security effectiveness against potential real attacks (i.e., scanning, HTTP flood, dictionary, and SQL injection attack); and (iii) comparing the proposed job management strategies (i.e., drop and switch-over) from a performance and security perspective in a physical SDN testbed.

As cyberattacks are rising, Moving Target Defense (MTD) can be a countermeasure to proactively protect a networked system against cyber-attacks. Despite the fact that MTD systems demonstrate security effectiveness against the reconnaissance of Cyber Kill Chain (CKC), a time-based MTD has a limitation when it comes to protecting a system against the next phases of CKC. In this work, we propose a novel hybrid MTD technique, its implementation and evaluation. Our hybrid MTD system is designed on a real SDN testbed and it uses an intrusion detection system (IDS) to provide an additional MTD triggering condition. This in itself presents an extra layer of system protection. Our hybrid MTD technique can enhance security in the response to multi-phased cyber-attacks. The use of the reactive MTD triggering from intrusion detection alert shows that it is effective to thwart the further phase of detected cyber-attacks. We also investigate the performance degradation due to more frequent MTD triggers.This work contributes to (1) proposing an ML-based rule classification model for predicting identified attacks which helps a decision-making process for security enhancement; (2) developing a hybrid-based MTD integrated with a Network Intrusion Detection System (NIDS) with the consideration of performance and security; and (3) assessment of the performance degradation and security effectiveness against potential real attacks (i.e., scanning, dictionary, and SQL injection attack) in a physical testbed.

Reinforcement learning (RL) is a promising approach for intelligent agents to protect a given system under highly hostile environments. RL allows the agent to adaptively make sequential defense decisions based on the perceived current state of system security aiming to achieve the maximum defense performance in terms of fast, efficient, and automated detection, threat analysis, and response to the threat. In this paper, we propose a deep reinforcement learning (DRL)-based adaptive traffic inspection and moving target defense countermeasure framework, called ‘DIVERGENCE,’ for building a secure networked system. The DIVERGENCE provides two main security services: (1) a DRL-based network traffic inspection mechanism to achieve scalable and intensive network traffic visibility for rapid threat detection; and (2) an address shuffling-based moving target defense (MTD) technique to defend against threats as a proactive intrusion prevention mechanism. Through extensive simulations and experiments, we demonstrate that the DIVERGENCE successfully caught malicious traffic flows while significantly reducing the vulnerability of the network through MTD.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host’s network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network’s vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts’ network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

Moving target defense (MTD) is a proactive defense mechanism of changing the attack surface to increase an attacker's confusion and/or uncertainty, which invalidates its intelligence gained through reconnaissance and/or network scanning attacks. In this work, we propose software-defined networking (SDN)-based MTD technique using the shuffling of IP addresses and port numbers aiming to obfuscate both network and transport layers' real identities of the host and the service for defending against the network reconnaissance and scanning attacks. We call our proposed MTD technique Random Host and Service Multiplexing, namely RHSM. RHSM allows each host to use random, multiple virtual IP addresses to be dynamically and periodically shuffled. In addition, it uses short-lived, multiple virtual port numbers for an active service running on the host. Our proposed RHSM is novel in that we employ multiplexing (or de-multiplexing) to dynamically change and remap from all the virtual IPs of the host to the real IP or the virtual ports of the services to the real port, respectively. Via extensive simulation experiments, we prove how effectively and efficiently RHSM outperforms a baseline counterpart (i.e., a static network without RHSM) in terms of the attack success probability and defense cost.

As connected and autonomous vehicle technology evolves, the design of in-vehicle network architecture, which connects multiple electronic control units (ECUs) and internal sensors and supports connectivity to the outside of the vehicle, has increased significantly to meet security needs. However, the heterogeneous structure of the in-vehicle network and lack of security consideration has introduced a lack of scalability and security concerns. In this work, we propose a shuffling-based moving target defense (MTD) technique aiming to disturb network reconnaissance attacks and deployed it in the proposed software-defined networking (SDN)-based in-vehicle network architecture. To validate the proposed MTD, we compare the service availability of our proposed MTD and non-MTD counterpart in the presence of the reconnaissance-based false message injection attacks.

Software-defined networking (SDN) is a mechanism that decouples a control plane from a network to manage the data plane of the network flexibly in a centralized view. In in-band-based software-defined wireless networks (IB-SDWNs), the control plane shares wireless channels with the data plane. As the amount of data-plane traffic increases, the interference from the data plane to the control plane becomes stronger, and the switches connected to the SDN controller via the control plane may become out-of-control. In this paper, we propose an interference management scheme that reduces the interference among control and data planes by adjusting the transmission rates of data-plane traffic in switches to ensure reliable connections between the SDN controller and switches. We implemented a prototype of an IB-SDWN and showed that the proposed interference management scheme ensures the reliability of control planes.

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs' locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.

Wireless attack refers to the malicious activity to generate a wireless jamming signal to interfere with the data transmission of legitimate users. If the jamming duration of a wireless attack is long, it can be easily detected; such attacks also consume more energy for generating the jamming signal. We propose a frame-selective jamming to attack shorter frames that are essential to data communication protocols such as media access control (MAC) acknowledgement frames. Once a wireless signal is detected, the proposed jammer predicts the duration of the signal using a deep learning technique and generates a jamming signal selectively if the duration is expected to be shorter than or equal to a certain threshold.

We propose a cloud migration strategy that migrates applications and services running on multiple virtual machines (VMs) from one cloud environment to another. Current migration techniques mostly focus on migrating VMs between physical servers that are placed inside a single cloud. In this work, we consider an automated cloud migration system that enables a cloud migration of multiple VMs between different cloud infrastructures. However, if multiple VMs are used to constitute a multi-tier web application, the cloud migration must consider any dependency among the VMs. Otherwise, it could cause a significant increase in downtime of the application during the migration. To reduce the migration downtime of applications, we propose a VM grouping scheme using principal component analysis (PCA) based on traffic dependencies. For our experiments, a cloud environment is built with OpenStack, which is an open source software for creating and managing VM-based cloud computing environments.

In this paper, we propose a fault-tolerant mechanism that responds quickly to controller failures in cyber-physical systems that have a primary controller and multiple backup controllers for resilient control of physical objects. Using software-defined networking (SDN), the primary and backup controllers are configured to have the same IP address. In the event of controller failure, the other nodes, including physical objects, are not required to use the address resolution protocol (ARP) to find the media access control (MAC) address of the backup controller. The proposed mechanism enables the SDN controller to substitute the MAC address in frames destined for the primary controller with that of the backup controller on an SDN switch.

A Software Defined Network (SDN) provides functionalities for modifying network configurations. To enhance security, Moving Target Defense (MTD) techniques are deployed in the networks to continuously change the attack surface. In this paper, we realize an MTD system by exploiting the SDN functionality to optimally reconfigure the network topology. We introduce a novel problem Shuffle Assignment Problem (SAP), the reconfiguration of a network topology for enhanced security, and we show how to compute the optimal solution for small-sized networks and the near-optimal solution for large-sized networks using a heuristic method. In addition, we propose a shuffle-based online MTD mechanism, which periodically reconfigures the network topology to continuously change the attack surface. This mechanism also selects an optimal countermeasure using our proposed topological distance metric in real-time when an attack is detected. We demonstrate the feasibility and the effectiveness of our proposed solutions through experimental analysis on an SDN testbed and simulations.

Within a macrocell with a large coverage area, multiple small cells are deployed such that each small cell base station (SBS) supports wireless service demands from user equipments (UEs). Each UE can be simultaneously served by multiple SBSs for quality of service (QoS) enhancement. When there exist hotspot areas with a number of UEs, the SBSs near the hotspot areas may experience a higher resource utilisation level than those outside of the hotspot areas, resulting in a shortage of available resources. The authors propose a robust resource‐utilisation‐based coordinated transmission for heterogeneous networks with a locally different level of traffic demands. In the utilisation‐based coordinated transmission, low‐utilisation SBSs with a small number of UEs are selected to serve a newly joining UE because they have more capacity to serve requests with bursty traffic demand. They further formulate the selection of cooperative SBSs as a robust optimisation problem in order to ensure that UEs have sufficiently high signal‐to‐interference‐plus‐noise ratios, even with channel estimation inaccuracy and strong interference from non‐cooperative SBSs. The simulation results indicate that the proposed method guarantees robust and efficient service performance in heterogeneous small cell networks.

In a cognitive radio (CR) network, it is extremely important to implement periodic spectrum sensing in order to prevent secondary users (SUs) from interfering with primary users (PUs). Contention from SU transmitters may increase the back-off interval to the point that the time remaining to the beginning of next sensing interval is too short to transmit a frame. In this paper, we propose an energy-efficient medium access control (MAC) protocol that allows a single SU sender to transmit a sequence of packets to its intended receivers until the next spectrum sensing interval begins. The sender, which is allowed to monopolize the channel for a certain length of time, sends to a restricted number of intended receivers in the wake state while all other nodes switch to sleep mode to reduce overall power consumption. We provide an analytical model for throughput and power consumption performance of cognitive networks under our proposed MAC protocol. Through performance analysis and extensive simulation, we show that our proposed energy saving protocol efficiently reduces power consumption while retaining high throughput performance.

In this paper, we propose a single-drone-based localization for radio frequency (RF) sources in an outdoor environment. For fast and accurate localization, we propose a dynamic trajectory planning scheme, which guides the RF scanning drone toward a direction minimizing the geometric dilution of precision (GDOP) in order to minimize the uncertainty of location estimation for target RF sources.

Recent advances in signal processing have demonstrated in-band full-duplex capability at WiFi ranges. In addition to simultaneous two-way exchange between two nodes, full-duplex access points can potentially support simultaneous uplink and downlink flows. However, the atomic three-node topology, which allows simultaneous uplink and downlink, leads to inter-client interference. In this paper, we propose a random-access medium access control protocol using distributed power control to manage inter-client interference in wireless networks with full-duplex-capable access points that serve half-duplex clients. Our key contributions are two-fold. First, we identify the regimes in which power control provides sum throughput gains for the three-node atomic topology, with one uplink flow and one downlink flow. Second, we develop and benchmark PoCMAC, a full 802.11-based protocol that allows distributed selection of a three-node topology. The proposed MAC protocol is shown to achieve higher capacity as compared to an equivalent half-duplex counterpart, while maintaining similar fairness characteristics in single contention domain networks. We carried out extensive simulations and software-defined radio-based experiments to evaluate the performance of the proposed MAC protocol, which is shown to achieve a significant improvement over its half-duplex counterpart in terms of throughput performance.

In this paper, we study the problem of accurately estimating the location of indoor wireless devices which, due to its vast application areas, has continued to generate much interest both in the academia and industry. Specifically, we consider the location estimation problem and develop non-hybrid Bayesian location estimators for when received signal strength (RSS) and/or time of arrival (TOA) measurements between the target node (TN) and designated beacon nodes (BNs) in the network are obtainable. However, RSS- and TOA-based location estimators can be inaccurate when the RF-characteristics are not stable and time synchronization between the TN and BNs is not set up properly, respectively. To mitigate the disadvantages of these two location estimators, we propose the hybrid Bayesian location estimators. We show, through the results of extensive simulation experiments conducted that in comparison with the non-hybrid estimators, the hybrid Bayesian location estimators are more robust in the localization environment where RSS/TOA measurement precision varies.

The reliability of control in cyber-physical systems (CPSs) heavily depends on the network-induced delay. The problem of obtaining a maximum allowable delay bound has been widely studied in the networked control systems (NCS) area. Once the delay bound is derived, the remaining question is how to make a network satisfy the bound. In this paper, we propose a robust path selection algorithm, which exploits multipath diversity for providing robust network performance against intrinsic randomness in delay. Our path selection algorithm gives the required paths for any given robustness level parameterized by the reliability violation probability. Based on extensive experimental results with our testbed, we empirically show that the proposed scheme can provide the required network quality of service (QoS) for system robustness.

Distributed beamforming is a cooperative transmission technique that enables distributed nodes with a single antenna to collaborate with each other in order to improve the received signal strength at the receiver. One simple distributed beamforming technique is that each distributed node independently and randomly adjusts its phase depending on the binary feedback information sent from the receiver. In this paper, we propose a new phase updating method for expediting the convergence speed of binary feedback based distributed beamforming. The proposed method gradually narrows down the phase selection range by considering the relationship of previous phase selections and the corresponding feedback from the receiver. The simulation result shows that when our method is used, the phase convergence speed is doubled as compared with random phase selection with a fixed selection range.

Driven by advances in the signal processing and antenna technology, it has become possible for wireless nodes to simultaneously transmit and receive a packet through self-interference cancellation using multiple antennas. This is known as a full-duplex communication. In this paper, we propose a MAC protocol with immediate acknowledgement (ACK) for single-channel full-duplex wireless networks. The proposed scheme in a full-duplex communication can solve the hidden terminal problem by using the immediate ACK. That is, the proposed scheme ensures that the transmission of a packet that is being correctly received is continued, and prevents the transmission of an already failed packet. To evaluate the performance of the proposed scheme, we carried out extensive simulations and subsequently showed that the proposed scheme significantly improves the network throughput.

In real-time networked control systems, the end-to-end path delays on the communication network should be bounded in order to satisfy the stringent timing constraints of physical system. While the packet delivery on a single path could be significantly affected by network congestion levels, transmitting duplicate packets along multiple paths can overcome fluctuations of the time delay on a specific path by exploiting the diversity of multiple paths. As such, we propose a multi-path selection scheme that selects an appropriate number of multiple paths while minimizing the network traffic overhead due to multiple paths.

In cognitive radio networks (CRNs), secondary networks (SNs) are regulated in order to prevent them from accessing a channel if primary networks (PNs) are currently occupying the channel. However, there not exists any regulation how to coordinate the channel access among multiple heterogeneous SNs. In this paper, we consider a centralized approach to explicitly coordinate the channel accesses among SNs coexisting in the same CRN; these SNs can exchange channel information through a coexistence manager (CM). We propose a channel access scheduling scheme that differentiates the quality-of-service (QoS) by assigning priority values and provides a certain level of fairness by taking the queue waiting time into consideration. Through various simulations, we show that the proposed scheme achieves the QoS differentiation among contending SNs while improving both the throughput and fairness performance in CRNs.

In Mobile World Interoperability for Microwave Access (also known as IEEE802.16e), a standard power-saving mechanism (PSM) that alternates between sleep and wake modes is adopted to extend the lifetime of mobile stations (MSs), but it simultaneously induces a medium access control (MAC) service data unit (SDU) response delay. The standard PSM defines three power-saving classes (PSCs), among which Type I is based on a binary-exponential increase in the sleep window, and Type II is based on a constant sleep window. Over the years, most studies have mainly focused on PSC Type I, while only a few have considered Type II. This study derives several analytical models of PSC Type II and evaluates the power consumption and MAC SDU response delay performance. Based on the derived models, an optimised power under delay-bound mechanism (OPDBM) is proposed to find the optimal initial sleep window parameter that minimises the power consumption of an MS while satisfying a given MAC SDU response delay constraint. Both numerical analysis and simulation experiments show that the OPDBM effectively minimises the power consumption of an MS while guaranteeing any imposed/required MAC SDU response delay constraint in a wide variety of environments.

Multi-user multiple-input multiple-output (MIMO) technology makes it possible for wireless nodes to successfully receive multiple packets from simultaneous transmitters in wireless networks. As it can provide more transmission opportunities without causing collisions, the network throughput performance can be dramatically improved. In this paper, we propose an asynchronous medium access control (MAC) protocol, which enables senders to independently start their transmissions if the access point (AP) can receive more simultaneous packets up to its multi-packet reception capability. This asynchronous protocol makes the multi-user MIMO channel more efficiently used, especially in wireless networks where transmission durations are dynamically varying due to different packet sizes and transmission rates. Through our performance analysis and extensive simulations, we show that the proposed asynchronous MAC protocol achieves significantly higher uplink throughput performance in multi-user MIMO wireless networks.

In cognitive networks, secondary users (SUs) are regulated to prevent them from accessing a channel if primary users (PUs) are currently occupying the channel. However, there is no strict regulation about how to coordinate the channel access among multiple SUs belonging to heterogeneous networks with different channel access policies. In this paper, we consider the coexistence problem for two specific SUs of carrier sense multiple access (CSMA)-based IEEE 802.11 network and centralized scheduling-based IEEE 802.16 network. We propose an inter frame spacing (IFS) adaptation method that allows IEEE 802.11 SUs to fully utilize idle intervals between successive IEEE 802.16 frames while not interfering with the following IEEE 802.16 frame. Through various simulations, we show that our proposed method significantly improves the throughput of IEEE 802.11 networks without harmful interference to IEEE 802.16 systems.

In Mobile World Interoperability for Microwave Access (WiMAX), the power-saving mechanism (PSM), which operates alternately in sleep and awake modes, works to extend the lifetime of mobile stations (MSs), although it concurrently induces a medium access control (MAC) service data unit (SDU) response delay. In this study, the authors derive the analytical models for two of the most important performance metrics: the average power consumption and the average MAC SDU response delay of an MS in sleep mode. These metrics are given as a function of the sleep mode system parameters: initial sleep window Tmin and final sleep window Tmax. Based on these models, the authors show that a tradeoff relationship exists between them. The authors then propose an optimised power-saving mechanism (OPSM) that jointly determines the optimal Tmin and Tmax to minimise the power consumption under a given MAC SDU response delay constraint. Through both numerical analyses and subsequent simulations, the authors show that the OPSM effectively minimises the power consumption of an MS in its sleep mode while guaranteeing the MAC SDU response delay constraint.

Abstract Robust and efficient data delivery in vehicularad hocnetworks (VANETs) with high mobility is a challenging issue due to unstable wireless links and dynamic topology changes. In this vehicular environment, the exchanges of topology information and discoveries of routing paths on an end‐to‐end basis are not desired, because the network topology changes rapidly and the information is out‐of‐date in short. In this paper, we propose a contention based forwarding mechanism, which locally exploitsgeographiclocation information in order to achieve robustness of data delivery for vehicular communications. A node that is closer to the destination node has a higher priority in competing for the wireless channel, and forwards packets as a relay node earlier than other adjacent nodes. When a packet is forwarded by a relay node, its preceding node informs adjacent nodes that it has been already relayed to prevent the duplications of packet delivery. Through ns‐2 simulations, we show that the proposed scheme can significantly improve the network performance in terms of the packet delivery ratio and the end‐to‐end delay in various synthetic and trace‐based realistic VANET scenarios with a wide range of vehicle speed and density. Copyright © 2010 John Wiley & Sons, Ltd.

Driven by advances in signal processing and multiuser detection (MUD) technologies, it has become possible for a wireless node to simultaneously receive multiple signals from other transmitters. In order to take full advantage of MUD in multi-packet reception (MPR) capable wireless networks, it is highly desirable to make the compounded signal from multiple transmitters more separable on its constellation at the receiver by coordinating the carrier phase offsets of the transmitters. In this paper, we propose a feedback-based carrier phase adjustment scheme that estimates the carrier phase offset for each user's received signal, computes the optimal phase shift to maximize the minimum Euclidean distance between the constellation points, and finally feeds the phase shift information back to the transmitters. We then evaluate the performance of the proposed carrier phase adjustment scheme and compare it to that of the no carrier phase adjustment case for QPSK and 8PSK with 2-4 users, and subsequently show that the proposed scheme significantly reduces the error probability in a multiuser communication system having MPR capability.

In multihop wireless networks, packets of a flow originating from a source node are relayed by intermediate nodes (relay nodes) and travel towards their destination along a multihop wireless path. Since the traffic forwarding capability of each node varies according to its level of contention, ideally, a node should not transmit more packets to its relay node than the corresponding relay node can forward. Instead, each node should yield its channel access opportunity to its neighbor nodes so that all the nodes can evenly share the channel and have similar forwarding capabilities. In this manner, nodes can utilize the wireless channel effectively, and further increase the end-to-end throughput of a multihop path. We propose a fully distributed contention window adaptation (CWA) mechanism, which adjusts the channel access probability depending on the difference between the incoming and outgoing traffic at each node, in order to equate the traffic forwarding capabilities among all the nodes in the path. We implement the proposed adaptive contention algorithm on Madwifi Linux kernel driver for Wi-Fi interface with Atheros chipset and carry out an empirical study in our division building. The experiment results demonstrate how the proposed mechanism can improve end-to-end throughput performance in the multihop wireless networks.

This paper addresses the power control problem in wireless multiple access communication systems with a multi-packet reception (MPR) channel. Driven by advances in signal processing and multiuser detection (MUD) technologies such as UWB and MIMO, it has become possible for a wireless node to simultaneously receive several packets from other transmitters. In this case, the receiver node can determine the set of transmitters from which it wants to receive data and instruct them to adjust their transmit power levels in order to maximize the overall network capacity by exploiting the MPR capability. We first show that the efficiency of the MPR channel utilization can be maximized by equalizing the transmission durations of simultaneously transmitting nodes, and then propose a power control that increases/decreases the transmit power of the nodes to equalize the transmission durations. Under the proposed power control, a node with a long transmission duration is allowed to use a higher transmit power if the next higher transmission rate is achievable with the increased transmit power. Through extensive simulations, we show that the proposed power control significantly improves the overall network capacity in multiple access.

Mobile ad-hoc network (MANET) is an appealing technology that has attracted lots of research efforts. Due to frequent communication failures, multipath routing is preferred than single-path routing in many applications such as multimedia streaming over MANET as the former can provide robustness, load balancing and reduce end-to-end delay. However, a security remains important factor that hinders the rapid deployment of multimedia applications over wireless ad hoc networks. In this paper, we propose a secure and reliable framework for multimedia streaming over multipath MANET that provides security not only for ad hoc routing but also for real-time data transfer. We discuss security analysis for the proposed security scheme. And we also conduct simulation to evaluate such a framework with respect to different performance metrics.

In this article, we address the issue of localization in anisotropic sensor networks. Anisotropic networks differ from isotropic networks in that they possess properties that vary according to the direction of measurement. Anisotropic characteristics result from various factors such as the geographic shape of the region (nonconvex region), different node densities, irregular radio patterns, and anisotropic terrain conditions. In order to characterize anisotropic features, we devise a linear mapping method that projects one embedding space built upon proximity measures into geographic distance space by using the truncated singular value decomposition (SVD) pseudo-inverse technique. This transformation retains as much topological information as possible and reduces the effect of measurement noise on the estimates of geographic distances. We show via simulation that the proposed localization method outperforms DV-hop, DV-distance, and MDS-MAP, and makes robust and accurate estimates of sensor locations in both isotropic and anisotropic sensor networks.

In carrier sense multiple access with collision avoidance (CSMA/CA) wireless networks, adaptive physical carrier sense (CS) mechanisms have been proposed to maximize spatial reuse and to mitigate packet collisions in various wireless networking environments. Most of them attempt to reduce the carrier sensing range for higher throughput performance as long as the signal-to-interference-plus-noise-ratio (SINR) requirement is satisfied at the receiver. In this paper, we implement one of adaptive CS mechanisms in IEEE 802.15.4 wireless networks and show that the adaptation leads to significant throughput unfairness among wireless nodes. We propose an adaptive CS mechanism for fairness and empirically show that the proposed mechanism improves both the throughputand fairness performance in CSMA/CA wireless networks.

We consider the problem of improving network capacity in wireless mesh networks from the angle of multi-flow diversity. As consecutive packets on a multihop path can interfere with one another within their interference range, the number of concurrent transmissions is limited, thereby degrading throughput performance. This intra-flow interference can be alleviated by enhancing multi-flow diversity in wireless mesh networks. Hence, we propose the use of a fair queueing policy to evenly distribute the traffic load to achieve fair bandwidth allocation among the flows. Through ns-2 simulations we subsequently show that CHOKe can effectively reduce intra-flow interference and improve throughput performance.

In multihop wireless networks, packets from a source node are relayed by intermediate nodes (relay nodes) toward their destination node along a multihop wireless path. However, if a relay node simultaneously receives a large number of packets and fails to forward them at the same rate as they are received, the end-to-end throughput performance of the network degrades due to packet losses at the bottleneck relay node. In this paper, we propose an adaptive contention control algorithm that adjusts the channel access probability depending on the level of congestion at each relay node in order to improve the end-to-end throughput performance in multihop wireless networks. In this algorithm, a bottleneck relay node is granted permission to increase its channel access probability by reducing its contention window size, thus enabling it to forward packets more aggressively. Through a series of simulations, we show that the end-to-end throughput is improved by about 60% in a chain topology.

In vehicular ad-hoc networks (VANETs), mobility is one of key characteristics that have a significant effect on routing performance because it causes dynamic changes of network topology. In this paper, we propose a contention based forwarding protocol that exploits geographic or virtual location information. A node that is closer to the destination node has a higher priority in competing for the wireless channel, and forwards packets as a relay node earlier than other adjacent nodes. When a packet is forwarded by a relay node, its preceding node informs adjacent nodes that it has been already relayed to prevent duplication of packet delivery. Through ns-2 simulations, we show that the proposed scheme can significantly improve the network performance in terms of the packet delivery ratio and the end-to-end delay in various VANET scenarios.

In multihop wireless networks, packets of a traffic flow originating from a source node are relayed by intermediate nodes (relay nodes) and travel toward their destination node along a multihop wireless path. If a source node transmits excessive packets at a high rate, its relay node may not have enough channel access opportunities to forward them, resulting in a significant throughput decrease on an end-to-end perspective of multihop path. In this paper, we propose a probabilistic packet dropping scheme that regulates the traffic load offered by source nodes and reduces their interference with neighboring relay nodes on the multihop paths. Through ns-2 simulations, we show that the proposed scheme can significantly improve the end-to-end throughput performance without exploiting topological information under chain and cross topologies.

In wireless mesh networks, the end-to-end throughput of a flow over a multiple-hop wireless link rapidly decreases as the hop-count between the source and destination nodes increases, and the flows that travel over a path of more than 4-5 hops from its source node eventually starve. To alleviate this unfairness, we propose a weighted random early detection (RED) mechanism that has a different dropping preference according to the hop-count information. When the network is congested, it forwards packets that come from a farther source node with a higher priority. Through extensive simulations, we show that the proposed queue management policy can improve the fairness performance effectively and alleviate the starvation problem in multihop wireless networks.

The importance of spatial reuse in wireless ad hoc networks has been long recognized as a key to improving the network capacity. In this paper, we show that 1) in the case that the achievable channel rate follows the Shannon capacity, spatial reuse depends only on the ratio of the transmit power to the carrier sense threshold and 2) in the case that only a set of discrete data rates are available, as a control knob for sustaining achievable data rates, tuning the transmit power provides more sophisticated rate control over tuning the carrier sense threshold, provided that there is a sufficient number of power levels available. Based on the findings, we then propose a decentralized power and rate control algorithm to enable each node to adjust, based on its signal interference level, its transmit power and data rate. The transmit power is so determined that the transmitter can sustain a high data rate while keeping the adverse interference effect on the other neighboring concurrent transmissions minimal. Simulation results have shown that compared to existing carrier sense threshold tuning algorithms, the proposed power and rate control algorithm yields higher network capacity.

Abstract — With the technical advances in ubiquitous computing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoretical base and develop a localization algorithm for building a zero-configuration and robust indoor localization system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters). I.

The importance of spatial reuse in wireless ad-hoc networks has been long recognized as a key to improving the network capacity. One can increase the level of spatial reuse by either reducing the transmit power or increasing the carrier sense threshold (thereby reducing the carrier sense range). On the other hand, as the transmit power decreases or the carrier sense threshold increases, the SINR decreases as a result of the smaller received signal or the increased interference level. Consequently, the data rate sustained by each transmission may decrease. This leads naturally to the following questions:(1)How can the trade-off between the increased level of spatial reuse and the decreased data rate each node can sustain be quantified? In other words,is there an optimal range of transmit power/carrier sense threshold in which the network capacity is maximized? (2)What is the relation between the transmit power and the carrier sense threshold.In this paper, we study both problems, and show that (i)in the case that the achievable channel rate follows the Shannon capacity, spatial reuse depends only on the ratio of the transmit power to the carrier sense threshold; and (ii) in the case that only a set of discrete data rates are available, tuning the transmit power offers several advantages that tuning the carrier sense threshold cannot, provided that there is a sufficient number of power levels available. Based on the findings, we then propose a decentralized power and rate control algorithm to enable each node to adjust, based on its signal interference level, its transmit power and data rate. The transmit power is so determined that the transmitter can sustain a high data rate, while keeping the adverse interference effect on the other neighboring concurrent transmissions minimal. Simulation results have shown that, as compared to existing carrier sense threshold tuning algorithms, the proposed power and rate control algorithm yields higher network capacity.

In this paper, we consider the problem of mitigating interference and improving network capacity in wireless mesh networks from the angle of temporal-spatial diversity. In a nutshell, while the achievable throughput on a multihop wireless path is limited by intra-flow interference, the overall capacity of a multihop wireless network can be increased by exploiting temporal-spatial diversity of concurrent transmissions that exist among a number of wireless links. Connections that are routed along multihop wireless paths can be scheduled to take place simultaneously if their transmissions do not interfere with each other (significantly).To make a case of exploiting the temporal-spatial diversity to improve network capacity, we focus on transporting downstream traffic at gateway nodes with Internet access. We propose to construct, based on measurements of received signal strengths, a virtual coordinate system that is used to determine the sets of paths along which transmissions can take place with the least inter-flow interference. Based on the sets of non-interfering paths, the gateway node then determines the order with which a gateway node schedules frames of different connections to be transmitted. Through extensive simulation (with real-life measurement traces on an operational, city-wide wireless community network), we show that the downstream throughput of a gateway node in a wireless mesh network can be improved by 10-35% under a variety of network topologies and traffic distributions. This, coupled with the fact that the proposed approach requires only minor code change in the gateway nodes and does not require any additional hardware, makes it a viable option to improving network capacity in existing wireless mesh networks.

In this paper, we consider the issue of localization in anisotropic sensor networks. Anisotropic networks are differentiated from isotropic networks in that they possess properties that vary according to the direction of measurement. Anisotropic characteristics result from various factors such as the geographic shape of the region (non-convex region), the different node densities, the irregular radio patterns, and the anisotropic terrain conditions. In order to characterize anisotropic features, we devise a linear mapping method that transforms proximity measurements between sensor nodes into a geographic distance embedding space by using the truncated singular value decomposition-based (TSVD-based) pseudo-inverse technique. This transformation retains as much topological information as possible and reduces the effect of measurement noises on the estimates of geographic distances. We show via simulation that the proposed localization method outperforms DV-hop, DV-distance (D. Niculescu, 2001), and MDS-map (Y. Shang et al., 2003), and makes robust and accurate estimates of sensor locations in both isotropic and anisotropic sensor networks.

In this paper, we consider the problem of how to represent the locations of Internet hosts in a Cartesian coordinate system to facilitate estimation of network distances among arbitrary Internet hosts. We envision an infrastructure that consists of beacon nodes and provides the service of estimating network distance between pairs of hosts without direct delay measurement. We show that the principal component analysis (PCA) technique can effectively extract topological information from delay measurements between beacon hosts. Based on PCA, we devise a transformation method that projects the raw distance space into a new coordinate system of (much) smaller dimensions. The transformation retains as much topological information as possible and yet enables end hosts to determine their coordinates in the coordinate system. The resulting new coordinate system is termed as the Internet Coordinate System (ICS). As compared to existing work (e.g., IDMaps and GNP), ICS incurs smaller computation overhead in calculating the coordinates of hosts and smaller measurement overhead (required for end hosts to measure their distances to beacon hosts). Finally, we show via experiments with both real-life and synthetic data sets that ICS makes robust and accurate estimates of network distances, incurs little computational overhead, and its performance is not susceptible to the number of beacon nodes (as long as it exceeds a certain threshold) and the network topology.

In this paper, we consider the problem of how to represent the locations of Internet hosts in a Cartesian coordinate system to facilitate estimate of the network distance between two arbitrary Internet hosts. We envision an infrastructure that consists of beacon nodes and provides the service of estimating network distance between two hosts without direct delay measurement. We show that the principal component analysis (PCA) technique can effectively extract topological information from delay measurements between beacon hosts. Based on PCA, we devise a transformation method that projects the distance data space into a new coordinate system of (much) smaller dimensions. The transformation retains as much topological information as possible and yet enables end hosts to easily determine their locations in the coordinate system. The resulting new coordinate system is termed as the Internet Coordinate System (ICS). As compared to existing work (e.g., IDMaps [1] and GNP [2]), ICS incurs smaller computation overhead in calculating the coordinates of hosts and smaller measurement overhead (required for end hosts to measure their distances to beacon hosts). Finally, we show via experimentation with real-life data sets that ICS is robust and accurate, regardless of the number of beacon nodes (as long as it exceeds certain threshold) and the complexity of network topology.

The virtual rate control (VRC) algorithm has been proposed for active queue management (AQM) in TCP networks. This algorithm uses an adaptive rate control instead of queue length control in order to respond quickly to traffic change with high utilization and small loss. By introducing the notion of virtual target rate, the VRC algorithm can maintain an input rate around the target rate, while attempting to regulate the queue length. In this paper, we analyze the stability of the VRC algorithm in a linearized model. From the results of our analysis, we provide a design guideline for the system to remain stable. We show the validity of our analysis and the effectiveness of the VRC algorithm compared to RED, PI, REM and AVQ algorithms through ns-2 simulations.